Key result
CNN using single-lead ECG achieves an 87.0 F-score for classifying normal, hypopnea, and apnea events.
Why the study?
Does a CNN-based deep learning architecture accurately classify obstructive sleep apnea and hypopnea using single-lead ECG recordings?
Does a CNN-based deep learning architecture accurately classify obstructive sleep apnea and hypopnea using single-lead ECG recordings?
A CNN-based deep learning model can achieve high performance in classifying obstructive sleep apnea and hypopnea using single-lead ECG recordings, offering a potential automated screening tool.
May support single-lead ECG screening for sleep apnea; leaves open prospective validation before clinical adoption.
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.
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Urtnasan et al. (2018) studied obstructive sleep apnea and hypopnea (OSAH) (n=86). Convolutional neural network (CNN) using single-lead ECG was evaluated on mean F-score for multiclass classification on the test dataset. A convolutional neural network using single-lead ECG achieved a mean F-score of 87.0 on the test dataset for the multiclass classification of normal, hypopnea, and apnea events.
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