Parametric Power Spectrum Analysis of ECG Signals for Obstructive Sleep Apnoea Classification

Author

Wang, Xia L. ; Eklund, J. Mikael ; McGregor, Carolyn

Author_Institution

Dept. of Electr., Comput. & Software Eng., Univ. of Ontario, Oshawa, ON, Canada

fYear

2014

fDate

27-29 May 2014

Firstpage

8

Lastpage

13

Abstract

This work applies time-varying parametric power spectral density analysis to ECG and derived signals in order to discover the frequency components related to obstructive sleep apnoea. Heart rate variability signals were derived from the original ECG signals using R-R wave intervals. The power spectral densities were calculated using a parametric method across the heart rate variability frequency bands. Based on the power spectrum values, a number of beat-by-beat frequency power features were extracted from a PhysioNet dataset and studied together with the PhysioNet apnoea expert annotations.

Keywords

electrocardiography; feature extraction; medical diagnostic computing; medical disorders; medical signal processing; spectral analysis; ECG signals; PhysioNet apnoea expert annotations; PhysioNet dataset; R-R wave intervals; beat-by-beat frequency power feature extraction; heart rate variability frequency bands; heart rate variability signals; obstructive sleep apnoea classification; time-varying parametric power spectral density analysis; Correlation; Electrocardiography; Heart rate; Mathematical model; Resonant frequency; Sleep apnea; Time-frequency analysis; ECG; classification; obstructive sleep apnoea; power spectral analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer-Based Medical Systems (CBMS), 2014 IEEE 27th International Symposium on

Conference_Location

New York, NY

Type

conf

DOI

10.1109/CBMS.2014.37

Filename

6881838