Obstructive Sleep Apnea Detection Scheme Based on Manually Generated Features and Parallel Heterogeneous Deep Learning Model Under IoMT

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular and cerebrovascular diseases that seriously affect the lives and health of people. The development of Internet of Medical Things (IoMT) has enabled the remote diagnosis of OSA. The physiological signa...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2022-12, Vol.26 (12), p.5841-5850
Main Authors: Shao, Shiliang, Han, Guangjie, Wang, Ting, Song, Chunhe, Yao, Chen, Hou, Jianxia
Format: Article
Language:English
Subjects:
Accuracy
Apnea
Cerebrovascular diseases
Deep Learning
Diagnosis
Diagnostic systems
EKG
Electrocardiography
Electrocardiography - methods
Feature extraction
Frequency spectrum
Heart rate
Heart rate variability
Humans
Internet of Things
manually generated features
Medical imaging
obstructive sleep apnea
Parallel heterogeneous deep learning
Recognition
Sleep
Sleep apnea
Sleep Apnea, Obstructive - diagnosis
Sleep disorders
Vascular diseases
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Summary:Obstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular and cerebrovascular diseases that seriously affect the lives and health of people. The development of Internet of Medical Things (IoMT) has enabled the remote diagnosis of OSA. The physiological signals of human sleep are sent to the cloud or medical facilities through Internet of Things, after which diagnostic models are employed for OSA detection. In order to improve the detection accuracy of OSA, in this study, a novel OSA detection system based on manually generated features and utilizing a parallel heterogeneous deep learning model in the context of IoMT is proposed, and the accuracy of the proposed diagnostic model is investigated. The OSA recognition scheme used in our model is based on short-term heart rate variability (HRV) signals extracted from ECG signals. First, the HRV signals and the linear and nonlinear features of HRV are combined into a one-dimensional (1-D) sequence. Simultaneously, a two-dimensional (2-D) HRV time-frequency spectrum image is obtained. The 1-D data sequences and 2-D images are coded in different branches of the proposed deep learning network for OSA diagnosis. To validate the performance of the proposed scheme, the Physionet Apnea-ECG public database is used. The proposed scheme outperforms the existing methods in terms of accuracy and provides a novel direction for OSA recognition.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2022.3166859