Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram
Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram
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DOI：
10.1016/j.cmpb.2019.105001
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发表时间：
2019-10-01
影响因子：
6.1
通讯作者：
Lee, Kyoung-Joung
中科院分区：
文献类型：
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作者：
Erdenebayar, Urtnasan;Kim, Yoon Ji;Lee, Kyoung-Joung
背景和目的：本研究展示了深度学习方法，旨在找到从心电图（ECG）信号中自动检测睡眠呼吸暂停（SA）事件的最佳方法。方法：设计并实现了六种深度学习方法用于SA事件的自动检测，包括深度神经网络（DNN），一维（1D）卷积神经网络（CNN），二维（2D）CNN，递归神经网络（RNN），长短期记忆和门控循环单位（GRU）。对设计的深度学习模型进行了性能分析和比较。对ECG信号进行预处理、标准化并分段为10 s间隔。随后，将信号转换为2D形式，以在2D CNN模型中进行分析。使用了从86例SA患者中收集的数据集。训练集包含来自69名患者的数据，而测试集包含来自其余17名患者的数据。结果：表现最好的模型的准确率为99.0%，一维CNN和GRU模型的召回率为99.0%。设计的深度学习方法在检测SA事件方面比以前的研究中开发和测试的方法表现得更好，他们可以通过心电图信号区分呼吸暂停和呼吸不足。1D CNN和GRU等深度学习方法可以成为在睡眠呼吸暂停筛查和相关研究中自动检测SA的有用工具。(C)2019爱思唯尔B. V.保留所有权利。
Background and Objective: This study demonstrates deep learning approaches with an aim to find the optimal method to automatically detect sleep apnea (SA) events from an electrocardiogram (ECG) signal.Methods: Six deep learning approaches were designed and implemented for automatic detection of SA events including deep neural network (DNN), one-dimensional (1D) convolutional neural networks (CNN), two-dimensional (2D) CNN, recurrent neural networks (RNN), long short-term memory, and gated-recurrent unit (GRU). Designed deep learning models were analyzed and compared in the performances. The ECG signal was pre-processed, normalized, and segmented into 10 s intervals. Subsequently, the signal was converted into a 2D form for analysis in the 2D CNN model. A dataset collected from 86 patients with SA was used. The training set comprised data from 69 of the patients, while the test set contained data from the remaining 17 patients.Results: The accuracy of the best-performing model was 99.0%, and the 1D CNN and GRU models had 99.0% recall rates.Conclusions: The designed deep learning approaches performed better than those developed and tested in previous studies in terms of detecting SA events, and they could distinguish between apnea and hypopnea events using an ECG signal. The deep learning approaches such as 1D CNN and GRU can be helpful tools to automatically detect SA in sleep apnea screening and related studies. (C) 2019 Elsevier B.V. All rights reserved.