Automated Explainable Detection of Cyclic Alternating Pattern (CAP) Phases and Sub-Phases Using Wavelet-Based Single-Channel EEG Signals

Sleep is a crucial component of health and well-being. It maintains the metabolism of the body and covers one-third of total life. The assessment of sleep quality is typically done by evaluating the macrostructure-based sleep stages, however, it does not take into account transient phenomena like K-complexes and transient fluctuations, which are crucial for the diagnosis of various sleep disorders. Cyclic alternating pattern (CAP) is a recurrent physiological electroencephalogram (EEG) activity that takes place in the brain during sleep and it is considered as a microstructure of sleep that can provide more accurate and relevant evaluation of sleep. The traditional way of CAP phase division is done manually by sleep specialists, which is sensitive, time-consuming, and prone to inaccuracies. Hence, there is a need for automated detection techniques that can solve the problems. This study proposes an automated, computerized approach for developing a machine learning model with explainable artificial intelligence (XAI) capabilities, using wavelet-based Hjorth parameters for classifying CAP A & B phases and phases A sub-phases (A1, A2, A3). The study utilizes SHAP (SHapley Additive exPlanations)-based feature ranking to provide insights into the model. This study uses the publicly accessible Physionet CAP sleep database. The model is developed using single-channel standardized EEG recordings from healthy subjects and patients with five types of sleep disorders, namely, insomnia, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement disorder (PLM), rapid eye movement behavior disorder (RBD) and narcolepsy. The best performance is obtained using k-nearest neighbors (KNN) and ensemble bagged trees (EbagT) classifiers. The proposed model achieved a average classification accuracy of 91.6% for healthy subjects and 94.33%, 86.3%, 88.68%, 84.43%, and 88.5% for narcolepsy, RBD, PLM, NFLE, and insomnia subjects respectively, for classifying phases A and B. Our model achieved a average classification accuracy of 92.85% for healthy subjects and 93.9%, 84.9%, 88.0%, 80.92%, and 89.41% for narcolepsy, RBD, PLM, NFLE, and insomnia subjects, respectively while categorizing A subphases (A1, A2, A3). The proposed method may help sleep experts to examine a person’s sleep quality automatically using the microstructure of sleep.