Accurate Deep Learning-Based Sleep Apnea Detection from Cardiac Physiological Signals

Sleep apnea is a severe sleep disorder characterized by interrupted breathing during sleep. Early detection and accurate diagnosis of sleep apnea are essential to avoiding potentially dangerous health complications. This study proposes a sleep apnea detection model using cardiac physiological signals and a deep learning approach. This model uses Convolutional Neural Networks (CNN) to learn patterns and features in cardiac signal data. Heart signal data is collected from individuals with sleep apnea and normal sleeping individuals as datasets. Datasets consisting of heart signal data were gathered from patients diagnosed with sleep apnea and people with normal sleeping patterns. The public dataset was enhanced and utilized for training, employing a Convolutional Neural Network (CNN) architecture of six layers. The improved data is derived from the process of segmenting the data. The trained model demonstrated a high accuracy of 98.4% in effectively classifying physiological heart signal samples into two categories: “normal” and “sleep apnea”. This project aims to develop a model that can assist medical professionals in diagnosing sleep apnea and providing early warning to patients.