Finding Hidden Information in heart rate dynamics

Men-Tzung Lo, Ph.D,

Assistant Research Scientist,

Research-Center-Adaptive-Data-Analysis, NCU

PhysioNet

Between Genomics and Diagnostics Something is Missing…

Biomedical Informatics: Methods, Techniques and Theories

BioinformaticsImaging

InformaticsClinical

InformaticsPublic HealthInformatics

Molecular and Cellular Processes

Tissues and Organs

Populations And Society

Individuals(Patients)

?

A More Complete Picture

Biomedical Informatics: Methods, Techniques and Theories

BioinformaticsImaging

InformaticsClinical

InformaticsPublic HealthInformatics

Molecular and Cellular Processes

Tissues and Organs

Diagnostic and Functional Dynamics

Populations And Society

Individuals(Patients)

Complex SignalsInformatics

Which is the Healthy Subject?Escape statistical distinction based on conventional comparisons

Variability vs. Complexity Ary L Golberberg, “complex system”,

ProC Am Thorac soc

“Beyond ANOVA” (ANalysis Of VAriance between groups) Three Key Concepts( The purpose of complex signal informatics ):

1. Physiologic signals are the most complex in nature

2. Important basic/clinical information is “hidden” (encoded) in these fluctuations

3. Complexity degrades with pathology/aging

The often “noisy” variability actually is the signal and

represents the nonlinear signaling mechanisms

Body as servo-mechanism type machine

• Importance of corrective mechanisms to keep variables “in bounds.”

• Healthy system are self-regulated to reduce variability and maintain physiologic constancy.

Underlying notion of “constant,” “steady-state,””

conditions.

Restored steady state

…ORBaseline

Perturbation

Homeostasis Revisited

…OR

Is complex spatio-temporal variability a mechanism of object with multi-organization ?

But, What’s the healthy complexity ?

Some Characteristics of Healthy Complexity

• Nonstationarity• Statistics change with time

• Nonlinearity• Components interact in unexpected ways ( “cross-talk”, the

superposition paradigm fails )• Multiscale Organization

• Fluctuations/structures may have fractal organization• Time Irreversibility

• Non-Periodic signal

Healthy Heart Rate Dynamics

Is Your World Linear or Nonlinear?• Linear Process:

• Simple rules simple behaviors • Things add up• Proportionality of input/output• High predictability, no surprises

• Nonlinear Process:• Simple rules complex behaviors • Small changes may have huge effects• Low predictability & anomalous behaviors• Whole sum of parts

*** Danger ***

Linear Fallacy: Widely-held assumption that biologicalsystems can be largely understood by dissecting out micro-components or modules and analyzing them in isolation.

“Rube Goldberg physiology”Pencil Sharpener

“Nonlinear” Pharmacology

Treatment of Chronic Heart Failure Linear (target) approach: increase contractility*• Milrinone • Vesnarinone

Systems approach: interrupt vicious neurohormonal cycle**

• Beta-blockers

* Excess mortality** Enhanced survival

• Hypotheses :• The output of physiologic systems often

becomes more regular and predictable with disease

Loss of Complexity/Information with Disease

Multiscale Time Irreversibility (MTI):

• Time irreversibility is greatest for healthy physiologic dynamics, which have the highest adaptability

• Time irreversibility decreases with aging and disease

Healthiest vs Sickest

Congestive heart failure

Heart Rate Fluctuates Cyclically During Sleep Apnea

60 minutes of data

Complexity analysis is developed to quantize the dynamics of

biology signals

Wonderful World of “Hidden” Complexity/Nonlinear Mechanisms in Physiology

• Bifurcations (abrupt change) • Nonlinear oscillations• Time asymmetry• Deterministic chaos• Fractals

• Nonlinear waves: spirals/scrolls

• Hysteresis

Biomedical signals that have been analyzed using complex signal informatics include heart rate, nerve activity, renal flow, arterial pressure, and respiratory waveforms.

Nonlinear Mechanisms in Physiology

• Bad news: physiology is complex!• Good news: the complex behavior can arise in general

mechanisms with simple rules

Fractal: Complex tree-like object or hierarchical process, composed of sub-units (and sub-sub-units, etc) that resemble the larger scale design.

