electronic detection and diagnosis of health and illness of premature infants

Electronic Detection and Diagnosis of Health and Illness of Premature Infants

Co-WorkersQuants

StatisticiansDoug Lake

George Stukenborg

Chemical EngineersJohn HudsonMatthew Clark

Craig RusinLauren Guin

PhysicistsAbigail Flower

Hoshik LeeJohn Delos

Medical Folks

Randall MoormanPamela Griffin

John KattwinkelAlix Paget-BrownBrooke Vergales

Kelley ZagolsAndy Bowman

Terri Smoot

Supported by NIH.

Overview

Medical Issues:

Apnea of Prematurity

Neonatal Sepsis

Observations:

Electronic Monitoring

of Heart, Respiration

What can we Quants contribute?

Statistical measures

Signal Analysis

Pattern recognition methods

Dynamical theories

Outline

Sepsismedical issueheart rate monitoringstatistical measuresrandomized clinical trialpattern recognitionphysiological modeling

Apneamedical issuechest impedance – cardiac artifactfiltering, signal analysisexamplesfuture work

Conclusion

Sepsis

the presence of bacteria, virus, fungus, or other organism in the blood or other tissues and the toxins associated with the invasion.

Of 4 million births each year, 56,000 are VLBW (<1.5 Kg). For them the risk of sepsis is high (25-40%)

Significant mortality and morbidity (doubled risk of death in VLBW infants; increased length of NICU stay; high cost). The diagnosis of neonatal sepsis is difficult, with a high rate of false negatives

Physicians administer antibiotics early and often.

Can heart rate monitoring give early warning of sepsis?

(The invading organisms, or the immune response, may affect the pacemaking system.)

Randall Moorman

Does heart rate give warning of illness?Plot (Time Between Beats) vs (Beat Number)

ms

beat number beat number

ms

Does heart rate give warning of illness?Plot (Time Between Beats) vs (Beat Number)

Repeated decelerations

vs.

Reduced variabilitypathologic

pathologic

normal

pathologic

expand

Statistical Measures of RR Interval Data

Standard Deviation and Sample Entropy: Variability in the signal.

Sample Asymmetry: Prevalence of decelerations over accelerations implies a skew, or asymmetry, in the data which we can detect statistically.

Find correlation of those measures with illness, and report correlation in terms of “fold increase of risk of sepsis”:

Take any random moment.Examine a window of 24 hours (plus 18 hours or minus 6 hours) around that moment.On average, 1.8% of the infants in our first study had a sepsis event within that 24 hour window.

If we report a “five-fold increase of risk of sepsis”, it means that about 10% of the infants showing those heart rate characteristics had a sepsis event within that 24 hour window.

Medical Predictive Science Corporation has developed and is marketing a system for

Heart Rate Characteristics monitoring in the NICU.

A computer beside each NICU bed continuously collects ECG data, extracts times of R peaks,

tracks RR intervals, and provides the following Heart Rate Observation (HeRO).

This system is installed in several NICUs in the US, and a large randomized clinical trial has just been

completed.

HRC rises before illness score

Conclusion 1:

New quantitative analysis of noninvasive, electronically-measured heart rate characteristics –

standard deviation, asymmetry, and sample entropy –

provides an early noninvasive warning of sepsis events.

Randomized Clinical Trial

8 Hospitals UVa, Wake Forest, UAl (birmingham), Vanderbilt, Umiami, Greenville SC, Palmer (Orlando) Penn State

SampleDisplay HeRO Score

(but do not tell clinicians what to do)

ControlSave HeRO data

but do not display it

152 deaths/1489, 10.2%

133 deaths/757, 17.6%

2989 VLBW (<1.5 Kg)

1513 ELBW (<1 Kg)

Randomized Clinical Trial

8 Hospitals UVa, Wake Forest, UAl (birmingham), Vanderbilt, Umiami, Greenville SC, Palmer (Orlando) Penn State

SampleDisplay HeRO Score

(but do not tell clinicians what to do)

122 deaths/1500, 8.1%

100 deaths/756, 13.2%

ControlSave HeRO data

but do not display it

152 deaths/1489, 10.2%

133 deaths/757, 17.6%

2989 VLBW

Δ = 2.1% absolute, 22% relative

1513 ELBW

Δ = 4.4% absolute, 33% relative

Conclusion 2:

New quantitative analysis of noninvasive, electronically-measured heart rate characteristics –

standard deviation, asymmetry, and sample entropy –

saves lives.

