Pre-Emptive Sleep Apnea Detection Using Deep Learning

Upper airway blockage, either partial or total, occurs repeatedly during sleep causes a dysfunction known as obstructive sleep apnea (OSA). This problem can cause disrupted sleep patterns and have major health effects, such as cardiovascular diseases. Current diagnostic methods for OSA, such as polysomnography (PSG), are expensive, cumbersome, and often require overnight hospital stays, limiting their accessibility and scalability. In order to aid in the diagnosis of OSA, a deep learning-based method for classifying single lead electrocardiogram (ECG) data is presented in this paper. The suggested model accurately classifies ECG signals as normal, mild, moderate, and severe by using convolutional neural networks (CNNs) to automatically learn discriminative characteristics from raw ECG data. The model was trained on a large annotated dataset, and evaluation was conducted using various performance metrics to assess classification accuracy and generalization performance. Furthermore, the design and implementation of a user-friendly website presented to facilitate the deployment and utilization of the OSA detection system in clinical and home settings. The website allows users to upload their ECG recordings and access diagnostic reports. The proposed OSA detection system holds promise as a cost-effective, and scalable solution for the early diagnosis of OSA, thereby improving patient outcomes and reducing healthcare burden.