Authors:
Constantino A. García
1
;
Abraham Otero
1
;
Xosé Vila
2
and
Maria J. Lado
2
Affiliations:
1
University San Pablo CEU, Spain
;
2
University of Vigo, Spain
Keyword(s):
Heart Rate Variability, Wavelet Transform, RHRV.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Cardiovascular Signals
;
Computer Vision, Visualization and Computer Graphics
;
Informatics in Control, Automation and Robotics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Signal Processing, Sensors, Systems Modeling and Control
;
Time and Frequency Response
;
Time-Frequency Analysis
;
Wavelet Transform
Abstract:
Heart rate variability (HRV) power spectrum analysis is a well-known technique used to study the activity of the autonomic nervous system. It is performed by calculating the spectral power of certain bands of the RR time series. There are several tools that perform this type of analysis: Kubios HRV, PhysioNet’s HRV toolkit for MatLab and aHRV, among others. All these tools use the Short Fourier Transform to estimate spectral power. However, the RR time series is a non-stationary signal. The Wavelet transform is often a more suitable tool for analyzing non-stationary signals than the Short Time Fourier Transform. Its usefulness in HRV analysis has already been proven in the literature. However, the lack of HRV analysis tools that support it has made this technique underutilized in HRV studies. In this paper we present an extension to the RHRV opensource package that enablesWavelet-based HRV spectral analysis. Until now this package only supported HRV spectral analysis based on the Fou
rier transform.
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