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A statistical modeling of mouse heart beat rate variability Paulo Gonçalves INRIA, France

A statistical modeling of mouse heart beat rate variability Paulo Gonçalves INRIA, France On leave at IST-ISR Lisbon, Portugal Joint work with Hôpital Lariboisière Paris, France Pr. Bernard Swynghedauw Dr. Pascale Mansier Christophe Lenoir

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A statistical modeling of mouse heart beat rate variability Paulo Gonçalves INRIA, France

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  1. A statistical modeling of mouse heart beat rate variability Paulo Gonçalves INRIA, France On leave at IST-ISR Lisbon, Portugal Joint work with Hôpital Lariboisière Paris, France Pr. Bernard Swynghedauw Dr. Pascale Mansier Christophe Lenoir Laboratório de Biomatemática, Faculdade de Medicina, Universidade de Lisboa June 15th, 2005

  2. Outline • Physiological and pharmacological motivations • Experimental set up • Signal analysis • Statistical analysis • Forthcoming work ?

  3. Physiological and pharmacological motivations Cardiovascular research and drugs testing protocoles are conducted on various mammalians: rats, dogs, monkeys… Share the same vagal (parasympathetic) tonus as humans Cardiovascular system of mice has not been very investigated Difficulty of telemetric measurements on non anaesthetized freely moving animals Economic stakes prompts the use of mice for pharmacological developments Recent integrated technology allows in vivo studies

  4. Controls cardiac rythm Physiological and pharmacological motivations Autonomic Nervous System Sympathetic branch accelerates heart beat rate Parasympathetic (vagal) branch decelerates heart beat rate Better understanding of the role of sympathovagal balance on mice heart rate variability

  5. Experimental setup • Sample set: eighteen male C57bl/6 mice (10 to 14 weeks old) • A biocompatible transmitter (TA10ETA-F20, DataSciences International) • implanted (under isofluran mixture with carbogene anaesthesia 1.5 vol %) • Electro-cardiograms recorded via telemetric instrumentation • (Physiotel Receiver RLA1020, DataSciences International) at a 2KHz sampling frequency • on non anaesthetized freely moving animals • Pharmacological conditions: • saline solution (placebo) Control • saturating dose of atropine (1 mg/kg)Parasympathetic blockage • saturating dose of propranolol (1 mg/kg) Sympathetic blockage • combination of atropine and propranololANS blockage • Physical conditions • day ECG Resting • night ECG Intensive Activity

  6. Sympathetic branch Parasympathetic branch VLF LF HF Signal Analysis Control Beat-to-beat interval (RR) Power spectrum density time frequency

  7. Signal Analysis Atropine (effort) Beat-to-beat interval (RR) Power spectrum density Sympathetic branch Parasympathetic branch time frequency VLF LF HF

  8. is an index of the sympathovagal balance Energy (LF) Energy (HF) (Akselrod et al. 1981) Signal Analysis Propranolol (rest) Beat-to-beat interval (RR) Power spectrum density Sympathetic branch Parasympathetic branch time frequency VLF LF HF

  9. RR (ms) Time (s) Signal Analysis Control Atropine Propranolol Atropine & propranolol Linear Mixed Model proves no significant effect of atropine on HRV baseline

  10. Signal Analysis Day RR time series (resting) Night RR time series (active) RR (ms) Time (s)

  11. VLF LF HF Signal Analysis Power spectrum density RR (ms) Time (s) Frequency (Hz) Need to separate (non-stationary) low frequency trends from high frequency spike train (shot noise)

  12. Signal Analysis: Empirical Mode Decomposition Entirely adaptive signal decomposition Objective— From one observation of x(t), get a AM-FM type representation K x(t) = Σ ak(t) Ψk(t)k=1 with ak(.) amplitude modulating functions and Ψk(.) oscillating functions. Idea— “signal = fast oscillations superimposed to slow oscillations”. Operating mode—(“EMD”, Huang et al., ’98) (1) identify locally in time, the fastest oscillation ; (2) subtract it from the original signal ; (3) iterate upon the residual.

  13. Signal Analysis: Empirical Mode Decomposition A LF sawtooth + A linear FM =

  14. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  15. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  16. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  17. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  18. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  19. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  20. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  21. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  22. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  23. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  24. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  25. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  26. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  27. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  28. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  29. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  30. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  31. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  32. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  33. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  34. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  35. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  36. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  37. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  38. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  39. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  40. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  41. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  42. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  43. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  44. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  45. Signal Analysis: Empirical Mode Decomposition S I F T I N G P R O C E S S

  46. Signal Analysis: Empirical Mode Decomposition

  47. HF LF + VLF Signal Analysis: Empirical Mode Decomposition

  48. Signal Analysis: Empirical Mode Decomposition Day heart rate variability Night heart rate variability • Next step: prove significant differences between day and night time series • statistically • spectrally

  49. Signal Analysis: Empirical Mode Decomposition Day heart rate variability Night heart rate variability • Next step: prove significant differences between day and night time series • statistically • spectrally

  50. Normal plots Statistical modeling Empirical distributions of RR-intervals • Non Gaussian distributions • Similar tachycardia for day and night HRV • Symmetric distribution for night RR • Heavy tail distribution for day RR

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