Explainable Computer-Aided Detection of Obstructive Sleep Apnea and Depression

Abstract

Obstructive Sleep Apnea Syndrome (OSAS) and Major Depressive Disorder (MDD) are common conditions associated with poor quality of life. In this work, we aim to classify OSAS and depression in patients with OSAS using machine learning techniques. We have extracted features from electrocardiograms (ECG), electroencephalograms (EEG), and breathing signals from polysomnography (PSG) at specific 5-minute intervals, where the participants’ statuses are known, meaning we do not need breathing signals. These statuses include sleep stage, whether or not they have depression, or an apneic event has occurred. The PSGs were recorded from a total of 118 subjects with a 75/25 split for training and testing and the resultant features were used in sleep staging and classifying OSAS and depression in OSAS patients. Sleep staging was best done with random forest without feature selection, yielding an accuracy of 70.52 % and F1-Score of 69.99 %. The best classification performance of OSAS happened during deep sleep without feature selection and SVM, which yielded an accuracy of 98.36 % and F1-Score of 98.82 %. All sleep stages with Chi2 ANN yielded an accuracy of 72.95 % and F1-Score of 73.43 % for classification of depression in OSAS patients. Results show promise in detecting OSAS and depression in OSAS patients, and the Bland-Altman plot shows that posterior probability provides comparable means of detecting OSAS to the apnea-hypopnea index (AHI). Besides detection of OSAS in depressed patients, this work serves to classify depression and give insights into relevant sleep stages to both of those conditions, allowing better planning for polysomnography.