Exploring the Efficacy of Convolutional Neural Networks in Sleep Apnea Detection from Single Channel EEG
This addresses the problem of costly and inconvenient sleep apnea diagnosis for patients by offering an automated, accessible alternative to polysomnography.
The paper tackled sleep apnea detection by developing a Convolutional Neural Network (CNN) using single-channel EEG data, achieving 85.1% accuracy and a Matthews Correlation Coefficient of 0.22 to enable home-based diagnostics.
Sleep apnea, a prevalent sleep disorder, involves repeated episodes of breathing interruptions during sleep, leading to various health complications, including cognitive impairments, high blood pressure, heart disease, stroke, and even death. One of the main challenges in diagnosing and treating sleep apnea is identifying individuals at risk. The current gold standard for diagnosis, Polysomnography (PSG), is costly, labor intensive, and inconvenient, often resulting in poor quality sleep data. This paper presents a novel approach to the detection of sleep apnea using a Convolutional Neural Network (CNN) trained on single channel EEG data. The proposed CNN achieved an accuracy of 85.1% and a Matthews Correlation Coefficient (MCC) of 0.22, demonstrating a significant potential for home based applications by addressing the limitations of PSG in automated sleep apnea detection. Key contributions of this work also include the development of a comprehensive preprocessing pipeline with an Infinite Impulse Response (IIR) Butterworth filter, a dataset construction method providing broader temporal context, and the application of SMOTETomek to address class imbalance. This research underscores the feasibility of transitioning from traditional laboratory based diagnostics to more accessible, automated home based solutions, improving patient outcomes and broadening the accessibility of sleep disorder diagnostics.