A Comparison of Automated Methods for Determining Averaged P-Wave Durations in an

Optimized Magnetocardiographic Acquisition System

by Timothy Bardouille

Submitted in partial fùlfillrnent

of the requirements for the degree of

Master of Science

at

Dalhousie University

Halifax, Nova Scotia, Canada

August 1999

@Copyright by Timothy Bardouille, 1999

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To rny family.

3. Inaccuracies In Imaging Cardiac Sources Using Electrical Or Magnetic

Sensors 53

3.1. Introduction 53

3.2. Background : Wolff-Parkinson- White Syndrome 56

3.3. Method 57

3.3.1. The Forward Solution 58

3.3.2. SimulatingSensor Misplacement 62

3 -3.3. The Inverse Solution 65

3.4. Results 65

3.4.1. Body Surface Poten tial Maps: Vertical Electrode Misplacement

65

3.4.2. Body Surface Poten tial Maps: Azirnuthal and MidAxiIfary

E fectrode Misplacement 68

3.4.3. Body Suface Potential Maps: Randorn Efectrode Misplacement

68

3.4.4. Magnetic Field Maps: Lateral Grid Misplacement 71

3 -4.5. Magnetic Field Maps: Nonnal Grid Misplacement 74

3 S . Conclusions 76

4. Determining the P-Wave Duration

4.1. Introduction

4.2. Atrial Fibrillation

4.2.1. Overview

4.2.2. Basic Physioiogkal Synopsis

4.2.3. Diagnosing and Predicting Atrial Fibrillation

4.3. Method

4.3.1. Data Acquisition and Averaging

4.3.2. Finding the Optimal P- Wave

4.3.3. Determining the P- Wave Duration

4.4. Results

4.5. Conclusion

Appendices

A MCG Acquisition Protocol

B Summary of Data Analysis Algoritbms

References

vii

List of Figures

Power Spectrum for 3rd Gradiometer on Amval

Attenuation Spectnim Generated by CTF Notch Filter

Maximum Attenuation Frequency Deviation for CTF Notch Filter

Attenuation at 60 Hz due to CTF Notch Filter

Cornparison of New and Old CTF Notch Filter Charactenstics

Mode1 ECG Heart Signal

ECG Ringing

MCG Ringing

Adaptive vs. Notch Filtering

Noise Peak Frequency Drift

Systern Ground Connections

S imulated Johnson Noise Spectrum

Simulated 1 Hz Johnson Noise

Power Spectnim for 3" Gradiometer after Modifications

Magnetic Field Map at R-wave Maximum

Determining the Signal-to-Noise Ratio

Power Spectrum for 3" Gradiometer in New Dewar

31d Order Asymmetric Gradiometer

MCG Acquisition Apparatus

MCG Acquisition F o m

MCG Acquisition Sites

Flowchart of the Data Averaging Process

Cornparison of Low-Pass Filters

Averaged MCG Complexes at 56 Sites

Contour Ptots at and after the R-Wave Maximum

viii

The Atrial and Ventricular Myocardium 54

The Cardiac Conduction System 55

WPW Preexcitation Sites 61

Electrode Locations on the Torso 64

Dipole Displacement due to Vertical Electrode Shifi 67

Vertical Dipole Displacement due to Vertical Electrode Shi fi 67

Dipole Displacement due to Azimuthal Electrode Shift 69

Dipoie Displacement due to Midaxillary Electrode Shift 69

Dipole Displacement due to Random Electrode Shift 70

Dipole Displacement due to Vertical Magnetometer Grid Shift 72

Dipole Displacement due to Horizontal Magnetometer Grid Shift 72

Vertical Dipole Displacement due to Vertical Magnetometer Grid Shift 73

Horizontal Dipole Displacement due to Horizontal Magnetometer Grid Shifi

73

Dipole Displacement due to Normal Magnetometer Grid Shift 75

Normal Dipole Displacement due to Normal Magnetometer Gnd Shift 75

4.1 Formation of Atrial Fibrillation

4.2 Magnetic Field Map at P-wave Maximum

3.3 P-Wave Times Determined by the Eyeball Method

4.4 P-Wave Times Detennined by the Soria-Olivas Method

List of Tables

Typical Settings for the CTF Notch Filter 12

Si gnal-to-Noise Results for Averaged MCG Data 3 1

MCG Specifications for the Old and New Acquisition Systems 32

Header Information for Averaged Database Files 47

Anatomical Positions of Preexcitation Sites 62

MCG P-Wave Times Determined by Three Different Methods 90

Differences in Averaged MCG P-Wave Times 90

ECG P-Wave Times Determined by Three Different Methods 91

Differences in Averaged ECG P-Wave Times 91

P-Wave Signal-to-Noise Characteristics of Averaged MCG and ECG Data

92

Summary of the important Results fiom Chapter 4 94

Abstract

The Dalhousie Biomagnetism group recently obtained a 3" order asymmetric

gradiometer connected to a DC SQUID. The optimization of the performance and

determination of the noise characteristics of this new system is accomplished.

Improvements are made to the notch filter algorithm to inçrease signal attenuation at

power line frequencies. An adaptive notch filter is also implemented to eliminate notch

filter ringing. The noise generated by the themal motion of electrons (Johnson noise) in

the Aluminium walls of the shielded room is calculated. The calculated Johnson noise in

the centre of the shielded room as measured by a 3rd order gradiometer is found to be less

than 0.1 p d ~ z for fiequencies greater than 1 Hz. We find that the intnnsic noise for the

new system is - 15fl/dHz over the recording bandwidth. The signal-to-noise ratio for

averaged magnetocardiographic (MCG) data acquired using this optimized system is

found to be - 1000. The protocol for acquiring, averaging, and plotting MCG data with

this new acquisition system is described.

We simulate body surface potential maps (BSPMs) and magnetic field maps

(MFMs) for single current dipoles in a homogeneous mode1 of the human torso. The

effects of misplacement of magnetic sensors and electrodes on the localization of these

cument dipoles are detennined. It is found that magnetic sensor and electrode placement

accuracy have similar effects on the accuracy of localizing a current dipole in a

homogeneous torso. In order to localize a cardiac source to within 5 mm, we find that

similar accuracy in sensor placement is required.

The onset, offset, and duration of the P-wave in averaged MCG and

electrocardiographic (ECG) data are determined using bvo automated methods - the

Soria-Olivas method and DALECG. The reliability of these methods are compared with

manually detennined P-wave times. We find that the Soria-Olivas method is more

reliable than DALECG for determining the P-wave times for averaged MCG data

acquired with the current rneasurement system. DALECG is more reliable for finding the

P-wave times for averaged ECG data. The disparity is attributed to the difference in the

P-wave signal-to-noise ratios for MCG and ECG data.

Symbols and Abbreviations

ADC

AF

AP

ASCII

AV

B

BSP

BSPM

&'*'

B,O d

DALECG

DC

ECG

EPS

fc FFT

f;

f m x

fS fu Ho3

HnO3 k

z LAL

LAP

LPL

analog-to-digital converter

atrial fibrillation

accessory pathway

text format

Atrioventricular

magnetic field vector

body surface potential

body surface potential map

magnetic field measured by a 3d order gradiometer due to Johnson noise

magnetic field measured by a magnetometer due to Johnson noise

adaptive filter error estimate

Daihousie averaging program

direct current

electrocardiogram

ECG processing system format

centre frequency for notch filter

fast fourier transfonn

lower fiequency for notch filter

maximum attenuation ftequency

sarnpiing fiequency

upper frequency for notch filter

response fhction

numerator of response fûnction

Boltzmann constant

gradiometer baseline length

left anterolateral

left anterior paraseptal

left posterolateral

xii

LPP

MCG

nb

P

PR interval

P-wave

QRS

QT interval

RAP

RE

RF

RPL

RPP

R-wave

SA

S-O

S Q m STD

SWPCHG

T

t

TV

T-wave

UNUC

UP interval

U-wave

VAX/VMS

WPW

g- Af

lefi posterior paraseptal

magnetocardiogram

intnnsic noise

instrumentation noise

curent dipole vector

interval fiom atrial to ventncular depolarization

atrial depolarization wave

ventricular depolarization

interval fiom ventricular depolarization ta repolarization

right antenor paraseptal

reproducibility error

radio fiequency m] right posterolateral

right posterior paraseptal

ventricular depolarization wave

sinoatrial

Soria-Olivas

superconducting quantum interference device

standard deviation

database manipulation algorithm

temperature

thickness of metallic plate

total variance

ventricular repolarization wave

computer operating system

interval fkom end of repolarization to atrial depolarkation

deflection on the end of the T-wave

computer operating system

Wol ff-Parkinson- Whi te syndrome

3dB width for notch filter

recording bandwidth

. . . X l l l

potential

potential vector

unit of measure of magnetic flux

conductivity

magnetic permeability

deflation matrix

deflated potentiai

deflated potential vector

xiv

Acknowledgements

Most importantly, 1 would like to recognize the support and encouragement that

has been received from rny supervisor, Gerhard Stroink. A special thanks is extended to

him for his time and patience. Also, 1 extend my appreciation to al1 those who worked in

the Biomagnetism lab over the past two years. Particularly, 1 would like to mention

Andy Adams for getting me started. Thanks, also, to Stephen Ritcey for explaining those

algorithms that nobody else could and for his involvement in the sensor misplacement

study.

In addition, 1 would like to express thanks for the ongoing support s h o w by the

entire Dalhousie Physics department. From a srnihg face in the office to a helping hand

in the basement to thought experiments over Earl Grey tea, al1 of your help has been very

much appreciated.

The Dahousie Biomagnetism group would also like to thank the Natwal Sciences

and Engineering Research Council (NSERC) for financial support.

Chapter 1

System Optimization

1.1 INTRODUCTION

In October 1997, the Dahousie Biomagnetism Group received a new SQUTD

measuring system fiom CTF Systems Inc. in Port Coquitlam, B.C. The system was

installed in the Aluminium shielded room in the Dunn building at Dalhousie University

campus. This new system boasts a lower intrinsic noise and a higher sensitivity in

cornparison to the previous system. We wish to optimize the performance of this new

system at its present location to ensure the largest signal-to-noise ratio possible when

acquiring magnetic data.

System optimization will involve the following:

Analysis of the noise spectnim of the new system upon arrival.

Evaiuation of the power line noise filter supplied by CTF Systems Inc.

Implementation of an adaptive power line noise filter to eliminate filter

ringing.

Removal of environmental noise sources.

Calculation of noise due to the thermal motion of electrons in the

Aluminium shielded room walls.

1.1.1 Rationale for Present System

In 1983, CTF Systems Inc. built and delivered to Dalhousie University a one

channel RF SQUID connected to an asymmetric 2nd order gradiometer with a 4.0 cm

baseline housed in a cryogenic dewar. This system was used for al1 biomagnetic research

at Dalhousie. The intrinsic noise for this system was 3 0 / r / h . Assurning this noise is

constant over the whole bandwidth, the magnitude of the noise in an acquired signal over

a given recording bandwidth, LY, is:

N; = n , *J;Y (1-1)

where nb is the intrinsic noise. Thus, for measurements acquired with this system over a

bandwidth of 125 Hz, the expected noise is -340jT.

Dawson [l], in 1951, showed that when N similar signals with random noise are

aligned temporally with respect to a reference point and averaged, the noise will be

reduced by l/dlV, and the signal will be unaffected. Thus, by averaging cardiac data over

N complexes, the instrumentation noise is lowered to:

In Our case, we acquire 30 seconds of MCG data at each site. Since a normal subject's

resting heart rate is about 60 beats per minute, we acquire about 30 similar cardiac

complexes. Thus, for averaged MCG data acquired using Our oid system, assuming there

are no environmental noise sources, the expected noise is -62JT.

CTF Systems Inc. believed that installing a DC SQUID connected to a 3d order

gradiometer in our dewar would reduce the i n t ~ s i c noise to -1 0 f l / d ~ z . This would be

a three-fold improvement from the original system. Furthemore, Adams 121 suggested

that increasing the baseline nom 4.0 cm to 6.0 cm in a 3" order gradiometer would

increase the peak signal strength. He expected the signal-to-noise ratio to increase six-

fold for measurements made with the new system over a 250 Hz bandwidth as compared

to the znd order gradiometer system over a 125 Hz bandwidth, assuming no other noise

sources except for the intrinsic noise. In August 1996, it was decided that this new

SQUID system should be acquired.

l .l.2 System Criteria

The new system was built and delivered by CTF Systems Inc. in October 1997.

This new rneasurernent system consists of a DC SQUID connected to an arynxneûic 3d

order gradiometer with a 6.0 cm baseline housed in the same dewar as the previous

system. The DC SQUID has a sensitivity of 1x10'~ ~ b / ' l ~ z . Also housed inside the

dewar are three magnetometers. These "balancing coils" measure the x, y, and z

components of environmental noise to be subtracted from measured data.

The channel processing system is a DSQ-80014. This unit can simultaneously

acquire 15 channels of infornation with an accwacy of 1024 counWa. The dynarnic

range of measurement for al1 magnetic cbannels is fi12 a. Also included in this box is

an analog-to-digital converter (ADC) for simultaneously acquiring up to 3 more channels

of data (Le. ECG limb leads). These channels have a dynarnic range of k3.6 kV over 20

bits. For up to and including 8 channels of simultaneous data acquisition, the maximum

sample rate per charnel available to the DSQ-800/4 unit is 12.5 kHz. The unit is

interfaced via SCSI port with a Maclntosh Quadra 650 computer that m s data

acquisition software developed by CTF Systems Inc [3j.

CTF Systems Inc. suggests that the intrinsic noise of the 3d order gradiometer

inside the present dewar is 10jT/dHz. Over a 250 Hz bandwidth, this equates to l6Ofï

of noise. For averaged data, the noise is reduced to 29 fT. Lamothe [4] found that the

average peak cardiac signal for the old systern is -16 pT, and the actual noise afier

averaging MCG data is 54 fl. Thus, the old signal-to-noise for averaged data with a

recording bandwidth of 125 Hz is -300. We intend to improve on this value.

