Journal
JOURNAL OF MEDICAL SYSTEMS
Volume 42, Issue 6, Pages -Publisher
SPRINGER
DOI: 10.1007/s10916-018-0963-0
Keywords
Obstructive sleep apnea; Single-lead ECG; Convolutional neural network
Funding
- National Research Foundation of Korea (NRF) - Ministry of Education
- Local Innovative Creative Human Resource Training Project [NRF-2014H1C1A1063845]
- National Research Foundation of Korea [31Z20130012973] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F-1-score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.
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