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Physiol Meas. 2018 Jun 20;39(6):065003. doi: 10.1088/1361-6579/aac7b7.

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

Author information

1
Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju-si, Gangwon-do 26493, Republic of Korea.

Abstract

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 [Formula: see text]-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.

PMID:
29794342
DOI:
10.1088/1361-6579/aac7b7
[Indexed for MEDLINE]

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