基于卷积神经网络的睡眠呼吸暂停自动检测方法

Sleep apnea automatic detection method based on convolutional neural network

传统基于机器学习的睡眠呼吸暂停(SA)检测方法,需花大量工作在特征工程与分类器设计上。本文提出了一种基于卷积神经网络(CNN)的SA自动检测方法,构建了一个包含4个卷积层、4个池化层、2个全连接层和1个分类层的一维CNN网络模型,通过网络自身结构实现特征自动提取与分类。利用呼吸暂停-心电图(Apnea-ECG)数据库中70例整晚单通道睡眠心电图(ECG)数据对该方法进行了验证,通过对比实验发现当输入为单通道ECG信号、RR间期(RRI)序列、R峰值序列、RRI序列+R峰值序列四种情况时,网络在SA片段检测上的准确率为80.1%~88.0%,表明该CNN网络是有效的,能从原始单通道ECG信号或其派生信号RRI、R峰值序列中自动提取特征并分类。当网络输入为RRI序列+R峰值序列时效果最好,在片段SA检测上的准确率、灵敏度和特异度分别为88.0%、85.1%和89.9%,个体SA诊断准确率达100%。研究结果表明,本文提出的方法能有效提高SA检测的准确性和鲁棒性,且性能优于近年主要文献报道,有望应用于配备远程服务器的便携式SA筛查诊断设备中。

Sleep apnea(SA)detection method based on traditional machine learning needs a lot of efforts in feature engineering and classifier design.We constructed a one-dimensional convolutional neural network(CNN)model,which consists in four convolution layers,four pooling layers,two full connection layers and one classification layer.The automatic feature extraction and classification were realized by the structure of the proposed CNN model.The model was verified by the whole night single-channel sleep electrocardiogram(ECG)signals of 70 subjects from the Apnea-ECG dataset.Our results showed that the accuracy of per-segment SA detection was ranged from 80.1%to 88.0%,using the input signals of single-channel ECG signal,RR interval(RRI)sequence,R peak sequence and RRI sequence+R peak sequence respectively.These results indicated that the proposed CNN model was effective and can automatically extract and classify features from the original single-channel ECG signal or its derived signal RRI and R peak sequence.When the input signals were RRI sequence+R peak sequence,the CNN model achieved the best performance.The accuracy,sensitivity and specificity of per-segment SA detection were 88.0%,85.1%and 89.9%,respectively.And the accuracy of per-recording SA diagnosis was 100%.These findings indicated that the proposed method can effectively improve the accuracy and robustness of SA detection and outperform the methods reported in recent years.The proposed CNN model can be applied to portable screening diagnosis equipment for SA with remote server.