Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju-si, Gangwon-do 26493, Republic of Korea.
Physiol Meas. 2018 Jun 20;39(6):065003. doi: 10.1088/1361-6579/aac7b7.
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.
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.
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.
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.
本文提出了一种基于卷积神经网络（CNN）的深度学习架构，用于使用单导联心电图（ECG）记录对阻塞性睡眠呼吸暂停低通气（OSAH）进行多类分类。OSAH 是最常见的与睡眠相关的呼吸障碍。许多患有 OSAH 的患者未被诊断；因此，早期发现 OSAH 很重要。
本研究基于 CNN 对三类进行自动分类-正常、低通气和呼吸暂停。在训练数据集（45096 个事件）上训练最佳的六层 CNN 模型，并在测试数据集（11274 个事件）上进行评估。训练集（69 名受试者）和测试集（17 名受试者）从 86 名受试者中收集，长度约为 6 小时，并分段为 10 秒时长。
所提出的 CNN 模型在训练数据集上的平均[公式：见文本]-评分达到 93.0，在测试数据集上达到 87.0。
因此，所提出的深度学习架构在使用单导联 ECG 记录对 OSAH 进行多类分类方面取得了很高的性能。该方法可用于筛查疑似患有 OSAH 的患者。
