Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram
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...
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Published in | Physiological measurement Vol. 39; no. 6; pp. 65003 - 65011 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
England
IOP Publishing
20.06.2018
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Subjects | |
Online Access | Get full text |
ISSN | 0967-3334 1361-6579 1361-6579 |
DOI | 10.1088/1361-6579/aac7b7 |
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Summary: | 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. |
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Bibliography: | Institute of Physics and Engineering in Medicine PMEA-102424.R2 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0967-3334 1361-6579 1361-6579 |
DOI: | 10.1088/1361-6579/aac7b7 |