A Machine Learning Model for Obstructive Sleep Apnea Detection Using Ensemble Learning and Single-Lead EEG Signal Data
Atiya Khan, Saroj Kr. Biswas, Chukhu Chunka, Akhil Das
Abstract
Sleep is crucial for cognitive and physical functions, and sleep disorders like Obstructive Sleep Apnea (OSA) can significantly affect a person’s health. Polysomnography is the gold standard for diagnosing OSA, but despite its effectiveness, it is time-consuming and prone to human errors. To address this issue, this paper proposes an Ensemble Expert System for Obstructive Sleep Apnea Detection - II (EESOSAD-II) that leverages the single channel (C4-A1) Electroencephalography (EEG) signal and an ensemble learning model. The proposed model employs Discrete Wavelet Transform (DWT) with db8 for efficient EEG sub-band separation and statistical feature extraction. To enhance the data quality, the proposed model incorporates a Gaussian filter for feature smoothing and an Isolation Forest for outlier treatment. To further enhance the pre-processing pipeline, Recursive Feature Elimination (RFE) is used for sub-optimal feature set selection, and the Extra Tree classifier is employed for efficient classification of apnea and non-apnea events. The performance of the proposed model is evaluated using multiple evaluation metrics like - Precision, Recall, Accuracy, F1-Score and ROC_AUC curve for detailed analytical and benchmark comparison. The verification result shows that the proposed model achieved an average accuracy of 86% in comparatively optimized computational time than the state-of-the-art feature selection techniques. Furthermore, the EESOSAD-II outperformed the benchmark OSA detection model with optimal performance margin and achieved efficient performance results.