A fine-grained convolutional recurrent model for obstructive sleep apnea detection

Obstructive Sleep Apnea (OSA) is a prevalent sleep-related breathing disorder that leads to various health issues such as hypertension, heart disease, diabetes, and stroke. In order to achieve a convenient, robust and accurate OSA detection, we analyze the cardiopulmonary coupling mechanism of OSA f...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics 2024-07, Vol.15 (7), p.3043-3056
Main Authors: Zhang, Enming, Yao, Yuan, Zhou, Nan, Chen, Yu, Zhang, Haibo, Guo, Jinhong, Teng, Fei
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Approximation
Artificial Intelligence
Blood pressure
Circadian rhythm
Classification
Clinical medicine
Complex Systems
Computational Intelligence
Control
Coupling
Deep learning
Electrocardiography
Engineering
Fourier transforms
Heart diseases
Heart rate
Hypertension
Machine learning
Mechatronics
Nervous system
Neural networks
Original Article
Pattern Recognition
Performance evaluation
Physiology
Respiration
Robotics
Sleep apnea
Support vector machines
Systems Biology
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Summary:Obstructive Sleep Apnea (OSA) is a prevalent sleep-related breathing disorder that leads to various health issues such as hypertension, heart disease, diabetes, and stroke. In order to achieve a convenient, robust and accurate OSA detection, we analyze the cardiopulmonary coupling mechanism of OSA from single-lead electrocardiogram (ECG) signals. Then we propose a fine-grained convolutional recurrent model (FCRM) for obstructive sleep apnea detection to learn the variation of cardiopulmonary coupling (CPC) features for OSA detection. Finally, we offer interpretable insights into the model’s decisions using respiration signal and achieve fine-grained apnea classification based on attention score. The proposed model’s performance on the Apnea-ECG dataset achieved 93.2% accuracy, 89.2% sensitivity, and 96.4% specificity. This demonstrates that the method effectively extracts cardiopulmonary characteristics during sleep apnea and outperforms other methods.
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-023-02080-5