Title :
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
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;
Conference_Titel :
Computer-Based Medical Systems (CBMS), 2014 IEEE 27th International Symposium on
Conference_Location :
New York, NY
DOI :
10.1109/CBMS.2014.37