This internal look-alike property is known asself-similarity or scale-invariance.

Fractals as a Design Principle in Nature

Fractal Self-Organization:Coronary Artery Tree

Fractals and Information Transmission:Purkinje Cells in Cerebellum

Fractal: A tree-like object or process, composed ofsub-units (and sub-sub-units, etc) that resemble thelarger scale structure

Self-similarity (scale invariance), therefore, may be a property of dynamics as well as structure

Fractal dynamics has memory effect (long range correlation: adjust to fit any scales)

Are there Fractal (Scale-Free) Processes in Biology?

Why is it Physiologic to be Fractal?

• Healthy function requires capability( non-integer fractal dimension) to cope

with unpredictable environments

• Scale-free (fractal) systems generate broad range of long-range correlated responses “memory effect”

disorder

Fractal mechanism

Fractal Complexity Degrades with Disease

Single Scale Periodicity Uncorrelated Randomness

Two Patterns ofPathologic Breakdown

Healthy Dynamics: Multiscale Fractal Variability

Nature 1999; 399:461Phys Rev Lett 2002; 89 : 068102

Healthy dynamicspoised between too much order and total randomness.

But randomness isnot chaos!

Transformation

Seems irregular ?

ApEn

Biological systems need to operate across multiple spatial and temporal scales; and hence their complexity is also multiscaled

DFA & multi-fractal

Scale dependent fractal (Detrend-Fluctuation-Analysis method, C.-k.Peng,1995, chaos )

• The average root-mean-square fluctuation functions F(n) is obtained after integrating and detrending the data (to exclude environmental stimuli)

Slope =0.5 un-correlation

Anti-correlation

Fractal

Harmonic or periodic

Color-coded wavelet analysis (Plamen Ch Ivanov, nature ,1999 )

singularity

Diagnosis To prognosis

Normal

abnormal

Traditional signal Complexity analysis can

help specify the abnormal. But, what is feature for the critical

case?

Dynamics of heart rate (HR)

Sympathetic stimulationSympathetic stimulation

Parasympathetic stimulationParasympathetic stimulation

HR(bpm)

Heart rate

Heart rate

Quantification of HR dynamics

• Time domain measurement

• Standard deviation of normal-to-normal beat intervals (SDNN)

• Power spectrum analysis• High frequency (HF)

• Low frequency (LF)

• Very low frequency (VLF)

• LF/HF

Task Force of the European Society of Cardiology and the North American of Pacing and Electrophysiology ,

Circulation 93:1043-65,1996

• Fourier analysis is a valid technique for investigation of the oscillatory components of circulatory and respiratory systems.

(Attinger, et al., Biophys. J. 6:291-304, 1966.)

Applicability of fourier transform In analysis of biological systems

Transformation

Quantification of HR dynamics

• Time domain measurement

• Standard deviation of normal-to-normal beat intervals (SDNN)

• Power spectrum analysis• High frequency (HF)

• Low frequency (LF)

• Very low frequency (VLF)

Task Force of the European Society of Cardiology and the North American of Pacing and Electrophysiology ,

Circulation 93:1043-65,1996

Clinical applications

Task Force of the European Society of Cardiology and the North American of Pacing and Electrophysiology,

Circulation 93:1043-65,1996

Clinical applications (cont’)

Task Force of the European Society of Cardiology and the North American of Pacing and Electrophysiology ,

Circulation 93:1043-65,1996

Modulation of heartbeats

Sympathetic nerve Parasympathetic nerve

baroreceptor chemoreceptor

Stretch receptor

Analysis of HR dynamics by nonlinear methods

• Dynamic measures of HRV may uncover abnormalities that are not easily detectable with traditional time and frequency domain measures.

Laitio, et al., Anesthesiology 93:69-80,2000

Huikuri, et al. Circulation 101:47–53, 2000

Applications of nonlinear analysis in patients with myocardial infarction

Application of nonlinear analysis in heart failure patients

Makikallio, et al. Am J Cardiol 87:178–82, 2001

Quatification of Poincaré plot

Huikuri, et al. Circulation 93:1836-44, 1996

SD2:Long-term HRV

SD1:Instantaneous HRV

SD1/SD2:Shape of the plot

Thank you for your attention