Multiscale Deep Neural Network for Obstructive Sleep Apnea Detection Using RR Interval From Single-Lead ECG Signal

Abstract

The detection of obstructive sleep apnea (OSA) based on single-lead electrocardiogram (ECG) is better suited to the noninvasive needs and hardware conditions of wearable mobile devices. From previous ECG-based OSA detection methods, we can find that deep learning methods have shown great potential and advantages. However, due to the nonstationarity of sympathetic nerve signals and the complex characteristics of heart rate variability (HRV), the neural network under a single scale cannot effectively capture the features of HRV. In this study, an OSA detection method based on a multiscale dilation attention 1-D convolutional neural network (MSDA-1DCNN) and a weighted-loss time-dependent (WLTD) classification model were proposed. The introduction of dilated convolution effectively balanced the relationship between model parameters and performance. Attention mechanism technology modified the multiscale features after fusion and improved the weight of features under important channels. In the final classification part of the network, the combination of weighted cross-entropy loss function and hidden Markov model effectively alleviated the problem of data imbalance and improved the classification accuracy of the classifier. In segment identification, the accuracy, sensitivity, and specificity of the proposed method are 89.4%, 89.8%, and 89.1%, respectively; as for individual identification, the accuracy of that achieved 100%. The results demonstrated that the method proposed in this study can identify sleep apnea accurately.