Electronic Detection and Diagnosis of Health and Illness of Premature Infants
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Electronic Detection and Diagnosis of Health and Illness of Premature Infants. Co-Workers. Medical Folks Randall Moorman Pamela Griffin John Kattwinkel Alix Paget-Brown Brooke Vergales Kelley Zagols Andy Bowman Terri Smoot. Quants Statisticians Doug Lake George Stukenborg

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Co workers
Co-Workers Premature Infants

Medical Folks

Randall Moorman

Pamela Griffin

John Kattwinkel

Alix Paget-Brown

Brooke Vergales

Kelley Zagols

Andy Bowman

Terri Smoot

Quants

Statisticians

Doug Lake

George Stukenborg

Chemical Engineers

John Hudson

Matthew Clark

Craig Rusin

Lauren Guin

Physicists

Abigail Flower

Hoshik Lee

John Delos

Supported by NIH.


Overview
Overview Premature Infants

What can we Quants contribute?

Statistical measures

Signal Analysis

Pattern recognition methods

Dynamical theories

Medical Issues:

Apnea of Prematurity

Neonatal Sepsis

Observations:

Electronic Monitoring

of Heart, Respiration


Outline Premature Infants

Sepsis

medical issue

heart rate monitoring

statistical measures

randomized clinical trial

pattern recognition

physiological modeling

Apnea

medical issue

chest impedance – cardiac artifact

filtering, signal analysis

examples

future work

Conclusion


Sepsis Premature Infants

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

ms

ms

beat number

beat number


Does heart rate give warning of illness plot time between beats vs beat number1
Does heart rate give warning of illness? Premature InfantsPlot (Time Between Beats) vs (Beat Number)

Reduced variability

Repeated decelerations

pathologic

normal

vs.

pathologic

pathologic

expand


Statistical measures of rr interval data
Statistical Measures Premature Infantsof 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 correlation in terms of “fold increase of risk of sepsis”:

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 correlation in terms of “fold increase of risk of sepsis”:


Conclusion 1: correlation in terms of “fold increase of risk of sepsis”:

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 correlation in terms of “fold increase of risk of sepsis”:

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

Control

Save HeRO data

but do not display it

152 deaths/1489, 10.2%

133 deaths/757, 17.6%

Sample

Display HeRO Score

(but do not tell clinicians

what to do)

2989 VLBW (<1.5 Kg)

1513 ELBW (<1 Kg)


Randomized Clinical Trial correlation in terms of “fold increase of risk of sepsis”:

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

Control

Save HeRO data

but do not display it

152 deaths/1489, 10.2%

133 deaths/757, 17.6%

Sample

Display HeRO Score

(but do not tell clinicians

what to do)

122 deaths/1500, 8.1%

100 deaths/756, 13.2%

2989 VLBW

Δ = 2.1% absolute, 22% relative

1513 ELBW

Δ = 4.4% absolute, 33% relative


Conclusion 2: correlation in terms of “fold increase of risk of sepsis”:

New quantitative analysis of noninvasive, electronically-measured heart rate characteristics – standard deviation, asymmetry, and sample entropy –

saves lives.


New Question: correlation in terms of “fold increase of risk of sepsis”:

Would direct measures of decelerations

provide additional information?


Pattern recognition for detecting decelerations abigail flower

Pattern Recognition correlation in terms of “fold increase of risk of sepsis”:

for detecting decelerations

Abigail Flower


Result storms of decelerations are highly predictive of sepsis
Result: correlation in terms of “fold increase of risk of sepsis”: “Storms” of Decelerations are Highly Predictive of Sepsis


Statistical measures correlation in terms of “fold increase of risk of sepsis”:


Conclusion 2 correlation in terms of “fold increase of risk of sepsis”:

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.


Discovery
Discovery correlation in terms of “fold increase of risk of sepsis”:

We 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 correlation in terms of “fold increase of risk of sepsis”: 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: correlation in terms of “fold increase of risk of sepsis”: 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 correlation in terms of “fold increase of risk of sepsis”:

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

  • Physiologically-based models

    The pacemaker?

    The baroreflex loop?

    Periodic apneas?


HR jumps from small fluctuations [“steady-state”] correlation in terms of “fold increase of risk of sepsis”: to periodic decelerations [above-threshold oscillations].