1.2 SYSTEM MODIFICATIONS

1.2.1 Noise Spectrum on Arriva1

The DC SQUID systern was installed upon amival. fhe dewar containing the 3d

order gradiometer and balancing coils was filled with liquid Helium and placed in the

shielded room. The data acquisition electronics, including the Macintosh computer, were

arranged in an adjoining room. This room is used for subject preparation, but is also

convenient for data acquisition due to its proximity to the shielded room. Data transfer

occurs through a 50-pin cable which connects to the dewar inside the shielded room,

10 Frequency [Hz]

Fig. 1.1. A plot of the power spectrurn for 10 seconds of background data collected with our new system. Though the average noise is about 15 ff/dHz, there are numerous noise peaks greater than 100 ffld~z over the entire fiequency range.

snakes out through a feedthrough hole in the Aluminium plate ceiling, and terminates at

the DSQ-800/4 in the adjoining room.

Figure 1.1 shows the noise spectrum for 10 seconds of data acquired at a sample

rate of 1250 Hz with no subject in the shielded room (i.e. a background

intrinsic noise is -15 f~ldHz. This is higher than the expected intrinsic

fI7.l~~. Furthemore, there are numerous fiequencies for which the noise is

100 /T/~HZ. Some of these noise peaks are stable (i.e. always occurring

scan). The

noise of 10

greater than

at the sarne

fiequency). However, noise peak fiequency drift occurs in many cases. In order to

collect usefùl cardiac data, it is imperative that the noise at al1 fkequencies be no greater

than the intrinsic noise. Thus, we must eliminate al1 noise peaks fiom this plot.

1.2.2 Power Line Noise Reduction

The rnost prominent peak observed in Figure 1.1 occurs at 60 Hz, with harmonies

at 120 and 180 Hz. The source of this noise is the magnetic fields generated by 60 Hz

power lines. It is referred to as power Iine noise. These fields are not entirely attenuated

by the shielded room. Thus, it is necessary to filter our data at these fiequencies.

The CTF Systems Inc. data acquisition software inchdes 4 identical Butterworth

notch filters with variable centre fiequencies and 3dB widths. The following two

sections will discuss the effectiveness of this filter algorithm. Specifically, we will

analyze the reduction in 60 Hz signal attenuation due to the instability of the centre

tiequency of the filter. Increased attenuation will be achieved by implementing a more

stable tram fer function. We wiIl also observe 60 Hz ringing occumng after strong spikes

in the notch filtered time data. hplernenting an adaptive notch filter in place of the non-

adaptive Butterworth tilter will eliminate the nnging.

1.2.2.1 Notch Filter Centre Frequency Stability

To ensure that the notch filter supplied by CTF Systems Inc. suficiently reduces

power line noise, an analysis of the CTF filter was completed. The filter is a 4 I h order

Butterworth notch filter. It is included with the data acquisition software received from

CTF. The transfer fùnction for this filter is [SI:

where:

- denom

The parameter,&, represents the sample frequency. The upper and lower fiequencies Vu

a n d n are:

wheref, and df are the centre frequency and the 3dB width, respectively.

Figure 1.2 shows that for this notch filter at 60 Hz with a 1 Hz 3dB width. the

response hnction has a maximum attenuation of -101 dB at 59.998 Hz. The attenuation

at 60 Hz is -55 dB. For the same filter with a 4 Hz 3dB width, the maximum attenuation

is -68 dB and occurs at 59.97 Hz - even fkther corn the expected centre frequency. The

attenuation at 60 Hz is -41 dB. The fiequency at which maximum aîtenuation occurs

e O . L.

C -5 60. e a 3 3 E -80. 0

5: P) CI

-9 0..

-1 00.. l -1 0 0 -

59.996 59.998 60 LOJ02 60.904 59.9 59.92 51.94 59.99 59.98 60 60.02 60.04 60.09 90.08 60.1

Frequency m] Frequency [Hz]

Fig. 1.2. Plots of the attenuation, in decibels, due to the response function, Ho, with a centre fiequency of 60 Hz (a) for a 1 Hz 3dB width, and (b) for both 1 Hz (solid) and 4 Hz (dashed) 3dB widths.

does not correspond to the centre fiequency. Thus, power line signals are not attenuated

as strongly as expected.

The maximum attenuation frequency of the response function can be detennined

fiom the numerator of the response fùnction. The modulus of the numerator is a real,

smooth fùnction, H#), which contains/, and df: The stationary point of this hinction is

the maximum attenuation frequency of the transfer function. We differentiated H.0, set

it equal to zero, and solved for frequency. For the response function in equation 1.3, f,,

is:

Figure 1.3 shows the divergence of the maximum attenuation fiequency fiom the

centre frequency as the 3dB width (@) is increased. The notch filter centre fiequency is

Fig. 1.3. A plot of the maximum attenuation fiequency vs. the 3dB width for a response fimction with a centre frequency of 60 Hz. The maximum attenuation frequency, fmm, deviates fkom 60 Hz as the 3dB width increases.

60 Hz and the sample rate is 1250 Hz. As the 3dB width gets larger, the difference

between /, and f,, increases. This is a problem since the attenuation will not be

maximized at 60 Hz. Instead, the maximum attenuation will occur at some frequency

below 60 Hz.

To detennine the effect of the divergence off,, from the centre frequency on

attenuation, we looked at the attenuation at 60 Hz for various 3dB widths. This was

accomplished by determining the value of the response function at 60 Hz while keeping

the centre frequency constant and varying the 3dB width. Figure 1.4 shows the

attenuation at 60 Hz, in decibels, due to a 60 Hz notch filter with 3dB widths up to 10 Hz.

Since the maximum attenuation frequency drifts away fiom 60 Hz, the attenuation at 60

Fig. 1.4. The attenuation (in dB) at 60 Hz due to the response function with a centre frequency of 60 Hz and 3dB widths between O Hz and 10 Hz. As the 3dB width increases, the attenuation at 60 Hz decreases.

Hz decreases with increasing 3dB width. Thus, the notch filter becomes less effective at

the centre fiequency as the 3dB width is increased.

After notifying CTF Systems Inc. of these problems, it becarne apparent that /I

and5 needed to be modified. The new equations for upper and lower fiequency are 161:

Figure 1.5 shows a cornpanson of the maximum attenuation frequency and the

attenuation at 60 Hz for the new and old notch filter parameters with a 60 Hz centre

frequency and a 1250 Hz sampling rate. The maximum attenuation frequency is more

Fig. 1 S. A companson of (a) maximum attenuation fkequency and (b) attenuation (in dB) at 60 Hz vs. the 3dB width for the CTF notch filter with old (dashed) and new (solid) values forfr andf,. The horizontal line in (a) represents the ideal behaviour for f-.

constant in the case of the new filter. At a 3dB width of 10 Hz, f,, for the old filter

deviates by 0.2 Hz fiom the centre fiequency. The new filter is only 6 mHz fiom the

centre fiequency at the same 3dB width. This improvement translates into increases in

maximum attenuation at the centre fiequency. At a 3dB width of 2 Hz, the old notch

filter gives -50 dB attenuation, while the new filter gives -85 dB attenuation.

By redefiningfy andfi, we are able to improve the performance of the CTF notch

filter. Since the maximum attenuation fiequency is more constant for the new filter, the

attenuation at the centre fkequency is greater. For a 60 Hz notch filter with a 4 Hz 3dB

width, the attenuation at 60 Hz increases fiom 41 dB to 75 dB with the change in notch

filter parameters. Table 1.1 lists the typical settings we will use for this new filter.

1 Filter # 1 Centre Frequency [Hz] 1 3dB Width (Hz1 1

Table 1.1. Typical settings for the CTF notch filters.

1.2.2 Notch Filter Ringing - The Adaptive Filter

Figure 1.6 shows a mode1 of an ECG heart signal. The P wave is generated by

atnal depolarisation. Ventricular depolarisation is associated with the QRS cornplex, also

referred to as the R wave. The T wave represents ventricular repolarisation. No cardiac

activity has been c o ~ e c t e d to the small U wave. Intervals and segments are defined as

shown in the diagram. A segment is defined as the region within an interval that does not

include a wave. The same notation is used for MCG signals.

The CTF notch filter defined by equation 1.3, with the adjustments outlined in

equations 1.14 and 1.15 is effective at attenuating 60 Hz noise for smooth signals.

However, Hamilton [73 suggests that this type of non-adaptive filter produces significant

60 Hz nnging following sharp peaks such as the R wave. Adaptive filters are said to

introduce less distortion in typical cardiac signals. Thus, we tested the CTF notch filter

for the production of 60 Hz ringing.

In order to test for ringing, we acquired cardiac data using the notch filter, and

analysed the resultant signal for 60 Hz content. MCG and ECG data were acquired at the

Fig. 1.6. A diagram of an ECG heart signal with its relevant features Iabeled [8].

Dalhousie Biomagnetism Lab on a normal subject. The gradiometer was positioned to

measure the strongest possible QRS complex. Thirty seconds of data were acquired at a

sample rate of 1250 Hz at this position. Dwing the data acquisition, one notch filter was

used with a 60 Hz centre fiequency and a 4 Hz 3dB width. The data were processed and

averaged according to the protocol in section 2.3 of this thesis, without the

inîplementation of the adaptive filter. The averaged MCG and ECG complexes were

analysed for 60 Hz noise content. Particularly, we compared the magnitude of 60 Hz

noise in the intervals imrnediately before the P-wave and after the R-wave.

Figures 1.7 and 1.8 respectively show averaged MCG and ECG complexes

measured on December 7th, 1998. In Figures 1.7(a) and 1.8(a), there is distortion afier

the R-wave. To further classi@ this distortion, Fast Fourier Transfoms (FFTs) are

calculated for the 160 ms before the onset of the P-wave and after the offset of the R-

wave (see Figures 1.7 and 1.8 (b and c)). There is no cardiac signal in the interval before

the P-wave. Thus, any noise will show up the clearest in this region. Both FFTs of the

region after the R-wave show a peak at 60 Hz. There is no cormponding 60 Hz peak in

the region before the P-wave. Thus, 60 Hz nnging is occumng after the QRS peak in

these averaged complexes.

To eliminate 60 Hz noise entirely, a new filter algorithm is used. Ahlstrom and

Tompkins [9] use an adaptive filter that relies on estimates of 60 Hz noise from two

previous data points to calculate the expected noise at the current point. Consider a data

set, x, which is compted by a signal at fiequency,f: For data sampled at a penod, T, the

noise at the fiequency, o = 27$ is:

e(n T) = A sin(m T ) (1.16)

Given that the error at the current and previous points are e(n7') and e(nT-T) respectively,

then the error at the next point is:

e ( n ~ + T ) = ~ s i n ( w r ~ +UT) (1.17)

This equation can be simpIified using trigonometry to be:

e ( n ~ + T ) = 2IVe(n~)- e ( n ~ - T )

where:

I . . i l i i . < . . . f i . .

O 200 400 600 800 1 k Time [ms]

1 O-2 t I 10 100 1 O00

Frequency [Hz]

Frequency m] Fig. 1.7. ECG Ringing: (a) An averaged notch filtered ECG complex, and the corresponding power spectra for 160 ms (b) before P-On, and (c) after R-Off. Note the 60 Hz peak apparent in the interval afier R- Off. Recorded December 07, 1998.

I I I 10 1 O0 1 O00

Frequency [Hz]

Fig. 1.8. MCG Ringing: (a) An averaged notch filtered MCG cornplex, and the corresponding power spectra for 160 ms (b) before P- On, and (c) d e r R-Off. Note the 60 Hz ringing apparent in the interval afier R-Off. Recorded December 07, 1998.

The variable,f,, represents the sample fiequency.

In order to detexmine the accuracy of the error estimate for the next point, any DC

offset is subtracted out by calculating:

g ( n ~ +T) = (x(nT + 7 ) - e ( n ~ + T ) ) - ( x ( n ~ ) - e ( n ~ ) ) (1.20)

At this point, an empirically determined constant, d, is introduced. This consta t is small

with respect to the data range, and is used to adjust the estimate of future data points.

If the magnitude of g(n T+ l) is less than d/2, then the error estimate is considered

accurate and no changes are made. Ifg(nT+T) is greater than d/2, then the estimate is too

large. Thus, the error estimate is reduced by d. If g(n T+ 7') is less than -d/2, then the

estimate is too small. Thus, the error estimate is increased by d. These mles are

Once this adjustment has been made, the filter output is detennined as:

y ( n ~ + T ) = x ( n ~ + T ) - e ( n ~ + T ) (1.22)

This filter algorithm is implemented in PV-Wave as the program

ADAPTIVE6O.PRO. This procedure takes a 1-D array as input, and outputs a filtered

array. The input array is ssumed to be in integer format, and output values are retumed

as integers.

To determine the effectiveness of this adaptive filter, we accumulated 30 seconds

of raw (i.e. unfiltered) and 60 Hz notch filtered (df = 4 Hz) MCG data on a normal

subject. The data were acquired at the position of maximum QRS peak strength. The

raw data were filtered using Ahlstrom and Tompkins' adaptive filter with d = 200. The

40 m . - -

@)

20

O

-10 l - . - i

O 200 400 600 800 1 k Time fms]

Fig. 1.9. Adaptive vs. Notch Filtenng: Averaged MCG complex with (a) no filtering, (b) adaptive filtering only, and (c) CTF notch filtering only (af = 4 Hz). Recorded December 17, 1998.

three data sets (raw, notch and adaptive filtered) were low-pass filtered at 250 Hz with a

4'h order Butterworth filter, and averaged using an averaging algonthm called DALECG

(details to follow in Section 2.3). The resultant complexes are s h o w in Figure 1.9.

