f20080820101055.ppt
TRANSCRIPT
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Finding Hidden Information in heart rate dynamics
Men-Tzung Lo, Ph.D,
Assistant Research Scientist,
Research-Center-Adaptive-Data-Analysis, NCU
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PhysioNet
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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)
?
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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
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Which is the Healthy Subject?Escape statistical distinction based on conventional comparisons
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Variability vs. Complexity Ary L Golberberg, “complex system”,
ProC Am Thorac soc
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“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
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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
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Homeostasis Revisited
…OR
Is complex spatio-temporal variability a mechanism of object with multi-organization ?
But, What’s the healthy complexity ?
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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
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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
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*** 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
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“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
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• Hypotheses :• The output of physiologic systems often
becomes more regular and predictable with disease
Loss of Complexity/Information with Disease
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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
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Congestive heart failure
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Heart Rate Fluctuates Cyclically During Sleep Apnea
60 minutes of data
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Complexity analysis is developed to quantize the dynamics of
biology signals
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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.
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Nonlinear Mechanisms in Physiology
• Bad news: physiology is complex!• Good news: the complex behavior can arise in general
mechanisms with simple rules
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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
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Fractal Self-Organization:Coronary Artery Tree
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Fractals and Information Transmission: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
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?
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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
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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!
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Transformation
Seems irregular ?
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ApEn
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Biological systems need to operate across multiple spatial and temporal scales; and hence their complexity is also multiscaled
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DFA & multi-fractal
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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
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Color-coded wavelet analysis (Plamen Ch Ivanov, nature ,1999 )
singularity
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Diagnosis To prognosis
Normal
abnormal
Traditional signal Complexity analysis can
help specify the abnormal. But, what is feature for the critical
case?
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Dynamics of heart rate (HR)
Sympathetic stimulationSympathetic stimulation
Parasympathetic stimulationParasympathetic stimulation
HR(bpm)
Heart rate
Heart rate
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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
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• 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
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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
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Clinical applications
Task Force of the European Society of Cardiology and the North American of Pacing and Electrophysiology,
Circulation 93:1043-65,1996
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Clinical applications (cont’)
Task Force of the European Society of Cardiology and the North American of Pacing and Electrophysiology ,
Circulation 93:1043-65,1996
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Modulation of heartbeats
Sympathetic nerve Parasympathetic nerve
baroreceptor chemoreceptor
Stretch receptor
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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
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Huikuri, et al. Circulation 101:47–53, 2000
Applications of nonlinear analysis in patients with myocardial infarction
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Application of nonlinear analysis in heart failure patients
Makikallio, et al. Am J Cardiol 87:178–82, 2001
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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
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Thank you for your attention