Detection of Sleep Apnea/hypopnea Events Using Synchrosqueezed Wavelet Transform
Delaram Jarchi, Saeid Sanei, Ales Procházka
Abstract
In this article, detection of sleep apnea or hypopnea events is addressed using a single channel electrocardiography (ECG) signal by analysis of respiratory extracted modulation. First, R peaks are detected from ECG signal. Then, a time-series with the amplitude (height) and timing of R peaks representing respiratory-induced amplitude modulation is constructed. This signal is resampled evenly at 4Hz. Synchrosqueezed wavelet transform (SSWT) together with an iterative time-frequency ridge estimation is applied to provide a robust estimation of instantaneous respiratory frequency and detect the regions with/without sleep apnea/hypopnea events. Signal reconstruction using inverse synchrosqueezed wavelet transform (ISSWT) has been performed. The appeared peaks can identify and measure the duration of apnea/hypopnea events.
BibTeX
@inproceedings{icassp2019_detectionofsleep,
title = {Detection of Sleep Apnea/hypopnea Events Using Synchrosqueezed Wavelet Transform},
author = {Delaram Jarchi and Saeid Sanei and Ales Procházka},
booktitle = {ICASSP 2019},
year = {2019}
}