In the unfiltered case, most of the structure of the cardiac complex is hidden by

the 60 Hz signal. The CTF notch filter is effective in reducing power line noise over the

smooth regions of the complex. However, as noted previously, ringing occurs after the

QRS peak. Ahlstrom and Tompkins' adaptive filter is as effective as the notch filter over

smooth regions. Furthermore, since this filter is quicker to adapt to sharp changes in the

magnitude of the input signal, no ringing occurs after the QRS peak.

In sumary, the non-adaptive notch filter provided by CTF Systems Inc.

produces 60 Hz ringing following sharp changes in input signal strength. This ringing

impedes our ability to observe cardiac events after the R-wave. We can eliminate this

effect by implementing an adaptive filter according to the algorithm by Ahlstmm and

Tompkins. This new filter (with the adaptive parameter d = 200) eliminates 60 Hz

ringing after the R-wave.

1.2.3. Reduciag Environmental Noise Sources

Among the noise peaks apparent in the background scan power spectmm in

Figure 1.1, there is a strong peak near 110 Hz. It was deterrnined that this peak occurs

due to the proximity of the acquisition equipment (speci fically, the isolation transformer

for the DSQ-800/4) to the shielded room. The acquisition equipment had been placed in

a room adjacent to the shielded room for convenience during recording sessions. The

straight-line distance between the transformer and the SQUID was about 4.5 m. To

eliminate the noise source, the equipment was moved to a new room such that the

transformer was about 9 m away from the SQUID. This was effective in attenuating the

noise due to the transformer. The shielded room attenuated al1 other stable environmental

____-_-_II__- - _ _ 20 40 60 80 1 O0 120

Frequency [Hz] Fig. 1.10. Noise Peak Frequency Drift. Power spectra for three consecutive 10 second background scans. The data was acquired at (a) 10:44 am, @) 10:45 am, and (c) 10:46 am. Note the drifting noise peaks beginning at - 40,65,85, and 115 Hz.

noise sources above 1 Hz, ignoring power line signals at 60, 120 and 180 Hz.

The remaining noise peaks fa11 between 20 and 120 Hz, and dnA irregularly over

frequency. Figure 1.10 illustrates the noise peak drift over three minutes. These signals

occur due to electronic noise picked up by the 12.2 m cable comecting the acquisition

system to the magnetic sensors. Any noise picked up by the cable will leak into the

shielded room through the feedthrough hole and compt the measured signal. A layer of

aluminium shielding was added to the cable to reduce RF radiation pick-up.

Shielded Room

F i 1 1 1 Ground Connections . A sketch of the setup used to eliminate ground loops in the MCG acquisition equipment.

In addition. it was necessary to ensure that no ground loops occurred in the

system circuitry. A ground loop occurs when more than one end of a system is connected

to the same ground. This can lead to erratic system performance. The 12.2 m cable was

wrapped with foam at the feedthrough hole to improve electrical isolation fiom the

shielded room, and ensure that ground loops did not occur. Figure 1.1 1 is a sketch of the

ground connections for our system. This set-up elirninates the drifting noise peaks.

1 .t.4. Johnson Noise

The current 3<d order gradiometer DC SQUID contained in the dewar acquired in

1985 has an intrinsic noise of 15 / f l \ l ~ z . The same system was tested by CTF Systems

Inc. in a modem "low noise" dewar in an unshielded environment suggesting a noise

level of 5 f î / . / ~ z . We intend to test our SQUID system in such a dewar. However, at

this noise level, noise sources that could previously be ignored may become significant.

Thus, before acquiring such a system, we must determine if other noise sources or

instrumentation will be the limiting factor for reducing noise in the new dewar.

In particular, we are concemed with the noise introduced by the thermal motion of

electrons within the Aluminium plates of the shielded room. This is referred to as

thermal, or Johnson noise. The purpose of this section is to calculate this noise and

compare the results with expected noise levels for the new dewar. If the calculated

Johnson noise in our shielded room were substantially larger than the noise level of the

new dewar, then acquiring such a dewar would not lower the overall noise level as

rneasured by the SQUID.

Varpula and Poutanen [IO] have calculated and measured the thermal magnetic

noise generated by conductive plates as a f ic t ion of distance from the plate for a

magnetometer and ln order gradiometer. The method of calculating this noise as a

function of distance and fiequency has s h o w good agreement with experimental results.

They also estimated the Johnson noise generated by a shielded room. The rms Johnson

noise generated above an infinite plate is detemined by summing the dipole terms caused

by the thermal motion of electrons in the conductor parallel to the plate walls. Suppose

an infinite conducting plate of thickness, t, is placed in the x-y plane with its upper

surface at z = O. Ampere's law can be used to denve the magnetic field generated at

height, z, due to a dipole at height, -2'. By integrating z s over t, the magnetic field

perpendicular to the plate can be found. For f # O Hz, this derivation gives a complicated

integral that can only be calculated nwnencally. However, for f = O Hz, the z-component

of the rms magnetic field as measured by a magnetometer is:

The variables, k, T, p, O-, and Z, are the Boltzmann constant, and the temperature,

magnetic pemeability, conductivity, and thickness of the plate, respectively.

For the case of higher order gradiometers, the exponential term including z is

altered. We assume that a 3" order gradiometer measures a weighted difference between

the magnetic field sensing coils at z, z+l, 2+21, and z+31, where 2 is the SQUID baseline

length. Assuming that B, is constant over the area of the coil, the magnetic field

measured by a 3rd order gradiometer will be [ I l ] :

B,"(r) = BzO(z) - 3Bz0(z + 1) + 3BZ0(r + 21) - BZ0(z + 32) (1 -24)

From this, we can determine the expected Johnson noise at f = O Hz. AAer integration,

w e find that the Johnson noise can be written as:

In the case of thin slabs (z » t ) , the thermal noise fiequency dependence can also

be approximated. Stroink and MacAulay [12] estimated the dependence to be:

In this equation. f, = (4opzt)-'.

The thermal noise due to a shielded room c m be calculated by approximating the

walls of the room as six infinite plates. The square mot of the sum of the squares of the

noise contributions fkom each plate gives the total Johnson noise expected. Stroink and

I I o. 1 1 10 1 O0 1 k

Frequency [Hz]

Fig. 1.12. The simulated magnetic field spectnim generated by thermal noise as measwed by a 3rd order gradiometer. Data is s h o w for a SQUID placed 1.2 m above an infinite conducting plate (dashed line) and in the centre of our shielded room (solid line)

MacPlulay [12] fond that this approximation matched well with experimental data.

Our shielded room is 2.4 m high x 2.4 m wide x 3.6 m long. The Aluminium

plates used for the walls are 1.88 cm thick

The 3rd order gradiometer SQUlD has a 6.0

the standard value in a vacuum, 4nx10-'.

and have a conductivity of

cm baseline. The magnetic

36x 1 o6 R;" m" .

permeability is

The simulated thermal noise spectrum calculated for Our

distance of 1.2 m away from an infinite conducting plate, and in

room, is shown in Figure 1.12. At

f l l d ~ z . At frequencies below 1 Hz,

fkequencies above 1 Hz,

3d order gradiometer at a

the centre of the shielded

the noise is below 0.01

the magnitude still stays below 0.1 /r/dHz. Thus,

Fig. 1.13. The simulated magnetic field at 1 Hz generated by thermal noise in an Aluminium conducting plate vs. distance, z, as measured by a 3rd order gradiometer. Data is shown for the infinite conducting plate (dashed) and the shielded room (solid). In the case of the room, the SQUID starts at the centre of the room floor and is shifted vertically.

for measurements taken in the centre of the shielded room, the intrinsic noise of the

SQUID in the new dewar (-5 PI/&) will be the limiting factor to the system

performance, not the Johnson noise generated by the Aluminium walls.

In order to

h i t i n g factor, we

determine the conditions under which Johnson noise rnay become a

looked at the noise as a fùnction of gradiometer position. Figure 1.13

shows the Johnson noise calculated at 1 Hz as a finction of position above the shielded

roorn floor. The noise levels only become large enough to affect readings (-5 /T/~HZ)

when the SQUID is within 20 cm of the floor. Since during regular use the SQUID

remains very close to the centre of the room (r = 1.2 + 0.2 m), we conclude that our 3rd

order gradiometer with 5 ~ \ I H Z of intrinsic noise will not detect Johnson noise

generated by the shielded room.

1.3. RESULTS

1 A l Final Noise Spectrum

Afier completion of al1 system modifications, a finai background scan was

acquired for the 3rd order gradiometer DC SQUID in the old dewar. We accumulated ten

seconds of magnetic data with no patient in the shielded room. The data were separated

into ten one-second intervals. An FFT was perfonned on each interval, and the resulting

noise spectra were averaged. The averaged noise spectnim is shown in Figure 1.14.

The averaged background noise spectnun for the new 3d order gradiometer has an

intrinsic noise of -15 ~ ~ / I / H z . This is higher than the expected level of 10 / ~ / d ~ z as

suggested by CTF Systems. However, it is a two-fold improvement from the intrinsic

noise of the old 2"d order gradiometer. For a bandwidth of 250 Hz, the intrinsic noise in

any data set acquired with the new system will be -240fl. The znd order gradiometer

system acquiring data ovet a bandwidth of 125 Hz has an intrinsic noise of -34OjT. The

new system reduces the intrinsic noise by a factor of 1.4 for data acquired with twice the

recording bandwidth.

1.3.2. Determining the Averaged SignaETo-Noise Ratio

To determine the signal-to-noise ratio for the new system, we acquired MCG and

limb lead data on 11 nonnai subjects at 56 sites in a plane 0.5 cm in fiont of the torso.

1 f. - --A---- -- -- - - -- A--

1 10 100 Frequency w]

Fig. 1.14. A plot of the averaged power spectrum for ten 1 second background scans collected with our new system after noise reduction techniques have been applied. Notch filten are applied to this data at 60, 120, and 180 Hz. Uni-directional low pass filtering is applied at 250 Hz.

The subject group characteristics are outlined in Chapter 4 of this thesis. Thihirty seconds

of data were acquired at each site at a 1250 Hz sampling rate, and 1ow pass filtered at 250

Hz with a bi-directional 4* order Butterworth filter. Power line noise was filtered out of

the raw data using adaptive filters at 60, 120, and 180 Hz. These filtered data were

averaged to reduce noise. Data acquisition and averaging was perfonned according to the

procedures descnbed in Chapter 2 of this thesis. The noise after averaging and the peak

signal strength were detemined for each subject.

For each subject, we c m create a magnetic field map (MFM) that describes the

magnetic flux at al1 points in a 26.6 cm x 30.4 cm area located 0.5 cm in fiont of the

subject's torso. We can use this MFM to determine the site at which the strongest R-

wave amplitude occurs. This site is defined as the peak signal site. Figure 1.15 is a

MFM calculated during the peak of the R-wave for subject 202. The plus signs (+)

indicate actual measurement sites. The peak signal site is indicated by a plus sign

enclosed in a box.

Figure L16a) shows the averaged MCG complex at the peak signa1 site for the

same subject. The noise in the averaged data set is determined by calculating the rms

average of the signal in the 100 ms before the P-wave onset. In most cases, the signal in

this time interval is not Bat. Thus, we subtract a 4'hrder polynomial fit to the data fiom

the signal in the UP interval. The noise after averaging for the subject is the rms average

of this "baseline corrected" signal. Figures 1.16b) and 1.16~) show the last 100 ms of the

segment before the P-wave (the UP segment) at the peak signal site for subject 202

before and after baseline correction, respectively. We assume that the noise in the UP

interval is about the same as the noise over the entire MCG complex. Thus, the peak

signal magnitude divided by the rms noise in the UP interval gives the overall signal-to-

noise ratio.

Table 1.2 lists the measured peak signal strength, m s noise, and signal-to-noise

ratio for each of the 1 1 subjects. The average measured noise level is 39JT (STD=lO/T)

which is slightly lower than the expected intrinsic noise for this system. The average

peak signal strength is 36 PT. Thus, the signal-to-noise for averaged MCG data acquired

on the new system is -1 000. Reina Lamothe [4] determined the specifications for the old

system in 1994. The peak signal strength, nns noise after averaging, and signal-to-noise

ratio were 16 PT, 54/î, and 300, respectively.

enclosed in a box. Contour labels represent magnetic field magnitude in picoTeslas

Fig. 1.16. Determining the Signal-to-Noise Ratio. (a) The averaged MCG complex at the peak signal site, (b) the UP interval signal and a 4th order polynomial fit, and (c) the baseline corrected UP interval signal for subject 202. Recorded January 27th, 1999.

Time [ms] 0.2 I , - - - , 1

- (b) - - - - - -

- - - -0.4 - -

- -

-0.6- - - v - k - -

- S -0.8 I

1 - . . 1 * 1 -

0.2

0.0

-0.2

- v - 1 I . . . I 1

O 20 40 60 80 1 O0 Time [ms]

1 I 1

Average 1 35.6 1 39 1 1000 1

Subject

202 203 204 205

TabIe 1.2. Signal- to-Noise Rcsults. R-wave amplitude, rms noise level, and signal-to- noise ratio for the peak signal site for 11 normal subjects. Al1 &ta acquired using the new 3d ordtr gradiomctcr SQUID in the old dcwar.

The signal-to-noise ratio has increased three-fold for the new system. By

cornparison, the new system outperforms the old system in al1 three parameters while

doubling the recording bandwidth. We can look for consistency between the

pedorrnance of the old and new systems by comparing the respective intrinsic noise

magnitudes after correcthg for system improvements. The noise after averaging for the

old system was 54fl. However, the bandwidth for the old system is half of the new

value, and the intrinsic noise is twice the new value. By rearranging equation 1.1 and

asserting that:

Peak Amplitude @Tl 28.8 53.4 18.0 32.6

RMS Noise l/rl 34 29 44 3 0

Signal-to-Noise

855 1857 409 1104

f gradiome ter 1 - 2.5 cm fiont end coi1 Sensor Type

1 - 2.5 cm fiont end coi1 1 - 6cmbaseline

Old MCC System 1 New MCG System 2nd order asymrnetric i 3rd ordcr syrnmetric gradiometer

I - Average Peak 1 - 16pT" i -36>~**

Intrinsic Noise No. Channels Bandwidth Sampling Frequency ADC Range ADC Bit Resolution

- 4 cm bascline 30flldHz 56 sequential

Signa1 (R-Max) Noise after

1 S/N Ratio 1 1

15 fT/dHz 56 scquential

Averaging Typical Max.

