Classification of sleep apnea based on EEG signals using ensemble learning methods

Abstract

Sleep Apnea Syndrome (SAS) is a common disorder characterized by repeated cessation of airflow during sleep. Accurate classification of its main subtypes—Obstructive Sleep Apnea (OSA), Central Sleep Apnea (CSA), and Normal Breathing (NB)—is essential for effective clinical management. This study proposes a classification framework based on electroencephalography (EEG) signals and ensemble learning techniques. EEG data from C3-A2 and C4-A1 channels in 25 subjects were extracted and then segmented into 10-s windows. From each segment, four features such as Sample Entropy, Higuchi Fractal Dimension, Variance, and Standard Deviation were extracted across five frequency bands. Principal Component Analysis (PCA) was applied for dimensionality reduction prior to classification. Classification was performed using ensemble models with decision trees as base learners. Using subject-wise cross-validation, Boosting showed the best performance among the methods tested, with classification accuracies of 92.33% for NB, 78.6% for OSA, and 77.7% for CSA. These findings suggest that EEG-based ensemble learning may provide a non-invasive and scalable approach for Sleep Apnea subtype classification, with the potential to reduce reliance on full Polysomnography (PSG) in certain screening or monitoring scenarios.