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Extraction Of Heart Rate Variability
#1

Abstract
In this paper neural classifier system preliminary feature extraction and selection process using time-frequency representation of heart rate variability (HRV) signal is presented. The crucial point of described method is hybrid multi-domain feature set creation, combining different type parameters as well as feature selection based on the measure of class separability property, computed for each extracted feature. Regarding specific properties of non-stationary HRV signal, wavelet transform was chosen as time-frequency representation tool. Presented results are connected both with optimal feature extraction and selection of HRV signals from patient with coronary artery disease as well as classifier performance verification.
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#2
The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved.Various algorithms of autoregressive (AR) recursive identification make it possible to evaluate power spectral distribution in correspondence with each sample of a time series, and time-variant spectral parameters can be calculated through the evaluation of the pole positions in the complex z-plane.neural classifier system preliminary feature extraction and selection process using time-frequency representation of heart rate variability (HRV) signal is presented.
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#3
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Extraction Of Heart Rate Variability

Introduction

Heart Rate Variability (HRV)


HRV is the physiological phenomenon where the time interval between heart beats varies.

Dependant on the extrinsic regulation of the heart rate.

Useful for understanding the status of Autonomic Nervous System (ANS).
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