Table 1.3. MCG specifications for the old and new SQUID systcms. n is the DSQ-400 SQUID connollcr DAC output range (typically n = 1/2 or 1). Computed from data of 27 normals with an average agc of 47.7 (STD = 7.6) ycars. ** Computcd fiom data of 1 1 normals with an average age of 19 (STD = 2 ) years.

(DC - 125) Hz (DC-250) Hz 500 Hz 1 1250 Hz i 150pTx n 1 f 160nT 7 5 f ï x n 1 300 fï

- 54/r8

and:

- 3 9 f F

- 300*

Af = 24f"

then the expected instrumentation noise after averaging for the new system is:

- 1000**

This is very close to the measured noise afler averaging for the new system. Thus, the

expected improvement in noise performance was achieved by the new system.

In summary, our measured value of the noise after averaging, 39 fl, is slightly

less than, but very close to the calculated instrumentation noise for a 15 / f / d ~ z system.

l p - -

- -- -----A- - 1 10 1 O0 1 k

Frequency [Hz]

Fig. 1.17. A plot of the averaged power s p e c t m for ten 1 second background scans collected with the new gradiometer in the new CTF dewar. No notch filters are applied to this data. Uni-directional low pass filtering is applied at 250 Hz.

Also, the reduction

is consistent with

suggests that the

in the measured noise

the improvements in

after averaging

the bandwidth

fiom the

and the

old to the new system

intrinsic noise. This

limiting factor in our new system's performance is still the

instrumentation, not the environment, after removing power line noise. The average

signal-to-noise ratio for MCG data acquired on 11 normal subjects is

compares the MCG specifications for the old and new SQUID systems.

1000. Table 1.3

1.3.3. Addendum: The New Dewar

In June 1999, CTF Systems Inc. replaced the cryogenic dewar that houses the

gradiometer and SQUID at the Dalhousie Biomagnetism Lab. The new dewar uses

substantially less metal as a heat-sink. The reduction in metallic content Ieads to a

decrease in the Johnson noise (as explained in Section 1.2.4) generated by the dewar.

This reduces the intrinsic noise. Figure 1.17 shows the averaged power spectrum for a

background scan taken in our shielded room with the 3" order gradiometer in the new

dewar.

The average noise over the fiequency range fiom 5 to 250 Hz is - ~ / T / ~ H z . There

is a larger noise peak at 60 Hz. However, this signal can be removed later using adaptive

filtering. Thus, following Equation 1.2, the expected noise aAer averaging for our

gradiorneter in the new dewar is 23 f l . The 3d order gradiometer is still used in this

system and the distance between the front coi1 and the outside wall of the dewar is 1 1 mm

for bath the old and new dewars. It follows, then, that the expected peak signal strength

will remain the same (39 PT). Thus, the new signal-to-noise ratio for averaged MCG

measurements with the new system in the new dewar over a 0-250 Hz recording

bandwidth will be - 1700.

The motivation behind the acquisition of the new SQUID and dewar system is to

obtain an averaged MCG signal-to-noise ratio comparable to that of the ECG

measurernents. According to Lamothe [4], the signal-to-noise ratio for averaged ECG

measurements recorded over a 0.025-1 25Hz recording bandwidth is 1700. With the new

system, the signal-to-noise ratio is the same for MCG data acquired with twice the

recording bandwidth. Thus, the desired improvements have been achieved.

Chapter 2

MCG Metbodology

2.1 INTRODUCTION

A magnetic fieid map (MFM) is generated by the acquisition of temporal MCG

data at a number of sites over a plane in fiont of the torso. Plotting the signal strength at

al1 measurement sites for a given tirne instant and interpolating between these points

creates these two-dimensional plots. Our magnetic measurement system consists of one

DC SQUlD connectai to an asymmetric 3m order gradiometer (see Figure 2.1). Since we

use a single-channel system, we can only measure the magnetic flux at one site at a time.

Thus, we are required to acquire data at different sites sequentially. This is time

Fig. 2.1. A diagram of the asymrnetric 3" order gradiometer used in the new Dalhousie MCG acquisition system. The baseline length, 1, is 6.0 cm. The small and large coi1 diameters are 2.54 cm and 5.67 cm, respectively.

consuming. Also, since the system (or in our case, the subject) must be shifted after each

measurement, SQUD placement inaccuracy can occur .

To maintain reasonable session lengths and avoid inaccuracies in sensor

placement, the operator must be careful to adhere to certain data acquisition procedures.

Atso, we employ cardiac beat matching and averaging to improve the signal-to-noise

ratio for al1 data. This chapter will outline the procedures involved in acquiring,

Fig. 2.2. A block diagram of the apparatus inyolved in acquinng rnagnetic cardiac data. The dewar contains a 3r order gradiorneter connected to a DC SQUID. Magnetic data is collected by the SQUID, arnplified, and sent to the DSQ for alignment with the three sets of limb lead data. Al1 data is then transferred to the Mac for processing, display, and storage.

processing, and averaging MCG and limb lead data at the Dalhousie Biomagnetism Lab.

In particular, we will summarize:

the positions at which MCG and limb lead data are acquired,

the protocol enforced dunng the measurement session,

the data processing techniques which will be applied to the data, and

the averaging process used for noise reduction.

2.2 MCG DATA ACQUISITION PROTOCOL

Below is a description of the current protocol for acquiring MCG data at the

Dalhousie Biomagnetism Lab. A block diagram of the data acquisition system is show

in Figure 2.2.

Before each measurement session, the operator confirms that al1 apparatus is

working. To ensure that noise sources are being properly attenuated, the power spectra

for ten 1 second background scans are averaged and plotted with the CTF acquisition

software. The operator should verify that there are no noise peaks and that the intriwic

noise is no larger than expected (- 8 / f /dHz for the 3d order gradiometer in the new

dewar). For comparison, Figure 1.17 in Section 1.3.3 of this thesis shows an optimal

background noise s p e c m with no notch filten applied to remove power line signals.

The dewar needs to be filled to at least 20% with liquid Heliurn so that the

S Q W remains superconducting for the duration of the measurement session. Also, the

voltage across the battery pack for the analog-to-digital converters in the DSQ unit

should be checked. The pack uses eight 3 V lithium batteries to supply f 12 V to the

converters. Al1 batteries must be charged to ensure proper ADC calibration.

When the subject arrives, s/he is required to read and sign a consent form. This

form contains a bnef synopsis of the purpose, procedure, any risks, and benefits of the

experiment. There is also a MCG Acquisition Form that the operator uses to record

hisher progress throughout the session. An example of such a form can be found in

Figure 2.3. Some clothing may have magnetic properties that will cormpt the acquired

magnetic data. Thus, the subject is required to Wear a hospital gown for the duration of

MCG Data Acquisition Form

Date: Operator:

Narne: Sample Rate: Hz File: Sex: Low Pass: Hz Offset Removed: C Age: High Pass: Hz €CG: O Yes C No Height: Other Filters: Hz Record Time: sec Weight: Bandwidth : Hz

Comments:

Jemm - Al +A2 +A3 +A4 +A5 +A6 +A7

81 i+- 62 + 83 +- 04 + 85 + 66 + B +I - kl +C2 + W +C4 +CS +C6 +C7 GRlD

D l 4-02 +D3 +O4 +O5 +û6 +d +intcrcoiaispaœ Rows A-H

E l -) €2 -) E3 -) E4 ES -b €6 -) €7 Col 1-7

F1 4 - F 2 4-F3 4-F4 4-FS 4-F6 4 - F 56 sites

REPEATs

hl + 0 2 -b G3 + 04 -W GS + 0 6 + 0 7

Hl + H2 + H3 + H4 + H5 + H6 + H #

Fig. 2.3. A sarnple MCG Acquisition Form for the operator's use during a measurement session. Subject and session parameters are recorded at the top. The recording sequence is shown. There is also a table for noting the subject platform position at each site.

the experiment. Any metal accessories wom by the subject (Le. necklaces, eamngs. etc.)

should be removed.

For the remainder of the measurement session, the subject is in a supine position

on a moveable padded platform in the Aluminium shielded room. MCG data is acquired

over a 26.6 cm x 30.4 cm area in a plane just above the highest point on the tono dunng

inhalation. A 7 x 8 grid defines the points at which the magnetic flux will be measured.

Adjacent grid points are 3.8 cm apart. The rows are lettered fkom A to H (top to bottom),

and columns are numbered between 1 and 7 (subject's right to lefi). To ensure that

magnetic field maps acquired on different subjects are aligned and that measurements can

be reproduced, site D3 is always located at the 4" intercostal space on the sternum of the

subject.

A Hewlett-Packard 1505A electrocardiograph simultaneously acquires ECG limb

lead data with a 100 Hz bandwidth. Electrodes are attached to the subject's wrists and

ankles. A floating amplifier isolates the subject fiom the electrocardiograph electronics.

The potential differences across the lefi leg, and nght and lefl arm electrodes are

recorded. The right leg connection is used to ensure that the potential of the floating

amplifier is close to that of the patient. The signais are amplified by a factor of 1000

[13]. Data is sent to the DSQ-80014 where it is aligned with the magnetic data, and saved

on the MacIntosh. The limb lead data is used as reference data during the averaging of

magnetic cardiac data (see Section 2.3 of this thesis).

As a trial measurement, the operator acquires 30 seconds of MCG and limb lead

data at site D3. The data is displayed on the MacIntosh. It is important that one checks

the limb lead data to ensure that the electrodes are in good contact with the subject's skin.

sternum l ! ssL

Fig. 2.4. Magnetic cardiac data acquisition site positions with respect to a subject's torso. Measurements begin at site Al and continue across and down until site H l is reached. Site D3 is aligned with the subject's 4Lh intercostal space on the sternum.

If good contact has not been achieved (as is apparent by noisy data), the skin at the

electrode positions should be rubbed with rough paper to reduce the contact impedance.

The actual measurement session begins at the top-right corner site (eom the

perspective of the subject) and proceeds to the left, then down one row, back towards the

right, down another row, and so on. Figure 2.4 shows the position and acquisition order

of the grid sites with respect to the subject's torso. At each site, MCG and limb lead data

are acquired for 30 seconds at a sarnple rate of 1250 Hz, and low-pass filtered with a

unidirectional 4h order Butterworth filter at 250 Hz. No notch filtering occurs d u h g the

acquisition process.

About 45 seconds elapse between the end of one scan and the beginning of the

successive scan. During this interval, the raw data are displayed on the Maclntosh. The

operator checks for artifacts such as spikes in magnetic data (usually due to subject

movement) or attenuation of ECG signals (usually due to loss of electrode contact). In

the case that an artifact occurs, the problem is corrected and the data are re-acquired.

Once "good" data have been acquired, the time scans for al1 channels are saved. The

subject is then positioned at the next site by moving the platform. Data are acquired for

the new site, and this procedure repeats until the last site (site Hl) is reached.

The entire data acquisition process is completed in about 70 minutes. Ideally, the

subject's heart rate remains constant for the entire study period. Larnothe [4] found that

the average subject heart rate decreases by 5-10% during the first 30 minutes of a

measurement session, and then becomes stable. The subject may be active during the

initial stages of the session, but relaxes as the recording session progresses. However, the

duration of large instantaneous signals, such as the R wave, have very little dependence

on heart rate 1141. Aiso, changes in the timing of slower events (i.e. in the PR and QT

intervals) are small enough to be ignored for mal1 deviations in heart rate. Thus, the

effect of 5-10% heart rate variations on differmt cardiac complexes can be ignored.

At the end of the measurement session, the electrodes are removed. The ADC

battery pack is disconnected to preserve battery power. Once the subject has ieft the

shielded roorn, the average power spectnun for another ten one-second background scans

is plotted. This ensures that the SQUID system behaviour has not changed over the

session interval.

Appendix A contains a brief synopsis of the experimental procedure for acquiring

MCG data. This can be used as a guideline for future sessions.

2.3. AVERAGING MCG DATA

Our biomagnetism group uses the Dalhousie ECG Analysis Program (DALECG)

to average cardiac data. Al1 averaging occurs off-line on a VAXNMS system. The

DALECG program has three fùnctions: beat detection, family assignment and beat

alignrnent, and averaging [15]. The input to our version of DALECG is the cardiac data

for three limb leads (used for beat detection) and one MCG site. Essentially, the program

detects cardiac complexes fiom the limb leads using slope and amplitude criteria. A

detected beat becomes the template for a new family of beats, or is assigned to an

existing family of similar beats. This is repeated for al1 complexes. The b a t s in a family

are aligned, baseline adjusted, and sumrned for al1 four input files. At the end of

averaging, the family with the most beats included becomes the representative family.

As explained previously, averaging similar cardiac complexes produces a 1 /hl reduction

in random noise, where N is the number of beats in the averaged family. Figure 2.5

outlines the data averaging process explained below.

