Machine Learning Based Severity Classification of Obstructive Sleep Apnea Patients Using Awake Eeg

Abstract

Obstructive sleep apnea (OSA) is one of the most widespread breathing-based sleep disorders. Previous studies reported daytime sleepiness, mental fatigue, and cognitive decline in patients with OSA. The diagnosis of OSA is done with laborious overnight polysomnography (PSG) recording. This study aims to classify the severity of OSA patients according to the Apnea-Hypopnea Index (AHI) into mild and moderate to-serve groups (AHI >= 15) without using recorded signals during sleep, non-PSG signals and investigate the relevant features. For this purpose, 25 OSA patients participated in 3-minute eyes closed resting state EEG session on the following morning of overnight PSG recording. Time, spectral domain, and nonlinear features were extracted from the delta, theta, and alpha subbands' of EEG signals. Several machine learning algorithms were used to classify patients' severity. To investigate optimal feature combinations features were grouped according to their types (Time domain/ spectral domain and nonlinear), their electrodes (frontal/central/parietal/occipital), and their subbands (delta/ theta/alpha). Also, the Relief method was applied to select the most relevant features. A 5-fold cross validation method was used to generalize the model behaviour. Optimal feature combinations were selected according to classifiers' performances that were evaluated by accuracy, sensitivity, specificity, and area under curve (AUC) parameters when feature sets were used as input. The 15 selected features by the Relief method showed the best performances in the K-Nearest Neighbours (K = 5) classifier [accuracy: 93.33 % +/- 5.27 %; sensitivity: 92.30 % +/- 4.97 %; specificity: 94.14 % +/- 4.24 %; AUC:0.98]. The findings establish that awake resting state EEG records might be used as a faster tool to use OSA severity level.