New Question:

Would direct measures of decelerations provide additional information?

Pattern Recognition

for detecting decelerations

Abigail Flower

)()()( 0 nnnanS

The idea is to detect discrete decelerations in a signal containing noise.Assume we have a deceleration of shape, . n

We can, then, represent our signal, , as the sum of these discretedecelerations and “Gaussian white noise”

)(nS)(n

= +

Create a Mother WaveletExamine representative decelerations from one baby:

- symmetry- steeper slope closer to center of waveform.

D

nn

nnnn

2

3

0

20

0

||1

)(exp)(

D

Sweep this wavelet through the signal, one width at a time. Calculate for each translation, and width w*a

0n

Scale = 30 beats

Scale = 50 beats

},...,,{ *,30

*2,30

*1,30

*30 Naaaa

},...,,{ *,50

*2,50

*1,50

*50 Naaaa

2

*

S

a

Result: “Storms” of Decelerations are

Highly Predictive of Sepsis

0

5

10

15

0 1 2 3 4 5 6 7 8

102

103

104

105

Fold-increase in sepsis w

ithin 24 hoursN

umbe

r of

409

6-be

at r

ecor

ds

Number of decelerations

-3 -2 -1 0 1 2 3

0.0

0.1

0.2

0.3

0.4

0

1

2

3decelerations

ln (

SD

) /

dece

ls p

er 3

0 m

in

Time (days; 0 = sepsis)

variability

HRC indexStatistical measures

Conclusion 2

Counting and measuring decelerations gives a second method for early

warning of sepsis.

Also an important finding was that HR decelerations are surprisingly similar in infants.

DiscoveryWe found that, within extended clusters of decelerations, there were sometimes shorter intervals of time, lasting up to several

hours, in which the decelerations showed remarkable periodicity.

For six infants in our study population we identified deceleration clusters in which periodicity was maintained for at least ten minutes.

In these periodic bursts of decelerations, the time to the next deceleration was about 15 sec.

New Question:

What Causes Periodic Decelerations?

Can we develop a model capable of producing decelerations like those observed in neonatal

HR records? Such models can guide future observations or experiments on animals.

Partial Answers

A. A mathematical model with minimal physiological assumptions: a noisy Hopf bifurcation

B. Physiologically-based modelsThe pacemaker?The baroreflex loop?Periodic apneas?

HR jumps from small fluctuations [“steady-state”] to periodic decelerations [above-threshold oscillations].

Hopf Bifurcations the most common way that a rest point changes to a cycle

Hard (Subcritical) Hopf bifurcation:

Small excerpt of real data and simulation.

large periodic decels

small periodic fluctuationsdata

simulation

noise-induced subthreshold oscillations

Hard Hopf oscillations

ConclusionOutput of a Noisy Hopf Bifurcation Model

ResemblesObserved Periodic Decelerations

Thus we have a mathematical model with minimal physiological assumptions: a noisy Hopf bifurcation.

Is there a physiologically-based model?

1. Pacemaker cells

Sino-Atrial node cells are the natural pacemaker of the heart. Can they go into “FM” mode with period ~15 seconds?

2. Baroreflex loop

The feedback loop connecting blood pressure and heart rate can go into oscillation with period ~14 seconds in infants. Does this resemble the observations?

3. Periodic apneas

Apneas produce decelerations of the heart. Sometimes they occur periodically, and the period is commonly ~15 seconds.

BP

Mayer (low freq) arrhythmia

RR

BP

HR

Hypothesis: Noisy Precursors are Familiar Mayer Waves.

There MUST be a nearby threshold.

Decelerations are result of a parameter going above the bifurcation.

