Sleep Apnea Detection Using Pulse Photoplethysmography

Abstract

This study investigates the use of pulse photoplethysmography (PPG) for the detection of sleep apnea and its added value to oxygen saturation (SpO2) based detection. PPG-time series known to be modulated by both respiration and the autonomous nervous system were derived: pulse rate, amplitude and width variability, slope transit time, maximal pulse upslope and the area under the PPG peak. Moreover, the instantaneous power in the high and low frequency band of the pulse rate was estimated using a point-process model. For all extracted time series, five features were computed over a 1 minute interval: the mean, minimum and maximum value, standard deviation and gradient. Feature selection resulted in the 6 most discriminative features for PPG based detection of apneic minutes. These features were used as input for a least-squares support vector machine classifier, which was applied on polysomnographic data of 102 subjects suspected of having sleep apnea-hypopnea syndrome. A classification accuracy of 68.7 % was achieved. When SpO2 features were added to the classifier the accuracy increased to 83.4 %, which is only slightly higher than the 82.2 % obtained using only SpO2. These results show the potential of PPG features for sleep apnea detection, however, their added value to SpO2 is limited.