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Heart Rate Variability: Measures and Models

Heart Rate Variability: Measures and Models. 指導教授：鄭仁亮 學生：曹雅婷. Outline. Introduction Methods Conventional Point Process Fractal Point Process Measure Standard Measures Novel Measures. Introduction. ECG a recording of the cardiac-induced skin potentials at the body ’ s surface HRV

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Heart Rate Variability: Measures and Models

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  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/λ2

  8. Dead-time modified Poisson point process • The interevent-interval probability density function Here τd 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) = xc • The particular case of fixed a admits a more general solution g(x; a) = xc cos[2πln(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 • Divided data into K non-overlapping blocks of L samples • Hanning window • 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.

  14. 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

  15. Other Standard Measures • The event-number histogram • The Fano factor

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

  17. Wavelet transform using Haar wavelet

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