A novel obstructive sleep apnea detection model based on multi-scale convolutional neural networks

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repeated brief interruptions of breathing during sleep. The existing methods of OSA detection using polysomnography (PSG) are inefficient and costly, and therefore the computer-aided diagnosis has become an important approach to improve the diagnostic efficiency and effectiveness. In this paper, we design a novel deep learning OSA detection model based on electrocardiograms (ECGs). Firstly, we introduce a multi-scale analysis framework for ECG signals, leveraging matrix profile-derived Euclidean distance spectra and a centralized cropping strategy to extract salient features at 1-minute, 3-minute, and 5-minute scales for robust model inputs. Secondly, a deep learning model for the OSA recognition is designed based on multi-scale convolutional neural networks, incorporating the residual attention and the channel attention to address vanishing and exploding gradients, focusing on key features of the ECG signals. Moreover, we design three different convolutional neural network (CNN) modules to process ECG signals with various segment lengths. Experimental results show that the accuracy of the segment-based OSA detection reaches 92.82% and 90.73% on two public datasets, respectively, 1.89% and 7.28% higher than the state-of-the-art models.