• Erdenebayar Urtnasan, Jong Uk Park, Eun Yeon Joo, and Kyoung Joung Lee.
• Institute of AI and Big Data in Medicine, Wonju College of Medicine, Yonsei University, Wonju, Korea.
• J. Korean Med. Sci. 2020 Dec 7; 35 (47): e399e399.

BackgroundThis paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal.MethodsA convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer. An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects.ResultsF1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%.ConclusionThe results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.© 2020 The Korean Academy of Medical Sciences.

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