Raw data are acquired by the MacIntosh Quadra 650 cornputer. Dunng

acquisition, al1 data are low-pass filtered at 250 Hz with a uni-directional 4th order

Butterworth filter. To M e r reduce the noise above the recording bandwidth, a post-

Binary Data Acqimed a cl"F Acquisition j 250HzLow-PassFilter : S o h e : 4th Order Bi -Directional Buttcrwonh ;

UNIX 56 x 4 data files

Adaptive Filter 1 I

Ahlstrom and Tonpkins i CTF2ASC.PRO 1

Repeat for al1

56 sites

1 4 avcragcd databarc files

i : ASC2EF'S.EXE i ASCII to EPS Conversion :

56 avcragcd MCG conpkxs 3 x 56 avcragcd ECG conpkaes

Fig. 2.5. Data Averaging Process. A flowchart of prograrns and platforms utilized to average 56 sites of MCG and limb lead data.

acquisition bi-directional low-pass filter is applied at 250 Hz. This filter is built into the

CTF acquisition software. Figure 2.6 shows a cornparison of the power spectra of fiitered

data acquired with no subject in the shielded room. With uni-directional low-pass

filtering at 675 Hz (i-e. the Nyquist fkequency) during acquisition, the noise does not drop

below 1 / T / ~ H Z at higher fkequencies (i.e. 5300 Hz). When a uni-directional low-pass

filter with a cut-off tiequency of 250 Hz is used, the high frequency noise falls jus t below

1 H Z . Applying the post-accumuiation bi-directional filter at 250 Hz reduces the

high fiequency noise to 5 a ~ / d H z . This represents a decrease of 3 orders of magnitude in

the noise at high fkequencies, and leads to a reduction in noise in the acquired signal.

After bi-directional low-pass filtering at 250 Hz, power line filters are applied to

the raw MCG and limb lead data. To achieve this, the MCG and limb lead binary data

files are transferred to the UNIX station to be converted into ASCiI format and adaptive

filtered. These two hctions are accomplished using a PV-Wave program called

CTF2ASC.PRO which implements the adaptive filter of Ahlstrom and Tompkins [9]

descnbed in Section 1.2.2 of this thesis. The filter is applied at 60, 120, and 180 Hz. 60

Hz noise as measured by the 3d order gradiometer inside the shielded room is usually -1

p ~ / d ~ z . For the raw data, this converts to an amplitude of -7 pT. As demonstrated

earlier, adaptive filtering reduces this noise to the background level.

MCG and limb lead data are saved in ASCII format on the Sun in four separate

files. However, the averaging algorithm expects its input data to be sampled in the ECG

Processing System (EPS) format. This format consists of one file containing a 1024-bit

--

1 OOm 1 10 1 O0 1 k Frequency [Hz]

Fig. 2.6. Cornparison of low pass filters: Background FFTs for (a) uni- directional filter at 675Hz and @) 250Hz, and (c) bi-directional filter at 2SOHz. Notch filters were not used here.

Function Subject number # Seconds averaged by DALECG (1 or 2) Data realignment boolean (1 or O) # Data points per lead in the averaged complex First averaged point in data array Last averaged point in data array P-Wave onset tirne [ms] P-Wave max. time [msl

Parameter ISUEJ

NSEC2A IALIGN M L F

- - P-Wave offset time rmsl

Format 32 bit integer 16 bit integer 16 bit integer 32 bit integer

R-Wave onset time imsl R-Wave max. time [ms] R-Wave offset tirne fmsl

IAV 1 1 lAVENDD

PWON PWMAX PWOFF RWON

RWMAX RWOFF TWMAX TWOFF

SINT NINCL

Table 2.1. Header information for output database files. Parameters are listed in the order in which they appear in the header.

32 bit integer 32 bit integer

32 bit floating point 32 bit floating point 32 bit floating point 32 bit floating point 32 bit floating point 32 bit floating point 32 bit floating point 32 bit floating point 32 bit floating point

32 bit integer

identification header, followed by the interleaved sampled data for al1 four acquired

channels. The header includes important acquisition information such as sampling rate

(up to 2000 Hz), recording length, scale factors, etc. Thus, a format conversion fiom

ASCII to EPS format must occur.

The four filtered ASCII data files (MCG and limb leads) are transferred to the

VAXNMS system. The files are converted into one EPS file with al1 the appropnate

header information. Our version of DALECG expects that limb iead data in the EPS file

will be scaled at 410 counWmV. Similarly, MCG data should be scaled at 41 countslpT.

However, the CTF acquisition sof?ware has different scale factors for binary storage.

Fig. 2.7. A plot of the averaged MCG data at 56 measured sites. Data were acquired on January 27th, 1999 on normal subject # 202.

CTF limb lead data is stored at 1.49~ 10' counWmV, and MCG data is stored at 3.3 1 x 1 o3

counts/pT. Thus, limb lead and MCG data must be rnultiplied by 2 . 7 5 ~ 1 0') and 1 . 2 4 ~ 1 O-*,

respectively, before being included in the EPS file.

Once the EPS file has k e n compiled, the data are ready for averaging by

DALECG. AAer averaging, the prograrn SWPCHG stores the averaged complex and 480

bits of relevant header information for each channel in a data base file. The header

information is outiined in Table 2.1. This process is repeated for al1 56 magnetic sites.

The output is four database files (one MCG and three limb leads). Each file contains 56

averaged cardiac complexes (one for each measurement site), as well as relevant header

information for each site. Figure 2.7 is a plot of the averaged MCG data at al1 56 sites for

a normal subject. Appendix B contains a brief description of the computer algorithrns

used to perform the above analysis.

2.4. PLOTTING AVERAGED MCG DATA

There is a number of plotting utilities available for viewing averaged cardiac data.

These algorithms m on either a UNIX or VAXIVMX platform, and are used to create

single channel time plots and spatial contour plots for a given time slice. It is assumed, in

al1 cases, that a MCG database file exists and contains 56 averaged cardiac complexes

with al1 relevant header information.

The plotting programs implemented on the UNIX station are al1 compiled in PV-

Wave. The PLOTLEAD prograrns plot an averaged MCG complex fiom one site in a 56-

site recording. The basic version (PLOTLEAD1 .PRO) of this prograrn takes the database

filename, scale factor, and site number as input. It reads al1 56 data sets, and plots the

specified site on-screen or to a file. The second implementation (PLOTLEAD2.PRO)

allows the user to repeatedly choose new sites to plot.

The PLOTMAP prograrns plot a contour map at a specified time instance with

respect to a given cardiac set point. in the basic version (PLOTMAPI.PRO), the user

chooses the filename, scale factor, cardiac set point, and the number of time steps to shifl

with respect to that set point. The program constructs a contour map that is either

displayed on-screen or exported to a file. PLOTMAP2.PRO continuously prompts the

user to enter the number of tirne steps to shift away 60m the cardiac set point. A new

contour plot is generated at the new time instance. In this way, one can watch the MFM

change over time.

On the VAX/VMS system, it is possible to print successive contour maps fkom a

MCG database file to a single page. This is a three-step process, and al1 three prograrns

are based on a question and answer format. The program SELECT.EXE is implemented

to extract the necessary spatial data from the database file. It prompts the user for input

and output filenames, the plot type ('spatial MCG' in this case), a cardiac set point, and

start and end times for the contour plots relative to the chosen set point. Enough data are

extracted from the database file to create one contour plot for every two milliseconds in

the given interval. The output file is called FOR012.DAT by default, so the output file is

referred to as a 0 12-format file.

Next, MAPDAT.EXE is implemented. This program specifies exactly which

time slices will be plotted. It prompts the user for the total number of maps to be plotted,

and the time of the first plot relative to the cardiac set point, in milliseconds. Also, the

th user can choose to plot only every N map, where N is a user-defined integer. The

algonthm assumes the input cm be found in the file FOROIZ.DAT, and sends the output

to a file called FOR030.DAT.

The final step in producing a senes of contour maps on the VAX is to run the

program PSMAPS.EXE. It uses the file FORO3O.DAT as its input. The user can set the

plot scale factor, a comment to be printed along with the maps, and the type of graphics

device to which the plot will be sent. I f the graphics display type is set to PostScnpt, then

the senes of contour maps is found in the file PGPLOT.PS. An example of such a set of

contour plots is shown in Figure 2.8.

Fig. 2.8. Contour plots of averaged MCG data at the R-wave maximum and in 1.6 ms intervals &er (as indicated to the lefi of each plot). Data was acquired on Febniary 3, 1999 on subject # 206. The two nurnbers directly to the right of each MFM are the maximum and minimum magnetic field, picoTeslas.

Chapter 3

Inaccuracies In Imaging Cardiac Sources Using Electrical Or Magnetic

Sensors

3.1 INTRODUCTION

Some aspects of the elecûical activity in the heart can be modeled with a single

moving current dipole. Performing an inverse solution on a MFM and/or body surface

potential map (BSPM) can localize such a dipole. The accuracy with which the dipole

position is found is dependent upon the properties of the inverse solution and the

precision of the acquired MFM and BSPM.

Kozmann et al. [16] acquired multiple BSPMs for a group o f 52 normal subjects over

10 years. For each subject, the BSPMs at the QRS peak were represented as the sum of

12-dimensional spatial Karhunen-Loeve vectors, each with the appropriate coefficient, c.

AV Ring -

Ventricular h

Fig. 3.1. The atrial and ventricular myocardium. This mass consists of muscle fibres that contract when an electrical impulse passes through. The atrial and ventricular myocardium are electrically isolated by a non-conducting layer called the atrioventricular (AV) ring [20].

The sum of the variances of the Karhunen-Loeve coefficients of a particular vector with

the group average coefficient of that vector is defned as the total variance between maps.

The reprodiicibility error is the total variance between maps acquired fiom the same

subject. The total variance (Tç? and reproducibility error (RE) can be written as:

AV

indle (

Fig. 3.2. The cardiac conduction system. Electrical impulses originate at the SA node, travel through the atria to the AV node. Conduction is delayed for about 100 ms at the AV node. The conduction continues rapidly through the bundle of His and to the rest of the ventricular myocardiurn [203.

dl In equation 3.1, cij is the i spatial Karhunen-Loeve coefficient of subject j, Ci is the mean

of the i& coefficients, and N is the number of subjects compared. In equation 3.2, cij(1)

and cij(2) are the i~ coefficients for the first and second measurements made on subject j,

respectively. Kozmann et al. found that approximately half of the total variance between

maps obtained at the same tirne instance on the same subject was attributable to

reproducibility error. T u m v a et al. (1 71 estimated the reproducibility error for their

systern to be as high as 25% of the total variance for simulated BSP data of normal

subjects. We intend to estimate the reproducibility error by determining the accuracy to

which a current dipole in a homogeneous torso can be located after inaccuracies in the

placement of both magnetic and electrical leads has been introduced.

We simulated BSPMs and MFMs for a single current dipole at seven locations in

the heart using a forward solution algorithm [la- 191. With these data, we simulated the

misplacement of magnetic and electrical sensors. The accuracy of Iocalizing the dipole

under conditions of sensor misplacement was determined. We also compared the

sensitivity of ECG and MCG measuring methods to sensor misplacement.

3.2 BACKGROUND: WOLFF-PARKINSON-WHITE SYNDROME

Figures 3.1 and 3.2 show the conduction system of the normal human heart. The

atria and ventricles contract and relax to pump deoxygenated blood into the lungs and

send oxygenated blood to the rest of the body. A non-conducting layer called the

atrioventricular (AV) ring separates the atrial and ventricular muscle masses (or

myocardium). The heart contracts and relaxes due to the propagation of electrical

impulses through the cardiac muscle fibres. An excitation in the sinoatrial (SA) node will

propagate through the atria in an activation wave. This wave can only p a s to the

ventricles through the AV node s h o w in Figure 3.2. The conduction is delayed at the

AV node for about 100 ms. It then propagates rapidly through the bundle of His and its

branches, and the Purkinje fibres towards the rest of the ventricles [20].

Wolff-Parkinson-White (WPW) syndrome occurs when a band of conducting

myocardial fibres stretch across the AV ring. This is refmed to as an accessory pathway

( AP) . Pre-exci tation c m occur in the ventricular myocardium when cardiac impulses

propagate across such an AP without encountering any delay at the AV node. The

superposition of a normal ventricular activation wave with an early ventricular

depolarization wave is called a hision complex. This ECG abnormality is caused by

WPW syndrome and manifests itself as an early initial deflection of the QRS complex,

and a shortened PR interval (5 120 ms). The conduction through the well-localized AP

can be modeled as a current dipole.

WPW can lead to retrograde conduction, where the activation wave in the

ventrides reactivates the atria through the AP. This activity is then propagated to the

ventricles via the bundle of His. This circular activation process is called paroxysmal

tachycardia, and cm cause sudden cardiac death. WPW has an incidence of 1 or 2 per

thousand people in the general population. The current mode of treatrnent is RF or

catheter ablation of the AP [21].

3.3 METHOD

The simulation of sensor misplacement and the recovery of a current dipole involve

the following steps. Single current dipoles are used to model cardiac preexcitation, as

described below. For each dipole, a foward solution is calculated to determine the body

surface potential and the magnetic field generated by the dipole on and outside a

homogeneous torso model, respectively. Sensor misplacement is then simulated. For

varying electrode and magnetometer misplacements, the inverse solution is calculated

and the shifted current dipole positions are found. The displacement of the recovered

dipole is defined as the square root of the sum of the squares of the x, y, and z differences

in the positions of the shifted and unshifted dipoles. In al1 cases, the displacement of the

recovered dipole due to the sensor shift is determined.

3.3.1 The Fonvard Solution

Cornputer modeling of the potentiais and magnetic fields in the extracardiac

regions is accomplished using the forward solution of Purcell el al [18]. As a first

approximation to finding the potential, 4, and the magnetic field, 5, generated in a finite

homogeneous volume conductor, the fields for an infinite volume conductor are

determined. The potential due to a current dipole in an infinite volume conductor is:

where the scaiar 0 is the conductivity, and the vector 5 is the current dipole vector.

Similady, the magnetic field due to a current dipole in an infinite homogeneous volume

conductor is [22]:

where P is the magnetic permeability.