Hopf bifurcations the most common way that a rest point changes to a cycle
Hopf Bifurcations correlation in terms of “fold increase of risk of sepsis”: the most common way that a rest point changes to a cycle

Hard (Subcritical) Hopf bifurcation:


Small excerpt of real data and simulation
Small excerpt of real data and simulation. correlation in terms of “fold increase of risk of sepsis”:

large periodic decels

data

small periodic fluctuations

simulation

Hard Hopf oscillations

noise-induced subthreshold oscillations


Conclusion correlation in terms of “fold increase of risk of sepsis”:

Output of a Noisy Hopf Bifurcation Model

Resembles

Observed Periodic Decelerations

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

Is there a physiologically-based model?


  • Pacemaker cells correlation in terms of “fold increase of risk of sepsis”:

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

  • 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?

  • Periodic apneas

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


Mayer (low freq) arrhythmia correlation in terms of “fold increase of risk of sepsis”:

RR

HR

BP

BP

  • Oscillation with period ~14 s in infants.

  • Feedback loop with time delay.

  • Many models connect Mayer waves with a Hopf bifurcation.

  • However, measurements indicate that under physiological conditions in adults, the feedback loop is stable (no oscillations)

  • Two studies (DeBoer et al. and Chapuis et al.) propose that observed Mayer waves are noise-driven subthreshold oscillations


  • Hypothesis noisy precursors are familiar mayer waves
    Hypothesis: Noisy Precursors are correlation in terms of “fold increase of risk of sepsis”: Familiar Mayer Waves.

    There MUST be a nearby threshold.

    Decelerations are result of a parameter

    going above the bifurcation.


    Rate of change of = correlation in terms of “fold increase of risk of sepsis”: 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 BP

    heart rate + Pos(t -τ ) a positive term caused by low BP

    which however is delayed

    A model of the baroreflex loop

    J. Ottesen, Roskilde University, Denmark


    A problem in nonlinear dynamics correlation in terms of “fold increase of risk of sepsis”:

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


    A problem in nonlinear dynamics correlation in terms of “fold increase of risk of sepsis”:

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

    YES!!

    What is the period ??


    A problem in nonlinear dynamics correlation in terms of “fold increase of risk of sepsis”:

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

    YES!!

    What is the period ??

    Depending on parameters, can be ~ 15 seconds.

    Do they resemble the observed oscillations?


    A problem in nonlinear dynamics correlation in terms of “fold increase of risk of sepsis”:

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

    YES!!

    What is the period ??

    Depending on parameters, can be ~ 15 seconds.

    Do they resemble the observed oscillations?

    Nope.

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


    A problem in nonlinear dynamics correlation in terms of “fold increase of risk of sepsis”:

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

    YES!!

    What is the period ??

    Depending on parameters, can be ~ 15 seconds.

    Do they resemble the observed oscillations?

    Nope.

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

    Nope.


    • The Future: Incoming Data Streams correlation in terms of “fold increase of risk of sepsis”:

    • 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 correlation in terms of “fold increase of risk of sepsis”:

    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 correlation in terms of “fold increase of risk of sepsis”:

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

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

    • Study of periodic apneas is the next step.

    • 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 of Prematurity (AOP) correlation in terms of “fold increase of risk of sepsis”:

    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%)

    and

    VLBW : Very Low Birth Weight, < 1500g

    ELBW : Extremely Low Birth Weight, < 1000g


    Three types of apnea are common in premature correlation in terms of “fold increase of risk of sepsis”: infants.

    • Obstructive apnea : a blockage of the airway, typically accompanied by struggling or thrashing movements of the infant.

    • Central apnea : cessation of respiratory drive, and the infant makes no effort to breathe.

    • Mixed apneas : obstructive central.

      Central apnea is taken to indicate immaturity of control of respiration, and discharge from UVaNICU 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
    Data Collection correlation in terms of “fold increase of risk of sepsis”:

    • 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
    Chest Impedance Measurement correlation in terms of “fold increase of risk of sepsis”:

    • 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.

    • 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


    200 correlation in terms of “fold increase of risk of sepsis”:

    100

    200

    100

    0

    Heart Rate

    ECG

    Chest Impedance

    Respiration Rate

    ECG & Chest Impedance


    200 correlation in terms of “fold increase of risk of sepsis”:

    100

    200

    100

    0

    Heart Rate

    ECG

    Chest Impedance

    Respiration Rate

    ECG & Chest Impedance during Apnea event


    200 correlation in terms of “fold increase of risk of sepsis”:

    100

    200

    100

    0

    Heart Rate

    ECG

    Chest Impedance

    Respiration Rate

    ECG & Chest Impedance during Apnea event


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

    Filtering, signal analysis, pattern recognition

    Hoshik Lee


  • Filter the heart signal from chest impedance.

