Sleep Disorder Classification Using Convolutional Neural Networks

Abstract

Sleep disorders can cause many inconveniences, such as mental fatigue, poor concentration, emotional instability, memory loss, reduced work efficiency, and increased accident rates. Among them, obstructive sleep apnea is a common sleep disorder characterized by repeated apnea and snoring during nighttime, affecting sleep quality, daily life, and work. Therefore, predicting obstructive sleep apnea events can help people better identify and treat sleep disorders and improve quality of life and work efficiency. To enhance the performance of predicting obstructive apnea, we use the MIT-BIH polysomnographic database in this article. We used deep learning methods, specifically transfer learning, with the AlexNet framework to predict the outcome of OSA events. The results show that the best optimization algorithm is SGDM. The accuracy rate is 86.63%, the sensitivity is 92.20%, the precision is 90.55%, and the AUC is 91.95%. This study demonstrates the strong potential of using artificial intelligence techniques, specifically deep learning and transfer learning, to predict OSA events from ECG signals, which could provide valuable information for diagnosing and treating sleep disorders.