Corrections must be made to Equations 3.3 and 3.4 to account for the finite size of

our intended torso. To accomplish this, the boundary element method is used. This is a

numerical technique for calculating the effects of the boundaries of the volume conductor

on the surface potential and magnetic field generated by a current source. The surface of

the volume conductor is approximated as a tessellated surface. Given that the tessellated

surface is made up of M triangles, the potential at a point 7 is [23]:

where 4 is the conductivity at the source location. SI represents the k~ triangle of the

tessellated surface. The conductivities just outside and inside the surface, Sk, are a ' k and

cYk, respectively. dC2 is the solid angle of the surface, Sk, as observed fiom r. The

magnetic field for the homogeneous torso mode1 is defined as [23]:

In this equation, ,u is the magnetic permeability.

The discretized foms of Equations 3.5 and 3.6 must be used to perform numencal

calculations. Equation 3.5 gives the discretized surface potential as [19]:

where :

g = 20,#t

and:

In Equation 3.9, Rlk is the solid angle subtended by the triangle, A*, at the centroid of dk.

However, the set of equations defined by Equation 3.7 is singular and has no unique

solution.

Lynn and Timlake [24] used the Weilandt deflation technique to remove the

singularity and aHow the set of equations to be solved. The deflated potentials can be

calculated as:

This equation set has the same structure as Equation 3.7, but does not contain any

singularities. The solution to this set of deflated equations leads to the reconstruction of

the actual potentials on the torso surface.

Equation 3.10 can be written in matrix multiplication form as:

Y = (r -n)-Wm (3.1 1)

In this equation, the N x 1 vectors, Y and am, contain the rnean values of ry, and bqi at

each triangle. The N x N matrices, 1 and C2, are the identity matrix and the matrix

containing al1 deflated mi values, respectively. The actual surface potentials can be

calculated from the deflated potential vector.

Once the discrete swface potentials are known, the magnetic field outside of the

torso can be calculated as:

B = B , + A Q , (3.12)

where B is a N X 1 vector containing the magnetic field values at al1 observation points,

and B, contains the same information for an infinite conducting volume. The

components of the matrix A are:

where d is the surface area, nj is the outward normal vector of unit length, and Cj is the

position of the centroid of the triangle A). [25].

Fig. 3.3. WPW Preexcitation Sites. This cross-section of the heart at the AV ring shows possible accessory pathway (AP) locations caused by WPW syndrome. The labels RV and LV refer to the right and left ventricles. PA is the pulmonary artery. The site labels enclosed in boxes refer to the dipole locations used in this study. Table 3.1 lists the full names of these seven sites.

For 14 curent dipoles onented perpendicular to the AV ring, the potential

distributions on the body surface and the magnetic fields outside the body were

deterrnined. Figure 3.3 shows the position of these sites on the AV ring. Table 3.1 lists

the full names of the sites. A subset of seven preexcitation sites was used for this study.

The potential was found at 352 body surface sites on the antenor and postenor torso. The

Table 3.1.

magnetic field was calculated at 225 sites on a 28 cm x 28 cm grid located 2 cm in fiont

of the torso. The finite homogeneous volume conductor that simulated the human torso

was constructed from data of a healthy male during expiration [263.

Site RPL RPP LPP LPL LAL LAP RAP

3.3.2 Simulating Sensor Misplacement

As explained in Chapter 2 of this thesis, magnetic measurements at the Dalhousie

Biomagnetism Lab are made on a 7 x 8 grid in a plane in fiont of the subject's torso.

Measurement site D3 is the intersection of the subject's 4h intercostal space and the

sternum (see Figure 2.3). Since the patient platform can be moved with a Iateral accuracy

of - 1 mm, the measurement grid can be considered to be well defined. However, due to

differing body sizes and types, it is difficult to localize the 4" intercostal space to within

more than 1 cm. Thus, the majority of reproducibility error is due to inaccurate

localization of the reference site. For magnetic data, then, we simulate lateral (right/lefi

and up/down) and normal displacement of the entire measurement grid with respect to the

torso.

The Dalhousie BSP acquisition system consists of 1 17 electrodes on 18 strips, and

three limb leads for averaging purposes. Al1 strips are attached vertically on the subject's

Anatomicai Position Right Posterolateral Right Posterior Paraseptal Left Posterior Paraseptal Lcft Posterolatcral Left Anterolateral Left Antenor Paraseptal Right Anterior Paraseptal

l e matornical positions of the seven preexcitation sites used in this smdy.

torso. The first strip, consisting of 8 electrodes, is placed along the sternum on the front

of the tono, with the fifth electrode fiom the top located at the 4" intercostal space.

Another 8-electrode strip is attached along the posterior midline, with the fiflh electrode

at the same height as its counterpart on the sternum. These two strips will be referred to

as the azimuthal stnps. Four-electrode strips are attached along the lefl and nght

midaxillary lines. The second electrode fiom the top is vertically aligned with the 41h

intercostal space. Al1 other electrode strips are spaced equally relative to these reference

positions [4]. Figure 3.4 is a diagram of the electrode locations on the torso.

Three types of systematic sensor misplacements are likely to occur for this 120-

lead system. In each case, a particular electrode, or pair of electrode strips, is misplaced,

and al1 other s t ips are arranged as though the error were unnoticed. The azimuthal

electrode strips may be placed out of alignrnent with the sternum. This can cause a

systematic azimuthal misplacement of al1 strips. Also, the midaxillary lines may be

positioned inaccurately. An error here will cause the rest of the strips to be positioned

more densely on either the fiont or back of the torso. Finally, a vertical inaccuracy may

occur when finding the subject's 4'h intercostal space. This reference site is used to

vertically align al1 electrode strips. Thus, al1 electrodes may be vertically misplaced.

Random misplacement of individual electrodes will also be considered. For this case, the

constraint that electrodes are arranged on vertical strips is removed.

Fig. 3.4. Electrode locations on the torso. Leads 1, 2, and 3 are limb leads. As a reference, electrode 33 is located at the 4' intercostai space on the sternum [4].

We modeled azimutfial, midaxillary , vertical, and random electrode

misplacements, and lateral and normal magnetic sensor grid misplacements on the

simulated data for al1 seven dipole positions. Dipole positions were recovered for lateral

magnetometer grid misplacements up to 20 mm. Magnetometer grid shifts normal to the

tono were also simulated up to 10 mm. For BSP data, electrode shifts up to 30 mm were

simulated in al1 cases.

3.3.3 The Inverse Solution

The inverse solution of the magnetic and surface potential data at a given time

instance recovers the position and strength of the cunent dipole that would cause such a

field. This is an iterative process. An initial estimate of the dipole strength and location

is made. The magnetic field (or body surface potential) map resulting fkom the dipole is

found by fonvard solution. The computed map is compared to the measured map. The

dipole is shifted and scaled to reduce the difference between the two maps, and a new

map is generated. The Levenberg-Marquardt nonlinear least squares optimization

algorithm guides the changes in the dipole properties. This process is repeated until the

change in the dipole position and strength for each iteration falls below a user-defined

threshold. The recovered dipole position and strength are the result of the 1 s t iteration.

3.4. RESULTS

3.4.1. Body Surface Potential Maps: Vertical Electrode Misplacemen t

Figure 3.5 shows the displacement between the recovered position of the

unshified and shifted dipole for a vertical misplacement of al1 electrodes. Positive values

on the x-axis represent upward electrode shifis. We also calculated the vertical dipole

displacement, by itself, due to the electrode misplacement. These results are shown in

Figure 3.6 for the seven sites. For al1 sites, the relationship between electrode shifi and

displacement of the recovered dipole is essentially linear. The dipole displacement is

always less than the electrode misplacement, but some dipole locations are somewhat

more sensitive to the misplacement than others. It is apparent fiom the similarity of

Figures 3.5 and 3.6 that, for a homogeneous torso model, vertical electrode misplacement

leads to a similar vertical shift in the recovered dipole position.

The above result is as expected. The electrode shift occurs over a small region of

the entire torso (< 30 mm), so the torso can be approximated as a long cylinder, with the

electrode strips attached to the outer surface. Vertical misplacement of the electrodes is

approximated as an upward shift of al1 strips on the cylinder surface. This causes a

sirnilar shifi in the recovered dipole position since the torso is syrnrnetric about the

vertical axis of the cylinder.

+ LAt * U P O LPL A LPP O w x RPL V RPP

- -3 O -20 - 10 O 10 20

Vertical Shift [mm] Fig. 3.5. A plot of the displacement of a current dipole from its unshifted recovered position due to systematic vertical misplacement of electrode strips. The displacement was found for seven original dipole sites. Positive x-axis values represent upward shifts.

+ UL * LN= O P L A LPP O RAP x RPL V RPP

Vertical Shift [mm] Fig. 3.6. A plot of the vertical displacement of a current dipole fkom its unshifted recovered position due to systematic vertical misplacement of electrode strips. The displacement was found for seven original diboie sites. Positive x-axis values rebresent ubward shifts.

3.4.2. Body Surface Potential Maps: Azimuthal and Midaxillary Electrode

Mis placement

Figures 3.7 and 3.8 show the displacement of the shifted recovered dipole from

the unshified position due to azimuthal and midaxillary electrode misplacement. f ositive

values on the x-axis represent electrode shifts to the subject's left and fiont for azimuthal

and midaxillary misplacements, respectively. In both cases, there is a noticeable

difference between the displacement profiles for v m n g preexcitation sites. Sites on the

left side of the AV ring are more stable under conditions of azimuthal sensor

misplacement than those on the right. Furthemore, for both left and right sites, the

posterior sites are more stable than the anterior sites. In the case of midaxillary

misplacement, antenor sites are more stable than posterior sites, and only the RPL site is

displaced by a distance greater than the actual electrode misplacement. The displacement

of this dipole due to 30 mm of forward midaxillary electrode misplacement is 38 m.

However, on average, we see that the displacernent of the recovered dipoles is less than

the azimuthal and midaxillary sensor misplacement.

3.4.3. Body Surface Potential Maps: Random Electrode Misplacement

Recovered dipole displacements due to random inaccuracies in electrode

placement are shown in Figure 3.9. For most preexcitation sites, the effect of random

electrode misplacement is minimal. Random electrode inaccwacies of 30 mm cause

about a 5 mm displacement of the recovered dipole for al1 locations except for the RAP

and RPL sites. For these two sites, the displacement due to 30 mm of m d o m electrode

misplacement reaches 12 mm and 25 mm, respectively.

+ LAL m LAP O LPL A LPf' O RAP x RPL V RPP

-3 O -20 -10 O 10 20 30 Azimuthal Shifi [mm]

Fig. 3.7. A plot of the displacement of a current dipole fiom its unshifted recovered position due to systematic azimuthal misplacement

Midaxillary Shi ft [mm]

of electrode strips. The displacement was f o n d for seven original dipole sites. Positive x-axis values represent l e h a r d shifts fiom the

Fig. 3.8. A plot of the displacement of a cument dipole fiom its unshifted recovered position due to systematic midaxillary misplacement of electrode strips. The displacement was f o n d for seven original dipole sites. Positive x-axis values represent forward shifts.

50

n

40; U

CI

E 0

E

- I 1 I

- - - - - + LAL - m LAP - -

O LPL -

- - - -

A LPP - 3 - O R A I - - x RPL x : - o RPP - X - - - X - X - - . I

Ci C -

0 - C -

g 20: .II

n - -

-3 O -20 -1 O O 10 20 30

+ LA1 m LAP O LPL A LPP O RAP x RPL V RPP

Random ShiR [mm] Fig. 3.9. A plot of the displacement of a current dipole from its unshifted recovered position due ta random misplacement of electrodes. The displacement was found for seven original dipole sites.

A cornparison of Figure 3.9 with previous results suggests that random electrode

misplacements generate smaller inaccuracies in the Iocalization of cardiac sources than

systematic electrode strip misplacements (i.e. azimuthal, midaxillary, or vertical). Since

the inaccuracies are not systematic, the error due to one electrode misplacement can

cancel out the error due to another. Thus, the recovered dipole position is more stable

under random electrode misplacement than in the systematic case.

3.4.4. Magnetic Field Maps: Lateral Grid Misplacement

Figures 3.10 and 3.1 1 show the displacement of the recovered dipole fiom the

unshifted position due to lateral misplacements of the magnetometer grid with respect to

the torso. Up/down (vertical) and rightneft (horizontal) inaccuracies are displayed.

Positive values on the x-axis represent grid shifts upward and to the subject's leA for

vertical and horizontal misplacements, respectively.

The results for vertical magnetometer grid misplacement are very similar to those

for vertical electrode shifts. The vertical dipole displacement is plotted against the

vertical grid shift in Figure 3.12. Based on these results, vertical magnetometer grid

shifts generate an equivalent vertical shifi in the recovered dipole position. On average,

this shifi is larger than that observed for recovered dipoles under vertical electrode

misplacement. Thus, MCG data is more sensitive to vertical sensor misplacement than

ECG data

Under horizontal grid misplacement, the displacement of the recovered dipole

fiom its original position has a more complicated behaviour. Shifiing the grid to the

Vertical Grid Shifi [mm] Fig. 3.10. A plot of the displacement of a current dipole from its unshifted recovered position due to systematic vertical misplacement of the magnetic xwor grid. The displacement was found for seven

Horizontal Grid Shift [mm] Fig. 3.1 1. A plot of the displacement of a current dipole fiom its unshifted recovered position due to systematic lateral misplacement of the magnetic sensor grid. The displacement was f o n d for seven original dipole sites. Positive x-axis values represent 1eftwa.d shifts.

Vertical Grid Shift [mm]

Fig. 3.12. A plot of the vertical displacement of a current dipole from its unshified recovered position due to systematic vertical misplacement of the magnetic sensor grid. The displacement was found for seven original dipole sites. Positive x-axis values represent

+ UC * w O P L A LPP - - . O RAP x RPL V RPP

Horizontal Grid Shift [mm] Fig. 3.13. A plot of the lateral displacement of a current dipole fiom its unshifted recovered position due to systematic lateral misplacement of the magnetic sensor grid. The displacernent was f o n d for seven orieinal diwle sites. Positive x-axis values renresent leftward shifts.

subject's right causes an approximately equivalent shifi in the recovered dipole position,

for most sites. Dipoles located on the lefi side of the AV ring undergo a slightly larger

displacement than do those on the right side. However, when the grid is shifted to the

lefi, the displacement of postenor sites becomes erratic.

