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Open AccessArticle

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

1
Heart Center, Cheng Hsin General Hospital, Taipei 112, Taiwan
2
Faculty of Medicine, School of Medicine, National Yang Ming University, Taipei 112, Taiwan
3
Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4157; https://doi.org/10.3390/s20154157
Received: 24 June 2020 / Revised: 17 July 2020 / Accepted: 24 July 2020 / Published: 26 July 2020
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2020)
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. View Full-Text
Keywords: obstructive sleep apnea; single-lead electrocardiogram; deep learning; convolutional neural network obstructive sleep apnea; single-lead electrocardiogram; deep learning; convolutional neural network
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Chang, H.-Y.; Yeh, C.-Y.; Lee, C.-T.; Lin, C.-C. A Sleep Apnea Detection System Based on a One-Dimensional Deep Convolution Neural Network Model Using Single-Lead Electrocardiogram. Sensors 2020, 20, 4157.

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