Heart-rate Variability

Christoph Guger, 10.02.2004

Heart-rate Variability (HRV)

1965 Hon & Lee – Fetal distress alterations in interbeat intervals before heart rate (HR) changed

1980 HRV is strong and independent predictor for mortality following myocardial infarct

Signs of increased sympathetic and reduced vagal activity has triggered the development of quantitiative markers

Content:

Standardization and definition of terms

Standard methods for measurement

Physiological and pathophysiological correlation

HRV in high-altitude medicine

QRS Complex Detection

Continuous ECG - > find the QRS complexes in the time series.

This signal is non-uniformly sampled and can be shown as Tachogram (the RR intervals are plotted over the number of RR intervals).

HRV Time Domain Measures

In a continuous ECG recording the normal-to-normal (NN or RR) intervals can be determined (interval from one QRS complex to the next).

• Simple Time Domain Measures

• Complex Time Domain Measuresa) derived from direct measurement of NN intervalsb) derived from difference between adjacent NN intervals

This measure can be used to investigate variations of the heart rate secondary to tilt, Vasalva manoeuvre, to describe the difference between night and day,…

Simple Time Domain Measures

Mean RR interval: e.g. 1000 ms or 256 samples

Mean heart rate: e.g. 60000 ms / 1000 ms = 60 bpm

Minimum RR interval: e.g. 700 ms

Maximum RR interval: e.g. 1200 ms

Difference between RRmin and RRmax: 500ms

Difference between night and day,...

From entire recording or from smaller segments -> allows comparision e.g. rest, sleep,...

Complex Time Domain Measures

SDNN ... Standard deviation of RR intervals

SDNN = SQRT ( VAR(RR) )

Reflects the cyclic components responsible for variability in the period of recording

Normally over 24 hours -> describes short term high frequency variations and low frequency components !

Recording Period ↓ -> SDNN estimates shorter -> shorter cycles

Total variance increases with recording length -> SDNN depends on recording length

Inappropriate to compare SDNN measures of different recording length

Standardize recording length: 5 min or 24 h

Segmented Measures

SDANN … standard deviation of the average NN interval calculated over short periods e.g. 5 minutes

µ1 µ2 µ3 µ4 ... µN

SDANN = STD ( [µ1, µ2, µ3, µ4,... µN])Estimates changes in heart rate due to cycles longer than 5 minutes (for 5 min intervals)

SDNNindex … mean of 5 min standard deviation of NN intervals calculated over 24 h

STD1 STD2 STD3 STD4 ... STDN

SDNNindex = MEAN ( [STD1, STD2, STD3, STD4,... STDN])

Measures variability due to cycles shorter than 5 min (for 5 min intervals)

Interval Differences

RMSSD … square root of the mean squared difference of successive NN intervals

RMSSD = SQRT ( MEAN ([∆ D12, ∆ D22,... ∆ DN2]) )

D1 D2 D3 D4 ... DN

∆ D1=|D2-D1| ∆ D2=|D3-D2| ... ∆ DN

Interval Differences

NN50 … number of intervals of successive NN intervals greater than 50 ms

NN50 = NN50+1 if ∆ Di > 50 ms

pNN50 ... NN50 / (total number of NN intervals)

D1 D2 D3 D4 ... DN

∆ D1=|D2-D1| ∆ D2=|D3-D2| ... ∆ DN

Segments must be equal !!

