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  • Make figure: (1) healthy heart rate sig
  • The method in quantifying Poincare plot was proposed. Although they may be proven to have strong correlation with linear measurement, they still exhibited prognostic value for some disease. SD1 is the standard deviation of the distances between each dispersed point and the this line X2 which slope=1. SD2 is the standard deviation of the distance between each dispersed point and the line perpendicular to X2. SD1/SD2 can see approximately as the shape of the plot.
  • f20080820101055.ppt

    1. 1. <ul><li>Finding Hidden Information in heart rate dynamics </li></ul><ul><li>Men-Tzung Lo, Ph.D, </li></ul><ul><li>Assistant Research Scientist, </li></ul><ul><li>Research-Center-Adaptive-Data-Analysis, NCU </li></ul>
    2. 2. PhysioNet
    3. 3. Between Genomics and Diagnostics Something is Missing… Biomedical Informatics: Methods, Techniques and Theories Bioinformatics Imaging Informatics Clinical Informatics Public Health Informatics Molecular and Cellular Processes Tissues and Organs Populations And Society Individuals (Patients) ?
    4. 4. A More Complete Picture Biomedical Informatics: Methods, Techniques and Theories Bioinformatics Imaging Informatics Clinical Informatics Public Health Informatics Molecular and Cellular Processes Tissues and Organs Diagnostic and Functional Dynamics Populations And Society Individuals (Patients) Complex Signals Informatics
    5. 5. Which is the Healthy Subject? Escape statistical distinction based on conventional comparisons
    6. 6. Variability vs. Complexity Ary L Golberberg, “complex system”, ProC Am Thorac soc
    7. 7. “ B eyond ANOVA” (ANalysis Of VAriance between groups) Three Key Concepts ( The purpose of complex signal informatics ): <ul><li>Physiologic signals are the most complex in nature </li></ul><ul><li>Important basic/clinical information is “hidden” (encoded) in these fluctuations </li></ul><ul><li>3. Complexity degrades with pathology/aging </li></ul>The often “noisy” variability actually is the signal and represents the nonlinear signaling mechanisms
    8. 8. <ul><li>Body as servo-mechanism type machine </li></ul><ul><li>Importance of corrective mechanisms to keep variables “in bounds.” </li></ul><ul><li>Healthy system are self-regulated to reduce variability and maintain physiologic constancy. </li></ul><ul><ul><li>Underlying notion of “constant,” “steady-state,”” conditions . </li></ul></ul>Restored steady state … OR Baseline Perturbation
    9. 9. Homeostasis Revisited <ul><li>… OR </li></ul><ul><li>Is complex spatio-temporal variability a mechanism of object with multi-organization ? </li></ul>But, What’s the healthy complexity ?
    10. 10. Some Characteristics of Healthy Complexity <ul><li>Nonstationarity </li></ul><ul><ul><li>Statistics change with time </li></ul></ul><ul><li>Nonlinearity </li></ul><ul><ul><li>Components interact in unexpected ways ( “cross-talk”, the superposition paradigm fails ) </li></ul></ul><ul><li>Multiscale Organization </li></ul><ul><ul><li>Fluctuations/structures may have fractal organization </li></ul></ul><ul><li>Time Irreversibility </li></ul><ul><ul><li>Non-Periodic signal </li></ul></ul>Healthy Heart Rate Dynamics
    11. 11. Is Your World Linear or Nonlinear? <ul><li>Linear Process: </li></ul><ul><ul><li>Simple rules  simple behaviors </li></ul></ul><ul><ul><li>Things add up </li></ul></ul><ul><ul><li>Proportionality of input/output </li></ul></ul><ul><ul><li>High predictability, no surprises </li></ul></ul><ul><li>Nonlinear Process: </li></ul><ul><ul><li>Simple rules  complex behaviors </li></ul></ul><ul><ul><li>Small changes may have huge effects </li></ul></ul><ul><ul><li>Low predictability & anomalous behaviors </li></ul></ul><ul><ul><li>Whole  sum of parts </li></ul></ul>
    12. 12. *** Danger *** Linear Fallacy : Widely-held assumption that biological systems can be largely understood by dissecting out micro-components or modules and analyzing them in isolation. “ Rube Goldberg physiology” Pencil Sharpener
    13. 13. “ Nonlinear” Pharmacology <ul><li>Treatment of Chronic Heart Failure </li></ul><ul><li>Linear (target) approach: increase contractility* </li></ul><ul><li>Milrinone </li></ul><ul><li>Vesnarinone </li></ul><ul><li>Systems approach: interrupt vicious neurohormonal cycle** </li></ul><ul><li>Beta-blockers </li></ul><ul><li>* Excess mortality </li></ul><ul><li>** Enhanced survival </li></ul>
    14. 15. Loss of Complexity/Information with Disease <ul><li>Hypotheses : </li></ul><ul><ul><li>The output of physiologic systems often becomes more regular and predictable with disease </li></ul></ul>
    15. 16. Multiscale Time Irreversibility (MTI): <ul><li>Time irreversibility is greatest for healthy physiologic dynamics, which have the highest adaptability </li></ul><ul><li>Time irreversibility decreases with aging and disease </li></ul>Healthiest vs Sickest
    16. 17. Congestive heart failure
    17. 18. Heart Rate Fluctuates Cyclically During Sleep Apnea 60 minutes of data
    18. 19. Complexity analysis is developed to quantize the dynamics of biology signals
    19. 20. Wonderful World of “Hidden” Complexity/Nonlinear Mechanisms in Physiology <ul><li>Bifurcations (abrupt change) </li></ul><ul><li>Nonlinear oscillations </li></ul><ul><li>Time asymmetry </li></ul><ul><li>Deterministic chaos </li></ul><ul><li>Fractals </li></ul><ul><li>Nonlinear waves: spirals/scrolls </li></ul><ul><li>Hysteresis </li></ul>Biomedical signals that have been analyzed using complex signal informatics include heart rate, nerve activity, renal flow, arterial pressure, and respiratory waveforms.
