Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG |
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| Authors: | Erdenebayar Urtnasan Jong-Uk Park Eun Yeon Joo Kyoung Joung Lee |
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| Affiliation: | 1.Institute of AI and Big Data in Medicine, Wonju College of Medicine, Yonsei University, Wonju, Korea.;2.Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Korea.;3.Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, Korea. |
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| Abstract: | 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. |
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| Keywords: | Automatic Prediction Sleep Apnea Short-term Normal ECG Convolutional Neural Network Deep Learning |
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