A model of the baroreflex loop J. Ottesen, Roskilde University, Denmark

Rate of change of = Rate in from heart – blood volume in arteries Rate out through capillaries to veins

dV / dt = heart rate x blood volume per beat - [P(arteries) - P(veins) ] / [Resistance]

P(arteries) = V(arteries) / [compliance = ]

Similar formulas for veins

Rate of change of = Neg(t) a negative term caused by high BPheart rate + Pos(t -τ ) a positive term caused by low BP

which however is delayed

/V P

A problem in nonlinear dynamics

Do these differential equations have a bifurcation that causes heart rate and blood pressure to oscillate?



YES!!

What is the period ??



YES!!


Depending on parameters, can be ~ 15 seconds.

Do they resemble the observed oscillations?



YES!!




Nope.

Do measurements of HR and BP in infants show the large correlated oscillations?



YES!!




Nope.

Do measurements of HR and BP in infants show the large correlated oscillations?

Nope.

The Future: Incoming Data Streams

1. Electronic diagnosis of infectious disease? Can we identify invading organisms by HR monitoring?

Preliminary evidence:

Reduced variability Gram-positive bacteria (e.g. Streptococci, Staphylococci) vancomycin

Clusters of decels Gram-negative bacteria (e.g. E. coli, Pseudomonas) gentamicin, cefotaxime

If this preliminary result holds up, we have the first example of continuous, noninvasive, purely electronic monitoring that gives early warning of infectious disease and also gives partial diagnosis, thereby identifying the recommended therapy.

(A medical tricorder)

The Future: Experiments proposed or discussed

2. Quadriplegics are subject to infections that they do not feel. Can heart rate monitoring provide early warning?

3. Can large decelerations be induced in animals?

Models suggest that increasing the time delay in the baroreflex loop pushes the system above bifurcation.

Can this be done by chemical means (beta blockers)?Or electrical means (cut the sympathetic nerve

and use an electrical time delay circuit)?

What happens when corresponding experiments are done on newborn or premature animals?

Conclusions

1. Decelerations are correlated with illness, and can be used as a new, noninvasive monitor for illness of premature infants.

2. Periodic decelerations resemble the output of a model based on Hopf bifurcation theory.

3. Study of periodic apneas is the next step.

4. The search for corresponding phenomena in animals has clinical, developmental, and dynamical significance.

5. When we quants work together with physicians, and overcome the knowledge barrier and communication barriers between us, important and unexpected advances in health care can be made.

Apnea of Prematurity (AOP)Apnea (cessation of breathing) is very common for premature infants.-> half of babies whose birth weight < 1500 g (VLBW)-Almost all babies whose birth weight < 1000 g (ELBW)

Definition of (clinical) AOP

Cessation of breathing > 20s OR

Cessation of breathing > 10s + Bradycardia (Heart Rate < 100 bpm) or O2 Desaturation (SpO2 < 80%)

Definition of (clinical) AOP

Cessation of breathing > 20s OR

Cessation of breathing > 10s + Bradycardia (Heart Rate < 100 bpm) or O2 Desaturation (SpO2 < 80%)

VLBW : Very Low Birth Weight, < 1500gELBW : Extremely Low Birth Weight, < 1000g

and

Three types of apnea are common in premature infants.1)Obstructive apnea : a blockage of the airway, typically accompanied by struggling or thrashing movements of the infant. 2)Central apnea : cessation of respiratory drive, and the infant makes no effort to breathe. 3)Mixed apneas : obstructive central.

Central apnea is taken to indicate immaturity of control of respiration, and discharge from UVa NICU is delayed until apneas have been absent for 8 days.

Apnea may be cause or an effect of many other clinical illnesses including sepsis or abnormal neurologic development. It is a serious clinical event which needs immediate medical attention.

However, the current generation of apnea monitors is unsatisfactory.

Data Collection

- Since January 2009, we have collected all waveform and vital sign data from the bedside electronic monitors in the UVa NICU. - Waveforms: ECG, Chest Impedance, Pulse Ox- Vital signs: Heart & respiration rates, oxygen saturation of

hemoglobin- ~ 1 TB / Baby-Year

- About 1300 admissions for over two years, more than 300 VLBW infants.

- Monitor alarms are stored (e.g. brady, desat, and apnea etc..). - Clinical data relevant to respiratory support are entered manually into

an connected clinical database, including presence and type of ventilatory support such as mechanical ventilation.