    20. 21. Nonlinear Mechanisms in Physiology <ul><li>Bad news: physiology is complex! </li></ul><ul><li>Good news: the complex behavior can arise in general mechanisms with simple rules </li></ul>
    21. 22. Fractals as a Design Principle in Nature 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 as self-similarity or scale-invariance.
    22. 23. Fractal Self-Organization: Coronary Artery Tree
    23. 24. Fractals and Information Transmission: Purkinje Cells in Cerebellum
    24. 25. Are there Fractal (Scale-Free) Processes in Biology? Fractal : A tree-like object or process , composed of sub-units (and sub-sub-units, etc) that resemble the larger 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)
    25. 26. Why is it Physiologic to be Fractal? <ul><li>Healthy function requires capability( non-integer fractal dimension) to cope </li></ul><ul><li>with unpredictable environments </li></ul><ul><li>Scale-free (fractal) systems generate broad range of long-range correlated responses  “memory effect” </li></ul>disorder Fractal mechanism
    26. 27. Fractal Complexity Degrades with Disease Nature 1999; 399:461 Phys Rev Lett 2002; 89 : 068102 Healthy dynamics poised between too much order and total randomness. But randomness is not chaos! Single Scale Periodicity Uncorrelated Randomness Two Patterns of Pathologic Breakdown Healthy Dynamics: Multiscale Fractal Variability
    27. 28. Transformation Seems irregular ?
    28. 30. ApEn
    29. 31. Biological systems need to operate across multiple spatial and temporal scales; and hence their complexity is also multiscaled
    30. 32. DFA & multi-fractal
    31. 33. Scale dependent fractal (Detrend-Fluctuation-Analysis method, C.-k.Peng,1995, chaos ) <ul><li>The average root-mean-square fluctuation functions F(n) is obtained after integrating and detrending the data (to exclude environmental stimuli ) </li></ul>Slope =0.5 un-correlation Anti-correlation Fractal Harmonic or periodic
    32. 34. Color-coded wavelet analysis (Plamen Ch Ivanov, nature ,1999 ) singularity
    33. 35. Diagnosis To prognosis Normal abnormal Traditional signal Complexity analysis can help specify the abnormal. But, what is feature for the critical case?
    34. 36. Dynamics of heart rate (HR) Sympathetic stimulation Parasympathetic stimulation HR(bpm) Heart rate Heart rate
    35. 37. Quantification of HR dynamics <ul><li>Time domain measurement </li></ul><ul><ul><li>Standard deviation of normal-to-normal beat intervals (SDNN) </li></ul></ul><ul><li>Power spectrum analysis </li></ul><ul><ul><li>High frequency (HF) </li></ul></ul><ul><ul><li>Low frequency (LF) </li></ul></ul><ul><ul><li>Very low frequency (VLF) </li></ul></ul><ul><ul><li>LF/HF </li></ul></ul>Task Force of the European Society of Cardiology and the North American of Pacing and Electrophysiology , Circulation 93:1043-65,1996
    36. 38. <ul><li>Fourier analysis is a valid technique for investigation of the oscillatory components of circulatory and respiratory systems. </li></ul><ul><li>(Attinger, et al., Biophys. J. 6:291-304, 1966.) </li></ul>Applicability of fourier transform In analysis of biological systems Transformation
    37. 39. Quantification of HR dynamics <ul><li>Time domain measurement </li></ul><ul><ul><li>Standard deviation of normal-to-normal beat intervals (SDNN) </li></ul></ul><ul><li>Power spectrum analysis </li></ul><ul><ul><li>High frequency (HF) </li></ul></ul><ul><ul><li>Low frequency (LF) </li></ul></ul><ul><ul><li>Very low frequency (VLF) </li></ul></ul>Task Force of the European Society of Cardiology and the North American of Pacing and Electrophysiology , Circulation 93:1043-65,1996
    38. 40. Clinical applications Task Force of the European Society of Cardiology and the North American of Pacing and Electrophysiology , Circulation 93:1043-65,1996
    39. 41. Clinical applications (cont’) Task Force of the European Society of Cardiology and the North American of Pacing and Electrophysiology , Circulation 93:1043-65,1996
    40. 42. Modulation of heartbeats Sympathetic nerve Parasympathetic nerve baroreceptor chemoreceptor Stretch receptor
    41. 43. Analysis of HR dynamics by nonlinear methods <ul><li>Dynamic measures of HRV may uncover abnormalities that are not easily detectable with traditional time and frequency domain measures. </li></ul><ul><li>Laitio, et al., Anesthesiology 93:69-80,2000 </li></ul>
    42. 44. Huikuri, et al. Circulation 101:47–53, 2000 Applications of nonlinear analysis in patients with myocardial infarction
    43. 45. Application of nonlinear analysis in heart failure patients Makikallio, et al. Am J Cardiol 87:178–82, 2001
    44. 46. 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
    45. 47. <ul><li>Thank you for your attention </li></ul>

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