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

  • Fourier Transform of

    chest impedance

    Use the Heart as the Clock !

    Cardiac artifact in chest impedance

    contract/stretch

    RR intervals are evenly spaced.

    Heart Clock

    1RR

    1RR


  • Filter the heart signal from chest impedance.

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

  • Fourier Transform of

    chest impedance

    Use the Heart as the Clock !

    Cardiac artifact in chest impedance

    contract/stretch

    RR intervals are evenly spaced.

    Heart Clock

    1RR

    1RR


  • Filter the heart signal from chest impedance.

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

  • Fourier Transform of

    chest impedance

    Use the Heart as the Clock !

    Cardiac artifact in chest impedance

    contract/stretch

    RR intervals are evenly spaced.

    Heart Clock

    1RR

    1RR


    Fourier Transform of CI signal?

    Fourier Transform of CI

    Using Heart Clock


    Fourier Transform of CI signal?

    Fourier Transform of CI

    Using Heart Clock

    Cardiac Artifact


    Fourier Transform of CI signal?

    Fourier Transform of CI

    Using Heart Clock

    Cardiac Artifact

    Breathing


    Fourier Transform of CI signal?

    Fourier Transform of CI

    Using Heart Clock

    Cardiac Artifact

    Slow change : movement or unknown but not breathing

    Breathing


    ` signal?

    HR

    EKG

    Chest Impedance

    Chest impedance

    Cardiac 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 signal?

    ‘Energy’ in fluctuations (variance)


    Probability of Apnea signal?

    ‘Energy’ in fluctuations (variance)

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


    Summary: from Chest impedance to probability of apnea signal?

    CI

    Remove cardiac artifact

    Remove slow variations and renormalize

    Compute

    2s variance

    P(apnea)


    Examples signal?

    Philips Research North America 07/13/2011


    Philips Research North America 07/13/2011 signal?

    200

    100

    100

    1

    100

    0

    50

    0


    Very long apnea undetected by monitor
    Very Long Apnea Undetected by monitor signal?

    We detected 782 apneas > 60 s in two years data


    Periodic Breathing = Periodic Apneas signal?

    1

    0

    Philips Research North America 07/13/2011

    200

    100

    100

    100

    50

    0


    • Current Studies signal?

    • 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 signal?

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

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

    • Study of periodic apneas is the next step.

    • 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: signal?

    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 = …


    • The equations have a “rest point” at which signal?

    • all variables are constant (including HR)

    • F(xrest(p);p) = 0.

    • Move origin of coordinates so rest point = 0.

    • 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 signal?

    At linear level

    Equations are real, so eigenvalues are real

    or come in complex conjugate pairs


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

    THEN:


    • [Center Manifold Theorem] signal?

    • In the many-dimensional y-space, there is a

    • smooth two-dimensional surface called the

    • center manifold, in which all the interesting

    • behavior occurs:

    • If the system starts on that surface, it

    • stays on that surface

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

    • surface

    • The equations in that surface have a

    • Taylor expansion


    AND FURTHERMORE: signal?

    [Normal Form Theorem]

    There exists a smooth change of coordinates

    such that the diff eqs can be put into a “Normal

    Form”; using polar coords in the center

    manifold, that form is:

    and this is easy to analyze !!!!


    YOU signal? 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.


    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.


    Small time delay

    Heart rate

    Arterial BP

    Venous BP


    These oscillations are “noisy precursors” :

    With no noise the system goes to steady state

    Subthreshold oscillations arise because of noise and a nearby bifurcation


    Peter Andriessen

    High freq fluctuations: RR ~ opposite to BP respiratory arrythmia

    Low freq fluctuations: BP leads RR Mayer waves


    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: a passable imitation of periodic decels seen in infants. Period 10 s for adults.

    • 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 Hopfbif instead of a hard Hopfbif.

    • (I think by playing with parameters we might convert it to hard Hopf.)

    • Continuing research:

    • More fun with baroreflex models

    • Begin study of respiration models


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