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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

Heart Rate Variability: Measures and Models

指導教授:鄭仁亮

學生:曹雅婷

outline
Outline
  • Introduction
  • Methods
    • Conventional Point Process
    • Fractal Point Process
  • Measure
    • Standard Measures
    • Novel Measures
introduction
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

methods
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
conventional point process
Conventional Point Process
  • Simplest
    • homogeneous Poisson point process
  • Related point process
    • nonparalyzable fixed-dead-time modified Poisson point process
    • gamma-γ renewal process
homogeneous poisson point process
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
dead time modified poisson point process
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

fractal point process
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).

fractal point process10
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)]

standard frequency domain measures
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
estimate the spectral density
Estimate the spectral density
  • Divided data into K non-overlapping blocks of L samples
  • Hanning window
  • Discrete Fourier transform of each block
measures in hrv
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.
standard time domain measures
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
other standard measures
Other Standard Measures
  • The event-number histogram
  • The Fano factor
novel scale dependent measures
Novel Scale-Dependent Measures
  • Allen Factor [A(T)]
    • The Allan factor is the ratio of the event-number Allan variance to twice the mean: