A Sleep Apnea Detection System Based on a One-Dimensional Deep Convolution Neural Network Model Using Single-Lead Electrocardiogram

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 15; p. 4157
• Main Authors: Chang, Hung-Yu, Yeh, Cheng-Yu, Lee, Chung-Te, Lin, Chun-Cheng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.07.2020; MDPI
• Subjects: convolutional neural network; Datasets; deep learning; Electrocardiography; Humans; Neural networks; Neural Networks, Computer; obstructive sleep apnea; Polysomnography; Respiration; single-lead electrocardiogram; Sleep apnea; Sleep Apnea Syndromes - diagnosis; obstructive sleep apnea; deep learning; single-lead electrocardiogram; convolutional neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20154157

Many works in recent years have been focused on developing a portable and less expensive system for diagnosing patients with obstructive sleep apnea (OSA), instead of using the inconvenient and expensive polysomnography (PSG). This study proposes a sleep apnea detection system based on a one-dimensional (1D) deep convolutional neural network (CNN) model using the single-lead 1D electrocardiogram (ECG) signals. The proposed CNN model consists of 10 identical CNN-based feature extraction layers, a flattened layer, 4 identical classification layers mainly composed of fully connected networks, and a softmax classification layer. Thirty-five released and thirty-five withheld ECG recordings from the MIT PhysioNet Apnea-ECG Database were applied to train the proposed CNN model and validate its accuracy for the detection of the apnea events. The results show that the proposed model achieves 87.9% accuracy, 92.0% specificity, and 81.1% sensitivity for per-minute apnea detection, and 97.1% accuracy, 100% specificity, and 95.7% sensitivity for per-recording classification. The proposed model improves the accuracy of sleep apnea detection in comparison with several feature-engineering-based and feature-learning-based approaches.