Figure 3.13 is a graph of the horizontal displacement of the recovered dipole due

to the horizontal grid misplacement. The erratic behaviour is most clear here. Under

increasing leftward grid shift, the displacement of the recovered dipole for the RPP site

increases, as expected. However, after about 10 mm of grid shift, the recovered dipole

moves back towards the unshifted position. The same behaviour is noted for the dipole at

the RAP site. in this case, the ''tumaround" occurs after 16 mm. The recovered position

for a dipole at the LPP site undergoes two tumarounds dunng the leftward grid shift. The

recovered dipole begins to retuxn to the unshifted position after 5 mm of lehard grid

shift. AAer the grid is shiAed by 9 mm the dipole moves away fiom the unshifted

position again.

3.4.5. Magnetic Field Maps: Normal Grid Misplacement

The magnetometer grid is usually located less than 2 cm fiom the fiont of the

subject's torso. However, the shape of the torso and chest movement due to the subject's

breathing cm cause inaccuracies when determining this distance. The displacement of

the recovered dipole fiom the unshified position for normal grid misplacements of up to

10 mm is shown in Figure 3.14. Positive values on the x-axis represent grid shifts away

fiom the torso.

Fig. 3.14. A plot of the displacement of a current dipole fiom its unshifted recovered position due to systematic normal misplacement of the magnetic sensor grid. The displacement was fond for seven original dipole sites. Positive x-axis values represent shifts away fiom

Normal Grid Shift [mm] Fig. 3.15. A plot of the normal displacement of a current dipole fiom its unshifted recovered position due to systematic normal misplacement of the magnetic sensor grid. The displacement was found for seven original dipole sites. Positive x-axis values represent shifts away fiom the torso.

To determine the direction of the dipole displacement, we plotted the

displacernent of the recovered dipole in the normal direction only against the normal grid

shif? (see Figure 3.15). From this graph, it is apparent that normal grid misplacement

leads to an equivalent noxmal displacement of the recovered dipole fiom its unshifted

recovered position.

3.4 CONCLUSIONS

For the simulated misplacement of electrode strips during acquisition of BSP data

in a homogeneous torso, we find that the preexcitation sites on the right side of the AV

ring are more sensitive to electrode placement than those sites on the lefi. In the case of

magnetic sensor grid misplacement, we find the reverse to be true. For lateral grid shifts,

sites on the lefi side of the AV ring are slightly more sensitive to sensor misplacement.

This effect is most noticeable under azimuthal and midaxillary electrode shifts and lateral

rnagnetic sensor grid shift.

The dispiacement of the recovered dipoles for posterior preexcitation sites

behaves enatically when the magnetometer grid is shifted to the subject's lefi. This

probably occurs since the grid is centered slightly to the left of the sternum and the

inverse solution becomes less predictable as colurnns of rnagnetic sensors begin to move

off of the torso.

Larger inaccuracies in localizing cardiac sources are generated by systematic

misplacement of electrode strips than random misplacements of individual sensors. In

the case of random electrode misplacements. the displacement of the recovered dipole

caused by one sensor can be cancelied out by the irnproper placement of another sensor.

There is an averaging effect that leads to a reduced inaccuracy in the recovered dipole

position. This does not occur during systematic sensor misplacement.

For vertical and normal magnetic sensor grid misplacement, the displacement of

the recovered dipole is essentially the same as the displacement of the sensor grid for al1

seven preexcitation sites. Vertical electrode misplacement generates a slightly less than

equivalent shift in the recovered dipole position. These effects are due to the geometry of

the homogeneous torso. in the case of vertical sensor misplacement, the torso c m be

treated as a long homogeneous cylinder. Thus, vertically shifting the electrodes or

magnetic grid leads to an equivalent shift in the recovered dipole position. For normal

magnetic sensor grid misplacement, we can approximate the torso as a semi-infinite

space since the distance between the grid and the torso surface is much smaller than the

dimensions of the torso. Under these conditions, it would be expected that a normal grid

shift would lead to an equivalent dipole shift.

Magnetic sensor and electrode placement accuracy have similar effects on the

accuracy of localizing a current dipole in a homogeneous torso. For most systematic grid

shifts, to image these cardiac sources to an accuracy of 5 mm, grid placement of similar

accuracy is required.

Chapter 4

Determining the P-Wave Duration

4.1. INTRODUCTION

Atrial fibrillation is a cardiac arrhythmia that can lead to cardiac death. The

duration of the P-wave in the averaged ECG complex can be used as a predictor for the

development of atrial fibrillation after cardiac surgery 133-341. We wish to detemine

how reliably the P-wave duration can be measured fiom averaged MCG data in

anticipation of measunng this parameter on a similar group of patients. We acquired

MCG data on a group of I I normal subjects. The onset, offset, and dwation of the P-

wave were detennined in each case using three different methods. We compared the

results fiom each method to estimate the accuracy with which the P-wave duration can be

found.

4.2. ATFUAL FIBRILLATION

4.2.1. Ovewiew

Fibrillation in the atrial myocardium, or atrial fibrillation (AF), is the most

common type of cardiac arrhyihmia. The basic mechanism of AF is an irregular

propagation of the activation wave in the atria. This can lead to irregular cardiac rates of

up to 600 beats per minute. The incidence of AF increases with age, but it is usually

associated with an underlying disease. Two types of AF exist. Paroxysmal AF occurs on

a recurrent basis. The subject will suffer an attack, followed by a retum to sinus rhythm.

The reversion may occur spontaneously, or due to treatment. Chronic AF is a more

persistent fom. The onset of AF is oAen associated with cardiac stress, such as surgery

or other forms of intense cardiac nerve stimulation [27].

The Framingham study observed 5209 men and women starting in 1948. It was

found that patients with AF had a mortality rate 1.8 times higher than patients without

AF. 60% and 45% of the men and women of this study, respectively, who were

diagnosed with AF died within 10 yean of the diagnosis. In general, those suffering

from AF have twice the mortality rate of age- and sex-matched normal subjects. The

incidence of stroke is five times greater for AF patients [28].

Acute attacks of AF are usually treated with drugs designed to impair AV

conduction, such as digitalis or beta blocking agents, or by the application of a DC

discharge synchronized with the R-wave. The latter technique is referred to as DC

cardiovenion. The long-tem treatrnent for AF usually involves the use of antiarrhythrnic

Inhomogeneous Mvocardium

Onset Promess

End

Fibrillation

Fig. 4.1. Atrial Fibrillation. A mode1 of the propagation of activation waves in homogeneous and inhomogeneous myocardia. Dark circles represent refractory cells. White circles are responsive cells. Re- excitation in the inhomogeneous myocardium causes AF to occur [3 11.

drugs [29]. In cases where these techniques are ineffective, RF catheter ablation of the

AV node, and the insertion of a rate-responsive pacemaker may be necessary [30].

4.2.2. Basic Physiological Synopsis

The major precipitator for AF is inhomogeniety in the atrial myocardiurn. Figure

4.1 compares the propagation of an activation wave in both homogeneous and

inhomogeneous myocardia. Before being activated, al1 muscle fibres in the

homogeneous myocardium are in a normal, conducting state. Since al1 the muscle fibres

conduct at the same rate, the activation wave travels forward through the myocardium at

a steady speed and with a stable front. Once a ce11 is activated, it becomes refractory,

meaning it no longer conducts for a few hundred milliseconds. This prevents the

activation wave fkom traveling backwards. Refractory cells return to the functional state

before the next excitation wave arrives.

In the inhomogeneous myocardium, some cells are in the refractory state before

the activation wave reaches them. The wave propagates around these cells, and the wave

front becomes non-unifom. Srnaller excitation wavelets fom and excite the refractory

cells once they becorne responsive to activation. The late excitation of some cells leads

to the propagation of more wavelets. These waveiets re-excite cells that have already

conducted the initial activation wave and have retumed to a normal state. Thus, the

irregularity spreads over the entire myocardium.

4.2.3. Diagnosing and Predicting Atrial Fibrillation

The most noticeable indicators for AF are irregular and high heart rates. Atrial

contraction can occur at rates of 350-600 beats per minute. A cardiogram containing

irregularities in the P-wave and perpetual arrhythmia between QRS complexes is also a

good indicator of paroxysmal or chronic AF.

Certain factors affecting the homogeneity of the atria can lead to the development

of M. An increase in atrial chamber size, or decrease in the conduction rate in the

myocardiurn, increases the time over which an activation wave must propagate. Longer

periods of propagation lead to a higher probability of inhomogeneities forming in the

wave. Shortened intervals between activation waves can cause new waves to begin to

propagate before cells have returned to a functional, conducting state. Also, the

simultaneous formation of excitation waves at more than one point in the atria can lead to

non-uniforrn propagation [3 11.

Although it is important to diagnose AF, it is even more desirable to predict

which patients are in danger of developing AF. Particularly, predicting the development

of AF after cardiac surgery is of great import. Buxton and Josephson [32] suggested that

delayed atrial conduction might be an indicator of sensitivity to AF after cardiac surgery.

Delayed &rial conduction generates a lengthening of the P-wave duration. In a study of

99 subjects undergoing coronary artery bypass surgery, those with average P-wave

durations over 110 ms developed AF with a sensitivity of 66% and a specificity of 70%.

Tamis and Steinberg [33] visually determined P-wave durations fiom averaged ECG data

acquired fiom a group of 130 subjects scheduled to undergo cardiac surgery. The

average P-wave duration for the 33 subjects who developed AF was 152 t 18 ms. The

average dwation for those who did not develop AF was 139 + 17 ms. In this case, P-

wave durations of over 140 ms accurately predicted the development of AF with a

sensitivity of 77% and a specificity of 55%.

Our aim is to determine which method for finding the P-wave duration is most

reliable. Results obtained by a simple graphical method (referred to as the Eyeball

method) will be compared with durations determined by two automated methods. Both

MCG and ECG data will be consideted.

4.3. METHOD

Determining the P-wave duration from MCG data is a three-step process. MCG

data is acquired at 56 sites and averaged as descnbed previously. A MFM generated at

the P-wave maximum determines the site at which the largest P-wave occurred. The

averaged complex at this site is then isolated, and the onset and offset of the P-wave are

found. The P-wave duration is also determined for averaged ECG data. in this case, the

limb lead data with the largest P-wave is chosen.

4.3.1. Data Acquisition and Averaging

MCG and limb lead data acquisition and averaging were performed according to

the rnethodology in Chapter 2 of this thesis. Measurements were made using the old

cryogenic dewar. Thus, the intrinsic noise was - 15 / î /&Iz. Al1 data were acquired

between January 27", 1999 and February 18", 1999.

The study group includes 11 normal healthy volunteer subjects. The average age

of the group is 19 (STD = 2) years, with a range of 18-24 years. There are 8 female and 3

male subjects. No group mernbers have been diagnosed with cardiac conditions.

4.3.2. Findiog the Optimal P-Wave

The header of each averaged cardiac complex stored in a database file created by

the averaging process, DALECG, contains the times at which al1 cardiac set points occur

for the given complex. Slope and amplitude critena defined by DALECG determine

these times for the ECG reference leads. The values are used to align magnetic

complexes for averaging. However, these times c m also be used to generate MFMs at

any time instance relative to a wave onset, offset, or maximum.

Figure 4.2 shows the MFM generated for a normal subject d u h g the maximum

of the P-wave. Plus (+) signs mark the actual measurement sites. The measurement site

at which the strongest P-wave occurs is the P-wave peak signal site. This site is labeled

by a pIus sign enclosed in a box. Since the averaged complex at this site has the largest

P-wave signal-to-noise ratio, we use this site to find the P-wave onset, offset, and

duration.

For Iimb lead data, the Iargest P-waves are recorded between the nght and lefi

anns (lead one), and the lefi leg and right m (lead three). The P-waves at both of these

leads are sirnilar in magnitude. Thus, for convenience, we wili always determine the

ECG P-wave durations using lead one.

O 5 10 15 20 25 x [cm1

Fig. 4.2. Finding the P-Wave Peak Signal Site. The magnetic field map at the P-wave maximum for subject 206. Plus signs (+) represent actual measurement sites. The Peak Signal Site is labeled by a plus sign enclosed in a box. Contour labels represent magnetic field magnitude in picoTeslas

Time [ms]

Fig. 4.3. The Eyeball Method. A plot of the averaged MCG P-wave for subject 206 with the graphically determined onset and offset labeled. Straight Iines are used to estirnate the upper and lower limits of the noise before and after the wave. The onset and offset occur when the signal exceeds these limits.

4.3.3. Determining the P-Wave Duration

The P-wave onset and offset can be found by graphical analysis of an averaged

cardiac cornplex. A d e r is used to mark the upper and lower limits of the noise before

and after the P-wave. The onset and offset become the points where the signal strength

becomes greater than these limits. This will be referred to as the Eyeball method. Figure

4.3 shows the onset and offset determined by the Eyeball method for subject 206.

Although this is the preferred method for finding P-wave times, it is labour intensive and

not very efficient. Values deterrnined by this method wiIl be compared with those found

by the automated methods described below.

A first approximation to the onset of the P-wave can be found using the method of

Valverde et al. [34]. The averaged cardiac complex is low pass filtered at 40 Hz with a

4" order Butterworth Alter. The first derivative of the filtered signal is then calculated.

The nns mean and standard deviation of the noise before the P-wave onset in the filtered,

differentiated signal are found. The mean plus three times the standard deviation is set as

a threshold. The onset is approximated as the point where the filtered, differentiated

signal first exceeds the threshold. This approximate P-wave onset is used as a guide for

finding the actual onset and offset using the Soria-Olivas method, which is descnbed

below.

