A Dirichlet Process Mixture Model for Autonomous Sleep Apnea Detection using Oxygen Saturation Data

Sleep apnea is a sleep disorder which is common in many children and adults. It is characterised by abnormal breath pauses or shallow breathing during sleep. Traditional diagnosis of apnea requires special equipment for data collection in clinical conditions and manual analysis by clinicians which is expensive and time-consuming. This paper presents a framework for autonomous detection of sleep apnea, using peripheral blood haemoglobin oxygen saturation (SpO(2)) data based on the fusion of multiple features and Dirichlet process mixture model. The SpO 2 signals are segmented into overlapping sub-sequences and several features are extracted from each segment. The distributions of features extracted from disorder and normal segments are modelled by two Gaussian mixture models, respectively, with the Dirichlet process as the prior. The advantage of the framework is that the number of clusters within mixture models can be learned from training data without strong assumptions, which contributes to accurate estimation of the distributions. The proposed framework is subject-independent and it is trained and tested on two publicly available databases with 10-fold cross-validation. It obtains accuracy of 84.89% on the St. Vincent's University Hospital Sleep Apnea Database and accuracy of 97.01% on the Apnea-ECG Database, outperforming state-of-the-art approaches. The results show that the proposed model is capable of representing the distributions of features independently of subjects and can accurately classify segmented signals from patients with symptoms of different severity. The results show the potential of the developed classification framework to support clinicians in their decision making.