Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Fukushima, Japan.
Division of Cardiovascular Medicine, Ohashi Medical Center, Toho University, Meguro, Tokyo, Japan.
PLoS One. 2021 Apr 26;16(4):e0250618. doi: 10.1371/journal.pone.0250618. eCollection 2021.
Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during sleep and is associated with many other medical conditions, including hypertension, coronary heart disease, and depression. Clinically, the standard for diagnosing OSA involves nocturnal polysomnography (PSG). However, this requires expert human intervention and considerable time, which limits the availability of OSA diagnosis in public health sectors. Therefore, electrocardiogram (ECG)-based methods for OSA detection have been proposed to automate the polysomnography procedure and reduce its discomfort. So far, most of the proposed approaches rely on feature engineering, which calls for advanced expert knowledge and experience. This paper proposes a novel fused-image-based technique that detects OSA using only a single-lead ECG signal. In the proposed approach, a convolutional neural network extracts features automatically from images created with one-minute ECG segments. The proposed network comprises 37 layers, including four residual blocks, a dense layer, a dropout layer, and a soft-max layer. In this study, three time-frequency representations, namely the scalogram, the spectrogram, and the Wigner-Ville distribution, were used to investigate the effectiveness of the fused-image-based approach. We found that blending scalogram and spectrogram images further improved the system's discriminative characteristics. Seventy ECG recordings from the PhysioNet Apnea-ECG database were used to train and evaluate the proposed model using 10-fold cross validation. The results of this study demonstrated that the proposed classifier can perform OSA detection with an average accuracy, recall, and specificity of 92.4%, 92.3%, and 92.6%, respectively, for the fused spectral images.
阻塞性睡眠呼吸暂停（OSA）是一种常见的慢性睡眠障碍，会在睡眠期间扰乱呼吸，并且与许多其他医学病症相关，包括高血压、冠心病和抑郁症。临床上，诊断 OSA 的标准涉及夜间多导睡眠图（PSG）。然而，这需要专家的人工干预和大量时间，这限制了公共卫生部门进行 OSA 诊断的可用性。因此，已经提出了基于心电图（ECG）的 OSA 检测方法，以自动化多导睡眠图程序并减少其不适。到目前为止，大多数提出的方法都依赖于特征工程，这需要先进的专家知识和经验。本文提出了一种新颖的基于融合图像的技术，仅使用单导联 ECG 信号即可检测 OSA。在提出的方法中，卷积神经网络自动从一分钟 ECG 段创建的图像中提取特征。所提出的网络包含 37 层，包括四个残差块、密集层、辍学层和 soft-max 层。在这项研究中，使用了三种时频表示，即谱图、声谱图和维格纳-维尔分布，以研究基于融合图像的方法的有效性。我们发现，融合谱图和声谱图图像进一步提高了系统的判别特征。使用 PhysioNet Apnea-ECG 数据库中的 70 个 ECG 记录，通过 10 倍交叉验证，使用该模型进行训练和评估。这项研究的结果表明，所提出的分类器可以使用融合谱图像实现平均准确率、召回率和特异性分别为 92.4%、92.3%和 92.6%的 OSA 检测。
