dimacs april, 2002 nonlinear dynamics, chaos, and complexity in bedside medicine ary l. goldberger,...
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DIMACS April, 2002
Nonlinear 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 Failure Heart Failure
Normal Atrial Fibrillation
Heart Rate Dynamics in Health and DiseaseWhich time series is normal?
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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
Healthy Heart Rate Dynamics
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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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What’s Wrong with this Type ofSignal Transduction Picture?
Answer: No feedback; No nonlinearityComplicated! but …Complex dynamics missing!
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*** Danger ***
Linear Fallacy: Widely-held assumption that biologicalsystems 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:
• 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
Sampler of Nonlinear Mechanisms in Physiology
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 ControlCambridge University Press, 2000.
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Fractal: A tree-like object or process, composed ofsub-units (and sub-sub-units, etc) that resemble thelarger scale structure.
This internal look-alike property is known asself-similarity or scale-invariance.
Multiscale Complexity and Fractals
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Fractal Self-Organization:Coronary Artery Tree
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Fractal Self-Organization:His-Purkinje Conduction Network
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Fractal Self-Organization:Purkinje Cells in Cerebellum
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Fractal: A tree-like object or process, composed ofsub-units (and sub-sub-units, etc) that resemble thelarger scale structure.
This internal look-alike property is known asself-similarity or scale-invariance.
Multiscale Complexity and Fractals
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Loss of Multiscale Fractal Complexitywith Aging & Disease
Single Scale Periodicity Uncorrelated Randomness
Two Patterns ofPathologic Breakdown
Healthy Dynamics: Multiscale Fractal Variability
Lancet 1996; 347:1312Nature 1999; 399:461
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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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• 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
Concept ofDE-COMPLEXIFICATION OF DISEASE
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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 / TremorsObsessive-compulsive behaviorNystagmusCheyne-Stokes breathingObstructive sleep apneaVentricular TachycardiaHyperkalemia “Sine-wave” ECGCyclic neutropeniaetc., etc.
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Warning!
Excessive Regularity is Bad For Your HealthExample: Photic (Stroboscopic) Stimulation and Seizures
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What’s the Cure?
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• Physiologic dynamics exhibit an extraordinary range of complexity that defies:
• Conventional statistics• Homeostatic models
• Important information hidden incomplex signal fluctuations relating to:
• Basic signaling mechanisms• Novel biomarkers
Finding and Using Hidden Information
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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
“ Babel-ography ”
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www.physionet.orgStart date: September 1, 1999
100,000+ visits to date1 terabyte of data downloaded!
NCRR Research Resource for Complex Physiologic Signals - “PhysioNet”
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PhysioNet• Dissemination portal• Tutorials• Discussion Groups
Design of the PhysioNet Resource
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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
Design of the PhysioNet Resource
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PhysioToolkit• Open source software• Data analysis packages• Physiologic models• Software from publications
Design of the PhysioNet Resource
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PhysioNet Signal Analysis Competitions
• Challenge 2001:Can you forecast an imminentcardiac arrhythmia (atrial fibrillation) during normal cardiac rhythm?
• Challenge 2002:Can you simulate/model complex healthyheart rate variability?
• Future:Seizure forecasting; Biomedical image processing, etc.
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• 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
Conclusions
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