Abstract
Sleep apnea, a prevalent respiratory disorder, poses significant health risks, including cardiovascular complications and behavioral issues, if left untreated. Traditional diagnostic methods like polysomnography, although effective, are often expensive and inconvenient. SleepNet addresses these issues by introducing a new multimodal approach tailored for precise sleep apnea detection. At its core, the framework utilizes a fusion of one-dimensional convolutional neural networks (1D-CNN) and bidirectional gated recurrent units (Bi-GRU) to analyze single-lead electrocardiogram (ECG) recordings, yielding an accuracy of 95.08%. When the model is enriched with additional physiological signals-namely nasal airflow and abdominal respiratory effort-the performance further rises modestly to 95.19%. This multimodal strategy surpasses the performance of existing unimodal approaches, yielding enhanced sensitivity and specificity rates of 96.12% and 93.45%, respectively. When compared to previous studies, SleepNet represents a substantial leap forward in diagnostic efficacy, showcasing the transformative potential of integrating multiple data streams for sleep apnea detection. The results highlight the promise of deep learning methodologies in advancing this domain and lay a robust foundation for subsequent research.