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

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Abstract

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.

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Data availability

The data that support the findings of this study are openly available in Apnea ECG database at https://www.physionet.org/content/apnea-ecg/1.0.0/.

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Author notes

  1. Enming Zhang and Yuan Yao have contributed equally to this work and should be regarded as co-first authors.

Authors and Affiliations

  1. Southwest Jiaotong University, Chengdu, 610031, China

    Enming Zhang, Nan Zhou, Yu Chen & Fei Teng

  2. West China Hospital of Sichuan University, Chengdu, 610041, China

    Yuan Yao

  3. University of Otago, Dunedin, 9016, New Zealand

    Haibo Zhang

  4. Shanghai Jiao Tong University, Shanghai, 200240, China

    Jinhong Guo

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  1. Enming Zhang
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  2. Yuan Yao
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  4. Yu Chen
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Correspondence to Fei Teng.

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Zhang, E., Yao, Y., Zhou, N. et al. A fine-grained convolutional recurrent model for obstructive sleep apnea detection. Int. J. Mach. Learn. & Cyber. 15, 3043–3056 (2024). https://doi.org/10.1007/s13042-023-02080-5

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  • DOI: https://doi.org/10.1007/s13042-023-02080-5

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