Obstructive sleep apnea (OSA) is a common sleep-related respiratory disorder. Around the world, more and more people are suffering from OSA. Because of the limitation of monitor equipment, many people with OSA remain undetected. Therefore, we propose a sleep-monitoring model based on single-channel electrocardiogram using a convolutional neural network (CNN), which can be used in portable OSA monitor devices. To learn different scale features, the first convolution layer comprises three types of filters. The long short-term memory (LSTM) is used to learn the long-term dependencies such as the OSA transition rules. The softmax function is connected to the final fully connected layer to obtain the final decision. To detect a complete OSA event, the raw ECG signals are segmented by a 10 s overlapping sliding window. The proposed model is trained with the segmented raw signals and is subsequently tested to evaluate its event detection performance. According to experiment analysis, the proposed model exhibits Cohen’s kappa coefficient of 0.92, a sensitivity of 96.1%, a specificity of 96.2%, and an accuracy of 96.1% with respect to the Apnea-ECG dataset. The proposed model is significantly higher than the results from the baseline method. The results prove that our approach could be a useful tool for detecting OSA on the basis of a single-lead ECG.

中文翻译：

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使用深度 CNN-LSTM 模型自动检测阻塞性睡眠呼吸暂停事件

阻塞性睡眠呼吸暂停（OSA）是一种常见的与睡眠相关的呼吸系统疾病。在世界各地，越来越多的人患有 OSA。由于监测设备的限制，许多患有 OSA 的人仍未被发现。因此，我们提出了一种使用卷积神经网络（CNN）的基于单通道心电图的睡眠监测模型，可用于便携式 OSA 监测设备。为了学习不同的尺度特征，第一个卷积层包含三种类型的滤波器。长短期记忆 (LSTM) 用于学习长期依赖性，例如 OSA 转换规则。softmax函数连接到最终的全连接层以获得最终的决策。为了检测完整的 OSA 事件，原始心电图信号通过 10 秒重叠滑动窗口进行分段。所提出的模型使用分段的原始信号进行训练，并随后进行测试以评估其事件检测性能。根据实验分析，该模型相对于 Apnea-ECG 数据集的 Cohen kappa 系数为 0.92，敏感性为 96.1%，特异性为 96.2%，准确性为 96.1%。所提出的模型明显高于基线方法的结果。结果证明，我们的方法可能是基于单导联心电图检测 OSA 的有用工具。