- Administration of medication (e.g. caffeine)

Chest Impedance Measurement

• Basic impedance (static) : several hundred ohms: muscles, tissue, blood + electrode-skin transitions, and wires.

• Respiration : ~ 2 ohm - Air has poor conductivity

- More air in the lung, higher impedance• Heart activity : ~ 0.5 ohm - Blood is more conductive than air

- Pumping blood out of thorax, less impedance

• Easiest way to monitor the respiration of baby • Impedance between two electrodes placed at the chest.• Use two electrodes already in use for the measurement of the EKG signal, but use a frequency (52 kHz) far outside the EKG signal.

ECG & Chest Impedance

Heart Rate

ECG

Chest Impedance

Respiration Rate

200

100

200

100

0

ECG & Chest Impedance during Apnea event

Heart Rate

ECG

Chest Impedance

Respiration Rate

200

100

200

100

0

How do we remove the cardiac artifact from chest impedance signal?

Filtering, signal analysis, pattern recognition

Hoshik Lee

Goal : Filter the heart signal from chest impedance.

Simple Fourier filter fails. Heart beat band is too broad. Especially, the heart beat slows during apnea.

Use the Heart as the Clock !

contract/stretch

RR intervals are evenly spaced.

Fourier Transform of chest impedance

Heart Clock1RR

Cardiac artifact in chest impedance

1RR

Fourier Transform of CI Using Heart Clock

Fourier Transform of CI



Cardiac Artifact



Cardiac Artifact

Breathing



Cardiac Artifact

Breathing

Slow change : movement or unknown but not breathing

HR

EKG

Chest Impedance

Chest impedanceCardiac Artifact Removed

Filtered Chest Impedance

`

There remain some small fluctuations in filtered CI. Compute the residual variance of the signal on 2 sec intervals, spaced by ¼ sec, and get a probability of apnea.

Probability of Apnea

‘Energy’ in fluctuations (variance)

Thresholding function looks like the Fermi distribution function. We obtain fitting function with two parameters.

Probability of Apnea

‘Energy’ in fluctuations (variance)

0

1( )

1 exp[ ( )]P E

E E

Summary: from Chest impedance to probability of apnea

CI

Remove cardiac artifact

Remove slow variations and renormalize

Compute 2s variance

P(apnea)

Philips Research North America 07/13/2011

Examples


1

0

200

100

100

0

100

50

Very Long Apnea Undetected by monitor

We detected 782 apneas > 60 s in two years data


Periodic Breathing = Periodic Apneas

200

100

100

0

100

50

1

0

Current Studies

What fraction of apneas are recorded by nurses? (~1/3)How does the apnea rate change with age? ^Does caffeine reduce apnea? (?)Do transfusions reduce apnea? (Yes)Can we get early warning of serious apneas? (Maybe)What is the significance of long apneas?Are short but periodic apneas benign?

Future Work

•Convert to real-time monitor- The system is set up for retrospective work : collecting statistics of apnea events and correlation with other clinical events.•Combined with (wearable) Automatic Stimulation system. - e.g. vibrate shirt, mattress and so on. •Improve sepsis detection? (HeRO monitoring)

Add-on system which provides a new way to analyze data that had previously been discarded

Conclusions

1. Decelerations are correlated with illness, and can be used as a new, noninvasive monitor for illness of premature infants.

2. Periodic decelerations resemble the output of a model based on Hopf bifurcation theory.

3. Study of periodic apneas is the next step.

4. The search for corresponding phenomena in animals has clinical, developmental, and dynamical significance.

5. When we quants work together with physicians, and overcome the knowledge barrier and communication barriers between us, important and unexpected advances in health care can be made.

ASSUME: the heart rate is governed by some set of

feedback loops that can be modeled by some big set of differential equations

dx/dt = F(x;p)x = (x1,x2,…) (dynamical variables)p = (p1,p2,…) (parameters)

Example: x1 = heart rate, x2 = blood pressure, x3 = …

2. The equations have a “rest point” at which all variables are constant (including HR)

F(xrest(p);p) = 0.

Move origin of coordinates so rest point = 0.

