MS-Net: Sleep apnea detection in PPG using multi-scale block and shadow module one-dimensional convolutional neural network. (March 2023)

• Record Type: Journal Article
• Authors: Wei, Keming; Zou, Lang; Liu, Guanzheng; Wang, Changhong
• Is Part Of: Computers in biology and medicine. Volume 155(2023)
• Publication Date: 2023-03
• Subjects: Sleep Apnea (SA) -- Photoplethysmography (PPG) -- Multi-scale convolution -- Shadow module; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106469
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Sleep Apnea (SA) is a respiratory disorder that affects sleep. However, the SA detection method based on polysomnography is complex and not suitable for home use. The detection approach using Photoplethysmography is low cost and convenient, which can be used to widely detect SA. This study proposed a method combining a multi-scale one-dimensional convolutional neural network and a shadow one-dimensional convolutional neural network based on dual-channel input. The time-series feature information of different segments were extracted from multi-scale temporal structure. Moreover, shadow module was adopted to make full use of the redundant information generated after multi-scale convolution operation, which improved the accuracy and ensured the portability of the model. At the same time, we introduced balanced bootstrapping and class weight, which effectively alleviated the problem of unbalanced classes. Our method achieved the result of 82.0% average accuracy, 74.4% average sensitivity and 85.1% average specificity for per-segment SA detection, and reached 93.6% average accuracy for per-recording SA detection after 5-fold cross validation. Experimental results show that this method has good robustness. It can be regarded as an effective aid in SA detection in household use. Highlights: Combining a multi-scale one-dimensional convolutional neural network and a shadow one-dimensional convolutional neural network based on dual-channel input is proposed. The time-series feature information of different segments were extracted from multi-scale temporal structure. Shadow module was adopted to make full use of the redundant information generated. Balanced bootstrapping and class weight was introduced to effectively alleviate the problem of unbalanced classes.