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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
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
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