Sleep Apnea Detection Model Using Time Window and One-Dimensional Convolutional Neural Network on Single-Lead Electrocardiogram - Pratama, Fadil; Wiharto, Wiharto; Salamah, Umi (Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA, 2025)
Sleep apnea is an important disorder that involves frequent disruptions in breathing during sleep, which can result in numerous serious health issues, such as cognitive deterioration, cardiovascular illness, and heightened mortality risk. This study introduces a detailed model designed for the detection of sleep apnea using single-lead electrocardiogram signals, providing an accurate detection method. We can use single-lead ECG signals to get ECG-Derived Respiration (EDR). EDR combines important respiratory signals with RR intervals to help find sleep apnea more accurately. We structure the research process into seven systematic stages, ensuring a comprehensive approach to the issue. The process commences with the acquisition of data from the