3. F(x;p) can be expanded in a Taylor series in x’s.

F(x;p) = Mx + xNx +….

Analyze first at linear level.

Almost all matrices can be transformed to diagonal form

1

1

y Cx

CMC

dy / dt y yCNC y ...

At linear level

1 1 1

2 2 2

dy / dt y

dy / dt y

Equations are real, so eigenvalues are real or come in complex conjugate pairs

4. All except two eigenvalues have negative real parts. Two eigenvalues are complex, and have real part near zero.

i

* i

is near zero, and as parameters p change, it can pass through zero.

THEN:

[Center Manifold Theorem]

In the many-dimensional y-space, there is a smooth two-dimensional surface called the center manifold, in which all the interesting behavior occurs:

a. If the system starts on that surface, it stays on that surface

b. If it starts elsewhere, it decays to that surface

c. The equations in that surface have a Taylor expansion

AND FURTHERMORE:

[Normal Form Theorem]

There exists a smooth change of coordinatessuch that the diff eqs can be put into a “Normal Form”; using polar coords in the center manifold, that form is:

3 5dr / dt r ar br ...

d / dt ...

and this is easy to analyze !!!!

YOU can easily show:

As increases through zero, The stable rest point goes unstable, and

EITHER A stable cycle appears

OR

An unstable cycle disappears

That is a Hopf bifurcation.

Summary: In the many-dimensional space, there is a smooth two-dimensional attractor in which all the interesting behavior occurs, and there exists a smooth change of coordinates such that the diff eqs can be put into a “Normal Form”:

As increases through zero, the stable rest point goes unstable, and either a stable cycle appears,or an unstable cycle disappears

That is a Hopf bifurcation.

3 5dr / dt r ar br ...

d / dt ...

In the new variables the motion is a sinusoidal oscillation,

We can connect to time between beats RR(t)by assuming RR = RR(u(t)) = RR(r cos ).

But we know the shape of the decelerations, and we can represent that shape by a Fourier cosine series.

Then the sinusoidal oscillation is converted into a sequence of decelerations.

( t)

u( t) rcos( t) ,v( t) rsin( t)

Heart rate

Arterial BP

Venous BP

Small time delay

These oscillations are “noisy precursors” :With no noise the system goes to steady state

Subthreshold oscillations arise because of noise and a nearby bifurcation

Figure 5. This figure shows a 60-s trace of R-R interval and systolic blood pressure values of the 3-min segment, as presented in Figure 1. It shows the temporal relationship between systolic blood pressure (SBP) and R-R interval fluctuations. SBP (left y axis) and R-R interval (right y axis) values are shown as a function of time. High frequent fluctuations with small amplitude variation, related to respiratory activity, can be observed. In addition, approximately six low frequent fluctuations with a variation of 3 mmHg per cycle in this 60-s trace can be observed, corresponding to an oscillation frequency of approximately 0.1 Hz. Each rise in SBP (indicated by the first arrow) is followed by an increase in R-R interval (indicated by the second arrow), with a time delay varying from 1.5 to 4 s (periods between arrows). The averaged time delay from this 60-s trace (2.7 s) is close to the calculated transfer phase.

High freq fluctuations: RR ~ opposite to BP respiratory arrythmiaLow freq fluctuations: BP leads RR Mayer waves

Peter Andriessen

Above the bifurcation (longer time delay)

the system oscillates even with no noise.

Above bif, the oscillations are large. The shape is (maybe) a passable imitation of periodic decels seen in infants. Period 10 s for adults.

RR

time

Comparison of model (based on adult data) with observations:

There is a bifurcation which produces large regular oscillations in HR. Increase in delay time of sympathetic action gives the bif. Shape of decels – could be better.

Liapunov exponent is too small. (Slow approach to steady oscillations.)

It’s a soft Hopf bif instead of a hard Hopf bif. (I think by playing with parameters we might convert it to hard Hopf.)

Continuing research:

1.More fun with baroreflex models2.Begin study of respiration models

electronic detection and diagnosis of health and illness of premature infants

Documents

warning of illness

early warning of sepsis

sample asymmetry

measures of decelerations

electronic monitoringof

sample entropy

storms of decelerations

prevalence of decelerations