Pulse Photoplethysmography derived respiration for obstructive sleep apnea detection

© 2017 IEEE Computer Society. All rights reserved. Five time series which are known to be modulated by respiration are derived from the pulse photoplethysmographic (PPG) signal, and they are analyzed for obstructive sleep apnea (OSA) detection: Pulse rate, amplitude, and width variabilities (PRV, PAV, and PWV, respectively), pulse upslopes, and slope transit time (STT). A total of 26 polysomnographic recordings were split in 1-min segments which were manually labeled as OSA (653 segments), normal breathing (7204 segments), or other pulmonary events. For each one of the 5 PPG-derived series, 4 features were extracted: the standard deviation, the power at high and low frequency (PLF) bands, and the normalized PLF. These 20 features were used as input of a least-squares support vector machine classifier using an RBF kernel. Results show an accuracy of 72.66%, suggesting that the analyzed features are promising for the detection of OSA from only the PPG signal.status: publishe