Deep Learning-based EEG Analysis for Sleep Apnea Detection

Sleep apnea, a condition disrupting normal breathing patterns during sleep, poses significant health challenges. While the definitive diagnosis relies on polysomnography, this method's complexity underscores the need for innovative approaches to detecting the condition. Central to this diagnostic process is electroencephalography (EEG), which measures the brain's electrical activity and potentially provides detailed insights into sleep patterns and disturbances. Addressing this, the present study introduces a cutting-edge Temporal Convolutional Neural Network (TCNN) approach for the automatic detection of sleep apnea using single-lead EEG signals. TCNNs offer several advantages over traditional analytical methods by ensuring the preservation of temporal data sequence, which is essential for analyzing the complex, long-range dependencies observed in EEG recordings. This methodological innovation allows for enhanced computational efficiency and potentially greater accuracy in diagnosis. To validate model effectiveness, we employ a comprehensive evaluation framework, comparing it against established machine learning models on several key performance metrics, including accuracy, Area Under the ROC (AUC), F1-score, recall, and precision. Our objective is not only to examine the TCNN model's performance but also to investigate its utility in streamlining the diagnostic process for sleep apnea. By advancing the accuracy and efficiency of diagnosis, the present study aims to promote earlier treatment interventions, ultimately improving patient outcomes and reducing the health complications associated with sleep apnea.