多尺度卷积神经网络检测睡眠呼吸暂停

睡眠呼吸暂停综合征作为一种常见的与睡眠相关的呼吸障碍性疾病,受到众多的关注。由于其复杂的检诊断过程及昂贵的价格,吸引了众多研究学者探索基于单通道信号的快速、便捷检测方法。基于心电信号(ECG)提出了一种多尺度卷积神经网络睡眠呼吸暂停快速检测方法,与常规的单尺度卷积神经网络方法相比,该方法可以有效地结合信号的细节信息和抽象信息,提升卷积神经网络的特征呈现能力。通过PhysioNet提供的Apnea-ECG数据库进行验证,多尺度卷积神经网络获得了85.2%准确率、83.1%敏感性和86.5%特异性。与现有方法相比,该方法进一步提升了睡眠呼吸暂停的检测性能。

Multi-scale convolutional neural network for sleep apnea detection

Sleep apnea syndrome, as a common sleep-related respiratory disorder, has gained a lot of attention. Due to its complicated diagnosis process and high price, it has attracted many researchers to explore fast and convenient detection methods based on singlechannel signals. The research proposes a multi-scale convolutional neural network method for rapid detection of sleep apnea based on ECG signals. Compared with the traditional single-scale convolutional neural network, the method can effectively combine the detailed and abstract information of the signal, and improve the feature representation ability of the convolutional neural network. By verifying on the Apnea-ECG database provided by PhysioNet, the proposed multi-scale convolutional neural network obtains an accuracy of 85. 2%, sensitivity of 83. 1% and specificity of 86. 5%. Compared with existing methods, the method further improves the performance of sleep apnea detection.