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)
A Time Series Challenge:
Heart Failure Heart Failure
Normal Atrial Fibrillation
Heart Rate Dynamics in Health and DiseaseWhich time series is normal?
Cardiac Electrical System
How is Heart Rate Dynamics Regulated?Coupled Feedback Systems Operating Over Wide Range of Temporal/Spatial Scales
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.
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
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
What’s Wrong with this Type ofSignal Transduction Picture?
Answer: No feedback; No nonlinearityComplicated! but …Complex dynamics missing!
*** 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”
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)
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.
Six Examples ofSpiral Waves in Excitable Media
From: J. Walleczek, ed. Self-Organized Biological Dynamics and Nonlinear ControlCambridge University Press, 2000.
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
Fractal Self-Organization:Coronary Artery Tree
Fractal Self-Organization:His-Purkinje Conduction Network
Fractal Self-Organization:Purkinje Cells in Cerebellum
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
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
Fractal Analysis of Nonstationary Time Series
Fractal Scaling in Health and Disease
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)
• 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
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.
Warning!
Excessive Regularity is Bad For Your HealthExample: Photic (Stroboscopic) Stimulation and Seizures
What’s the Cure?
• 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
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 ”
www.physionet.orgStart date: September 1, 1999
100,000+ visits to date1 terabyte of data downloaded!
NCRR Research Resource for Complex Physiologic Signals - “PhysioNet”
PhysioNet• Dissemination portal• Tutorials• Discussion Groups
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
Design of the PhysioNet Resource
PhysioToolkit• Open source software• Data analysis packages• Physiologic models• Software from publications
Design of the PhysioNet Resource
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.
• 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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