1. Heart Rate Variability: Measures and Models 指導教授：鄭仁亮 學生：曹雅婷

2. Outline  Introduction  Methods Conventional Point Process Fractal Point Process  Measure Standard Measures Novel Measures

3. Introduction  ECG a recording of the cardiac-induced skin potentials at the body ’ s surface  HRV called heart rate variability, the variability of the RR-interval sequence

4. Methods  The heartbeat sequence as a point process.  The sequence of heartbeats can be studied by replacing the complex waveform of an individual heartbeat recorded in the ECG.  The sequence of heartbeats is represented by

5. ECG Analysis

6. Conventional Point Process  Simplest homogeneous Poisson point process  Related point process nonparalyzable fixed-dead-time modified Poisson point process gamma-γ renewal process

7. Homogeneous Poisson point process  The interevent-interval probability density function where λ is the mean number of events per unit time.  interevent-interval mean=1/ λ  interevent-interval variance=1/ λ 2

8. Dead-time modified Poisson point process  The interevent-interval probability density function Here τ d is the dead time and λ is the rate of the process before dead time is imposed. 0

9. Fractal Point Process  Fractal stochastic processes exhibit scaling in their statistics.  Suppose changing the scale by any factor a effectively scales the statistic by some other factor g(a), related to the factor but independent of the original scale: w(ax) = g(a)w(x).

10. Fractal Point Process  The only nontrivial solution of this scaling equation, for real functions and arguments, that is independent of a and x is w(x) = bg(x) with g(x) = x c  The particular case of fixed a admits a more general solution g(x; a) = x c cos[2 π ln(x)/ ln(a)]

11. Standard Frequency-Domain Measures  A rate-based power spectral density Units of sec -1  An interval-based power spectral density Units of cycles/interval  To convert the interval-based frequency to the time-based frequency using

12. Estimate the spectral density 1.Divided data into K non-overlapping blocks of L samples 2.Hanning window 3.Discrete Fourier transform of each block

13. Measures in HRV  VLF. The power in the very-low-frequency range: 0.003 – 0.04 cycles/interval.  LF. The power in the low-frequency range: 0.04 – 0.15 cycles/interval.  HF. The power in the high-frequency range: 0.15 – 0.4 cycles/interval.  LF/HF. The ratio of the low-frequency- range power to that in the high-frequency range.

15. Standard Time-Domain Measures  pNN50. proportion of successive NN intervals  SDANN. Standard Deviation of the Average NN interval  SDNN. Standard Deviation of the NN interval

16. Other Standard Measures  The event-number histogram  The Fano factor

17. Novel Scale-Dependent Measures  Allen Factor [A(T)] The Allan factor is the ratio of the event- number Allan variance to twice the mean:

18. Wavelet transform using Haar wavelet