Geometric Measures

Histogram of RR intervals

a) Basic measurements of the geometric pattern e.g width of the histogram

b) Geometric pattern is interpolated by mathematically defined shape (e.g. exponential curve)

Most experience with bins of 8 ms (128 Hz)

Geometric Measures

HRVtriangular index … integral of density distribution / maximum of density distribution

maximum number

HRVtriangular index = Number of all NN intervals / maximum number

Dependent on the length of the bin -> quote the bin size

+ relative insensitive to the analytic quality of the series of NN intervals

- need of reasonable number of NN intervals to generate the geometric pattern (in practice 20 min to 24 h)

- not appropriate to assess short-term changes in HRV

Summary and Recommendation

Many measures correlate closely -> following measures are recommended

SDNN … estimate of overall HRV

HRVtriangular index … estimate of overall HRV

SDANN … estimate of long term components

RMSSD … estimate of short term components

• Methods for overall HRV, short and long-term can NOT replace each other

• Method should correspond to aim of study

• Do NOT compare overall measures from recordings with different duration

Easy pre-processing

Tilt Table Experiment: HRV Time Domain Measures

Rest Tilt

Tilt Table Experiment: Histogram

4th Presencia meeting - TUG

HRV Frequency Domain Measures

Spectral analysis provides information how power is distributed as function of frequency (Power Spectral Density – PSD) -> detects periodic oscillations

Non-parametric methods

Fast Fourier Transformation (FFT)

+ simple and fast

Parametric methods

+ smoother spectral components

+ easy post-processing of the spectrum

+ easy identification of the central frequency

+ accurate estimate of PSD even for small number of samples

- order of model must be chosen

HRV Frequency Domain Measures

Absolute MeasuresULF – ultra low frequency band <0.003 HzVLF – very low frequency band 0.003 – 0.04 HzLF – low frequency band 0.04 – 0.15 HzHF – high frequency band 0.15 – 0.4 Hz

The distribution of LF and HF is not fixed and varies with autonomic modulation of the heart rate

The energy in HF is vagal mediated

The energy in LF and VLF are due to both sympathetic and parasympathetic systems

Relative MeasuresThe normalization minimizes the effect of changes of Total Power (TP) on LF and HF

Lfnorm = LF / (TP-VLF), unit n.u.

Hfnorm = HF/(TP-VLF), unit n.u.

LF/HF

HRV Frequency Domain Measures

Shows balanced behavior of the 2 branches of the autonomic nervous system

Normalized units should be quoted with absolute values to describe the distribution of spectral components

Short term recording (5 minutes)

VLF is not reliable

Long term recording (24 hours)

Results include also ULF

Problem that heart period modulation responsible for LF and HF is not stationare during 24 h

Tilt Table Experiment: HRV Frequency Domain Measures

Rest Tilt

Tilt Table Experiment: HRV Map

Technical Requirements

Deviation from following requirements may lead to unreproduceable results that are difficult to interpret

Sampling rate

Low sampling rate produces jitter in R wave point -> alters spectrum

Optimal: 256 – 500 Hz or higher

If sampling rate is lower (in any case >100 Hz) -> use interpolation algorithm to refine R wave point

QRS point must be accurate

Ectopic beats, arrhythmic events, missing data and noise effects the estimation of PSD

-> use linear interpolation to reduce error

-> use error free data

Technical Requirements

Filter

Filter much lower than 200 Hz creates jitter in QRS point -> error in RR interval

Editing

Manuel editing of QRS complexes must be very accurate

Automatic filters can NOT replace manuell editing (remove intervals which differ more than 20 % from previous interval)

Algorithm Standards

QRS detection produces event series

Ri – Ri-1 versus time

-> irregularly time sampled signal: Tachogram

Spectrum is calculated of Tachogram or of interpolated DES

Recommendation: Tachogram + parametric PSD estimation

DES + non-parametric PSD estimation

or

DES + parametric PSD estimation

Sampling frequency of DES must be high enough that Nyquist frequency is not in frequency range of interest (2 Hz or 4 Hz)

Windowing function: Hanning, Hamming or triangular

Order of parametric method: 8-20

Correlation between Time and Frequency Domain

Time Domain

SDNN

HRVtriangular index

SDANN

SDNNindex

RMSSD

SDSD

NN50

pNN50

Frequency Domain

TP

TP

ULF

Mean of 5 min TP

HF

HF

HF

HF

Stability of HRV

Many studies show that short-term measures return to baseline after mild exercise,...