Soria-Olivas et al. [35] showed that high pass filtering an ECG signal at 0.8 Hz

would cause minima to occur at the offsets of the P- and T-waves. If the signal was high

pass filtered backwards, the minimum would occur at the onsets of the waves. We high

pass filtered the original averaged cardiac complex in the forward direction at 0.8 Hz,

starting at the approximate onset determined by the Valverde method. The centre of the

minimum generated at the end of the ! k t wave in the filtered signal is defined as the

offset. To find the actual onset, the original averaged cardiac complex is high pass

filtered in the backward direction, starting at the newly defined offset. The centre of the

minimum at the beginning of the fint wave is the P-wave onset. A plot of the unfiltered,

and forward and backward high p a s filtered averaged complexes is shown in Figure 4.4.

The P-wave onset and offset detennined by the above method are labeled with vertical

The P-wave onset and offset determined by the above method are tabeled with vertical

lines. This rnethod for determining the P-wave onset and offset will be referred to as the

S-O method.

As mentioned above, wave onsets, offsets, and maxima for the averaged ECG

complexes are also determined by DALECG during averaging. These times are recorded

with the averaged complex as header information. DALECG c m also calculate these

times for MCG data. These MCG and ECG P-wave times are found using slope and

amplitude criteria defined in the averaging algorithm. These results will be referred to as

the DALECG P-wave times.

For this study, ECG and MCG P-wave times found by the Eyeball method will be

used as reference times. We will compare the S-O and DALECG P-wave times to the

reference times. The difference between the P-wave tirnes determined by any two

methods will be referred to as the discrepancy. The standard deviations of the

discrepancy between automated and reference P-wave times for al1 subjects wilt be used

to measure the reliability of each rnethod as compared to the Eyeball method.

4.4. RESULTS

Figure 4.4 shows the averaged MCG complex at the P-wave peak signal site for a

subject used in this study. Onset and offset times determined by the S-O method are

labeled with vertical lines. For quantitative analysis, we compared the times found by the

S-O method and DALECG with the results of the Eyeball method. Tables 4.1 and 4.3

contain the P-wave onsets, offsets, and durations determined with each of the three

methods for MCG and ECG complexes, respectively. Table 4.2 lists the discrepancies

Fig. 4.4. Determining P-wave Duration. The unfiltered, and forward and backward high pass filtered averaged complexes at the P-wave peak signal site for subject 206 are shown fiom top to bottom. The P- wave onset and offset are labeled with vertical lines.

for MCG P-wave times detennined with each of the two automated methods, as

compared to the results of the Eyeball method. Table 4.4 lists the discrepancies for

averaged ECG data. The cross sign (X) indicates that the respective P-wave tirne could

not be detennined.

For MCG data, the standard deviations of the discrepancies between the S-O

method and the Eyeball method for P-wave onsets, offsets, and durations are 13.3 ms,

16.3 ms, and 20.0 ms, respectively. The standard deviations of the discrepancies between

the DALECG and Eyeball times are 25.7 ms, 50.1 ms, and 32.0 ms. For al1 P-wave

times, the standard deviations of the discrepancies are lower for the S-O method than

DALECG. The reverse is true for averaged ECG data. The standard deviations of the

Table 4.1. P-wave omets, offsets, and durations determined for averaged MCG complexes. Times are determined by DALECG, and the Eyeball and Soria-Olivas methods. The cross sign (X) indicates that the respective P-wave time could not be detennined.

1 f

Su bject 202 203 204 205 206 207 208 209 210 21 1 212

Average STD

the Eyeball method. The P-wave times are determined with DALECG and the Soria-Olivas method for averaged MCG data. The cross sign (X) indicates that the respective P-wave time could not be detennined.

Table 4.2. The MCG P-Wave times relative to the times deterrnined with

Onset [ms] Duration [ms] DALECG

1 -56 -2

Offset [ms] DALECG

41 48 36 -36 35 X 29 16 46 -3 1 X

20.4 32.0

S - 0 8 16 4

DALECG 42 -8 34 -75 38 X 5

-3 8 20 -94 X

S-O - 8 32 14 21 -19 25 11

-12 20 15

-29 6.4 20.0

S-O O

48 18 17 2

26 7 -5 13 -6 -4

-39 3 X

-24 -54 -26 -63 X

-28.9 25.7

-4 21 1 -4 7 -7 -2 1 25 4.2 13.3

-8.4 50.1

10.5 16.3

Table 4.3. P-wave onsets, offsets, and durations determined for averaged ECG complexes. Times are detemined by DALECG, and the Eyeball and Soria-Olivas methods.

Subject 202 203 204 205 206 207 208 209 210 21 1 212

Average STD

Table 4.4. The ECG P-Wave times relative to the times detennined with the Eyeball method. The P-wave times are detennined with DALECG and the Soria-Olivas method for averaged ECG data.

Omet [rns] DALECG

1 -2 - 1 -3 -4 -3 11 -5 O - 1 -6

-1.2 4.6

OCfset [ms] S-O - 1 -56 -16 -23 14

-27 -2 -4 -2 9 -5

-1 0.3 19.5

Duration [ms] DALECG

-4 - 7 6 -8 5 -5 -3 14 - 5 - 1 O -10 -2.5 7.6

DALECG -5 -5 7 -5 9 -2 - 14 19 -5 -9 -4

-1.3 9.3

S-O O

-1 1 4

-25 3

-11 -5 4 -6 -4 -9

-5.5 8.6

S-O 1

45 20 -2 -1 1 16 - 3 8 -4 - 13 -4 4.8 16.8

L I I I 1 1 I

Average 1 03% 1 3.72 1 0.11 1 138.67 1 2.48 1 16.91 1 0.18 1 65.17 Table 4.5. Signal and Noise Characteristics of Averaged Cardiac Data.

r

Subject

202 203 204 205 206 207

The P- and R-wave amplitudes, the ratios of these two aÏnplitudes, and the P-wave signal-to-noise ratio for both ECG and MCG data are shown. The recording bandwidths for MCG and ECG data are 250 Hz and 100 Hz, respectively.

discrepancies between the S-O and Eyeball times for ECG data are 19.5 ms, 8.6 ms, and

16.8 ms. For DALECG, the standard deviations are 4.6 ms, 7.6 ms, and 9.3 ms.

The P-wave onsets found by DALECG for MCG data are, on average, 29 ms

higher (STD = 26 ms) than the reference onsets. This suggests that DALECG

systematically overestimates MCG P-wave onsets. This overestimation is not observed

for the ECG onsets detennined by DALECG. Also, the standard deviation of the

discrepancy for MCG offsets deterrnined by DALECG is much larger than the

discrepancies for MCG onsets and durations. The standard deviations are about qua1 for

al1 ECG P-wave times detennined by DALECG.

ECG [mVJ P-Max

0.58 0.32 0.4 1 0.2 1 0.52

MCG [PT] P-Max

2.5 1 3.43 3 .O6 2.47

P-wave S/N

21 1.90 1 15.75 149.49 76.92 189.40

R-Max

8 -99 24.3 1 15.43 13.43 ,

P/R

0.28 O. 14 0.20 0.18

R - M a x r

4.03 4.86 2.89 3.12 2.50

129.54 3 -36

P-wave Sm

66.05 90.26 80.53 65 .O0

P/R

0.14 0.07 O. 14 0.07 0.2 1

0.36 I 5.22 0.07 2.05 i 9.31 1 0.22 1 53.95 25.05 1 0.13 88.42

These results suggest that the S-O method is more reliable than DALECG for

determining the P-wave onset, offset, and duration for MCG data acquired with the

current measurement system. DALECG is more reliable for finding the P-wave times for

the ECG data. To determine the cause of this disparity between the ECG and MCG

results, we looked at the P-wave signal-to-noise characteristics for our system. The P-

and R-wave amplitudes, the ratio of these two amplitudes, and the P-wave signal-to-noise

ratios for averaged MCG and ECG data are shown in Table 4.5. The recording

bandwidths for the MCG and ECG data are 250 Hz and 100 Hz, respectively. The noise

after averaging for MCG data is 38 /T, as detemined in Chapter 1 of this thesis. By the

sarne method, the noise after averaging fer ECG data is 2.8 pV.

The average P-wave signal-to-noise ratio for ECG data is a factor of two larger

than the average signal-to-noise ratio for MCG data. Cornparisons of Tables 4.2,4.4, and

4.5 show that the P-wave times determined by DALECG are more reliable for the data

with the larger signal-to-noise ratio. However, the reliability of the tirnes found by the

S-O rnethod are relatively unaffected by the increase in the signal-to-noise ratio. This

occurs since DALECG relies on dope and amplitude criteria to find the onsets and

offsets. The reliability of results found using these types of criteria increases as the

signal-to-noise ratio gets larger. Since the S-O method uses filtering and minima

localisation, rather than signal amplitude criteria, the results are less sensitive to changes

in the signal-to-noise ratio.

Standard Deviations

Eyebaii Method VS.

DALECG Eyeball Method

vs. Soria-Olivas Method

Table 4.6. Sumrnary of the most important results fiom Chapter 4. The standard deviations cf the discrepancies between P-wave times determined by automated methods and the Eyeball method are shown for MCG and ECG data. The MCG and ECG recording bandwidths and P-wave signal- to-noise ratios are dso shown.

-

4.5. CONCLUSION

Previously, it has been shown that the cardiac P-wave duration is a predictor for

the development of postoperative atrial fibrillation in patients undergoing cardiac

surgery. In a study of 11 normal subjects, we found that the Soria-Olivas method is more

retiable than DALECG for determining P-wave durations for averaged MCG complexes

acquired using our current measuring system. DALECG is more reliable than the Soria-

Olivas method for determining P-wave dwations for averaged ECG complexes acquired

using our current measuring system. This disparity occws since the signal-to-noise ratio

for our current system is a factor of two larger for ECG data than MCG data. Thus, the

Soria-Olivas method is more reliable than DALECG for determining P-wave durations in

P-Wave Signal-to-Noise

Ratio

noisy cardiac data. These results are summarized in Table 4.6.

Recording Bandwidth 1 0.05 - 250 Hz

MCG

0.025 - 125 Hz

ECG

65

Onset I d

4.6

19.5

Duration lml

32.0

20.0

Onset [ml

25.7

133 1

139

Offset [msl

50.1

163

Offset [ml

7.6

8.6

Duration ml

93

16.8

Appendix A

Acquisition Protocol

1. Before Subject Arrives - ensure liquid Heliwn dewar is more than 20% fiil1 - stock 8 ECG electrodes - check for clean hospital gowns - rneaçure voltage on ADC battery pack - check noise s p e c t m for peaks

2. Subject Arrives - subject signs Consent Form - fil1 in subject attributes on Data Acquisition Form - allow subject to change - check noise spectnun for peaks (save data as scan #O)

3. Subject Preparation - attach ECG electrodes to wrists and d i e s - subject iays on bed under dewar - locate subject's 41h intercostal space on sternum (site D3) - reposition subject such that site D3 is directly under the SQUID - attach ECG cables and start HP1 505A electrocardiograph - acquire 15 seconds of test data (do not save)

4. MCG Acquisition - reposition subject such that site Al is directly under the SQUTD - acquire 30 seconds of MCG and ECG data - repeat these two steps for sites A2, A3, . . ., A7, B7, . . . , B 1, C 1, . . . , . . . , H 1 - data that include excessive noise or other artifacts can be re-acquired

immediately and overwritten to the cornputer

5. Post-Acquisition - turn off ECG machine - detach ECG cables and electrodes - discomect ADC battery pack - allow subject tochange - check noise spectnim again for peaks (save data as scan #57) - thank subject - general clean-up

Appendix B

Summary of Data Analysis Algorithms

The following is an outline of prograrns used to process and average MCG data

acquired using the CTF acquisition software. The PV-Wave programs are al1 compiled

on the UNIX workstation. The Fortran programs are cornpiled on the VAXNMS

system.

The PV-Wave procedure ADAPTFILT.PR0 is an adaptive filter for eliminating

signals at a specific fiequency. The user chooses the filter frequency and the sample rate,

and inputs a data set. The adaptive filter algorithm described in Section 1.2.2 of this

thesis is then implemented on the data. The algorithm outputs the filtered data set.

CTF2ASC.PRO is a batch procedure for PV-Wave that converts one set of raw

MCG and limb lead data nom long binary to ASCII fonnat. The four input files are

filtered, converted to ASCII, and saved to new files. ADAPTFILT.PR0 is called by this

procedure to eliminate 60, 120, and 180 Hz noise. MFMCTF2ASC.PRO is a larger batch

procedure that converts al1 56 sets of raw data. Al1 input files must be contained in the

same directory.

ASC2EPS.EXE is a Fortran executabie file. This program convem four ASCII

data files into one interleaved EPS data file. The data is scaled to 410 counWmV and 41

counts/pT for limb lead and magnetic data respectively. The sample rate and other

header information are defined in the actual Fortran program code. To adjust this, the

code must be changed and the program must be re-compiled.

DALECG2000.EXE is the current version of the averaging algorithm used in Our

lab. This version reads the EPS file header to detemine the sample rate before

averaging. It works correctly for data sarnpled at rates up to 2000 Hz. The output fiom

this program is called a SWAP (.SWP) file, and is only used as a temporary storage site.

SWPCHG2000XXE is the current version of the database organization algorithm.

The user inputs the SWAP file name, subject number, and site number. The averaged

complex and relevant header information (as outlined in Table 2.1) for one acquisition

site are added to a database file named FOR02*.DAT. A number between O and 3, which

represents MCG or limb lead data, replaces the açterisk.

READ020-56.PRO is a PV-Wave program that reads a 56-site database file and

exports the averaged data for analysis. An averaged FORO2O.DAT file is its input. It

outputs a nurnber of three-dimensional arrays. These arrays include al1 averaged

complexes, and relevant header information.

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