DIMACS April, 2002 Nonlinear Dynamics, Chaos, and Complexity in Bedside Medicine

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DIMACS April, 2002Nonlinear Dynamics, Chaos, and
Complexity in Bedside Medicine
Ary L. Goldberger, M.D. Harvard Medical
School NIH/NCRR Research Resource for Complex
Physiologic Signals (PhysioNet)
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A Time Series Challenge
Heart Rate Dynamics in Health and Disease Which
time series is normal?
Heart Failure
Heart Failure
Normal
Atrial Fibrillation
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Cardiac Electrical System
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How is Heart Rate Dynamics Regulated?

• Coupled Feedback Systems Operating Over Wide
  Range of Temporal/Spatial Scales

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Three Themes

• Healthy systems show complex dynamics, with
  long-range (fractal) correlations and multiscale
  nonlinear interactions.
• Life-threatening pathologies and aging are
  associated with breakdown of fractal scaling and
  loss of nonlinear complexity.
• Open-source databases and software tools are
  needed to catalyze advances in complex signal
  analysis.

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Hallmarks of Complexity

• Nonstationarity
• Statistics change with time
• Nonlinearity
• Components interact in unexpected ways (
  cross-talk )
• Multiscale Variability
• Fluctuations may have fractal properties

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Is the Physiologic World Linear or Nonlinear?

• Linear World
• Things add up
• Proportionality of input/output
• High predictability, no surprises
• Nonlinear World
• Whole ? sum of parts (emergent properties)
• Small changes may have huge effects
• Low predictability, anomalous behaviors

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Whats Wrong with this Type ofSignal
Transduction Picture?
Answer No feedback No nonlinearity Complicated!
but Complex dynamics missing!
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Danger
Linear Fallacy Widely-held assumption that
biological systems can be largely understood by
dissecting out micro-components and analyzing
them in isolation.
Rube Goldberg physiology
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Nonlinear/Fractal Mechanisms in Physiology

• Bad news your data are complex!
• Good news there are certain generic mechanisms
  that do not depend on details of system
  (universalities)

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Wonderful World of Complexity
Sampler of Nonlinear Mechanisms in Physiology

• Abrupt changes
• Bifurcations
• Bursting
• Bistability
• Hysteresis
• Nonlinear oscillations
• Multiscale (fractal) variability
• Deterministic chaos

• Nonlinear waves spirals scrolls solitons
• Stochastic resonance
• Time irreversibility
• Complex networks
• Emergent properties

Ref Goldberger et al. PNAS 2002 99 Suppl. 1
2466-2472.
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Six Examples ofSpiral Waves in Excitable Media
From J. Walleczek, ed. Self-Organized Biological
Dynamics and Nonlinear Control Cambridge
University Press, 2000.
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Multiscale Complexity and Fractals
Fractal A tree-like object or process, composed
of sub-units (and sub-sub-units, etc) that
resemble the larger scale structure. This
internal look-alike property is known
as self-similarity or scale-invariance.
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Fractal Self-OrganizationCoronary Artery Tree
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Fractal Self-OrganizationHis-Purkinje
Conduction Network
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Fractal Self-OrganizationPurkinje Cells in
Cerebellum
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Multiscale Complexity and Fractals
Fractal A tree-like object or process, composed
of sub-units (and sub-sub-units, etc) that
resemble the larger scale structure. This
internal look-alike property is known
as self-similarity or scale-invariance.
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Loss of Multiscale Fractal Complexitywith Aging
Disease
Lancet 1996 3471312 Nature 1999 399461
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Fractal Analysis of Nonstationary Time Series
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Fractal Scaling in Health and Disease
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Why is it Healthy to be Fractal?

• Healthy function requires capability to cope with
  unpredictable environments
• Fractal systems generate broad repertoire of
  response ? adaptability
• Absence of characteristic time scale helps
  prevent mode-locking (pathologic resonances)

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Concept ofDE-COMPLEXIFICATION OF DISEASE

• The output of many systems becomes more regular
  and predictable with pathologic perturbations
• Clinical medicine not feasible without such
  stereotypic, predictable behaviors clinicians
  look for characteristic patterns/scales
• Healthy function multi-scale dynamics/scale-free
  behavior harder to characterize

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Loss of Fractal ComplexityResolves Clinical
Paradox

• Patients with wide range of disorders often
  display strikingly predictable (ordered) dynamics
• Reorder vs. Disorder
• Examples Parkinsonism / Tremors
• Obsessive-compulsive behavior
• Nystagmus
• Cheyne-Stokes breathing
• Obstructive sleep apnea
• Ventricular Tachycardia
• Hyperkalemia ? Sine-wave ECG
• Cyclic neutropenia
• etc., etc.

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Warning!
Excessive Regularity is Bad For Your
Health Example Photic (Stroboscopic) Stimulation
and Seizures
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Whats the Cure?
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Finding and Using Hidden Information

• Physiologic dynamics exhibit an extraordinary
  range of complexity that defies
• Conventional statistics
• Homeostatic models
• Important information hidden in
• complex signal fluctuations relating to
• Basic signaling mechanisms
• Novel biomarkers

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The Bad News for Complex Signal Analysis

• Databases are largely unavailable
• or incompletely documented
• Investigators use different, undocumented
  software tools on different databases

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NCRR Research Resource for Complex Physiologic
Signals - PhysioNet
www.physionet.org Start date September 1,
1999 100,000 visits to date 1 terabyte of data
downloaded!
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Design of the PhysioNet Resource

• PhysioNet
• Dissemination portal
• Tutorials
• Discussion Groups

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Design of the PhysioNet Resource

• PhysioBank
• Reference Datasets
• Multi-Parameter (e.g. sleep apnea intensive
  care unit)
• ECG
• Gait
• Other Neurological
• Images
• Data supporting publications
• 30 gigabytes currently online
• 1 terabytes online in 2003

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Design of the PhysioNet Resource

• PhysioToolkit
• Open source software
• Data analysis packages
• Physiologic models
• Software from publications

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PhysioNet Signal Analysis Competitions

• Challenge 2001
• Can you forecast an imminent
• cardiac arrhythmia (atrial fibrillation)
• during normal cardiac rhythm?
• Challenge 2002
• Can you simulate/model complex healthy
• heart rate variability?
• Future
• Seizure forecasting Biomedical image
  processing, etc.

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Conclusions

• Homeostasis revisited
• Physiologic control
• Complex (fractal/nonlinear) dynamics
• Loss of fractal/nonlinear complexity
• New markers of life-threatening pathology/aging
• Needed Open-source data and software for basic
  mechanisms and bedside diagnostics

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Welcome to PhysioNet!

• www.physionet.org
• Please visit and contribute