More powerful stimuli, e.g. maximum exercise result in longer effect

Duration of ECG recording

Duration is dictated by nature of investigation

-> standardization is needed

FDM – for short-term recording

At least 10 times the wavelength of the LF band

1 min is needed for HF

2 min are needed for LF

5 min should be used for standardization

Use Compressed Spectral Array to show variations over long time

SDNN, RMSSD can be used for short term, but FD are more easily to interpret

TDM are ideal for long-term

FDM are difficult to interpret for long-term

Physiological correlates of HRV

HR and HRV are largely under control of autonomic nervous system

Parasymphathetic influence on HR is mediated by vagus nerve (release of acetylcholine -> slow diastolic depolarization)

Sympathetic influence on HR is mediated by epinephrine and norepinephrine -> acceleration of the diastolic depolarization

Under rest: vagal tone is prominent

variations are largely dependent on vagal modulation

vagal and sympathetic activity interact

vagal impulses are brief (acetylcholine is rapidly hydrolyzed)

Vagal activity inhibits sympathetic activity and vice versa

Physiological correlates of HRV

HRV is modulated by central (vasomotor and respiratory centres)

and peripheral (oscillations in arterial pressure and respiratory movements) oscillations

HF ... Vagal activity

LF ... Marker for sympathetic modulation (when expressed in n.u.)

and marker for sympathetic and parasympathetic modulation

Discrepance: under exercise (SA ↑ ) LF is reduced !

Explanation: during SA ↑ -> Tachycardia -> TP ↓

during PA ↑ -> TP ↑

Therefore, use normalized units

SA ↑ -> TP ↓ : Lfnorm= LF/(TP↓ ) -> Lfnorm ↑

PA ↑ : Lfnorm=LF/(TP↑) -> Lfnorm ↓

Tilt Table Experiment: HRV Frequency Domain Measures

Rest Tilt

Lfnorm ↑ : tilt, standing, mental stress, moderate exercise, physical activity

Hfnorm ↑ : controlled respiration, cold stimulus in face, ...

LF/HF show balance of SA and PA

HRV changes related to pathology

Myocardial infarctionHRV ↓ after MI -> PA ↓ , SA ↑

Heart transplantation

HRV ↓ ↓ , no definite spectral components

Myocardial dysfunction

HRV ↓

Tetraplegic patients

Patients with complete high spinal cord lesion have intact efferent vagal and sympathetic neural pathways to sinus node.

But spinal sympathetic neurons have no control of baroreflex inhibitory impuls

LF ↓ ↓

4th Presencia meeting - TUG

Tests of g.MOBIlab on Dachstein

What

Effects of a fast ascent with the cable car on EEG and ECG

Measurements in the Dachstein (3000 m) region

Altitude difference of 998 m (1702 – 2700m)

Ascent with the cable car in 6 min

Drop of oxygen partial pressure has effect on human body

• 13 people performed a reaction time task

• paradigm

• simultaneous EEG and ECG recording

• 3 time points:

• in the morning in the hotel (initial state)

• during the cable car ascent

• at the mountain

• each measurement lasted 6 minutes

Measurements

g.MOBIlab

Mobile Pocket PC biosignal acquisition system

1 ECG derivation (Einthoven I)

2 EEG derivations (right and left hemisphere)

Button to log human responses

4th Presencia meeting - TUG

Content

What

Measurement in the Cable Car

Biosignal Visualization

Increase of the heart rate from 85 to 98 beats per minute during the cable car ride

ERD/ERS-Analysis: red colors - amplitude attenuationblue colors – amplitude enhancement

1700 m 2700 m

Results

Results

Parameter Valley Mountain

Heart rate [bpm] 69,72 82,47

SDNN [ms] 53,12 28,80

HRV-Index [nr] 12,99 8,04

PNN50 [%] 12,86 1,44

LF/HF [1] 1,96 3,09

Reaction time [ms] 333,44 367,28

Alpha ERD [%] 23,72 28,48

Beta ERD [%] 11,29 21,65

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