Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram

• Published in: Physiological measurement Vol. 39; no. 6; pp. 65003 - 65011
• Main Authors: Urtnasan, Erdenebayar, Park, Jong-Uk, Lee, Kyoung-Joung
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 20.06.2018
• Subjects: convolutional neural network (CNN); deep learning; obstructive sleep apnea and hypopnea (OSAH); single-lead electrocardiogram (ECG)
• ISSN: 0967-3334; 1361-6579; 1361-6579
• DOI: 10.1088/1361-6579/aac7b7

Objective: In this paper, we propose a convolutional neural network (CNN)-based deep learning architecture for multiclass classification of obstructive sleep apnea and hypopnea (OSAH) using single-lead electrocardiogram (ECG) recordings. OSAH is the most common sleep-related breathing disorder. Many subjects who suffer from OSAH remain undiagnosed; thus, early detection of OSAH is important. Approach: In this study, automatic classification of three classes-normal, hypopnea, and apnea-based on a CNN is performed. An optimal six-layer CNN model is trained on a training dataset (45 096 events) and evaluated on a test dataset (11 274 events). The training set (69 subjects) and test set (17 subjects) were collected from 86 subjects with length of approximately 6 h and segmented into 10 s durations. Main results: The proposed CNN model reaches a mean -score of 93.0 for the training dataset and 87.0 for the test dataset. Significance: Thus, proposed deep learning architecture achieved a high performance for multiclass classification of OSAH using single-lead ECG recordings. The proposed method can be employed in screening of patients suspected of having OSAH.