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    Deep Learning for Obstructive Sleep Apnea Detection and Severity Assessment: A Multimodal Signals Fusion Multiscale Transformer Model by Zhang Y, Zhou L, Zhu S, Zhou Y, Wang Z, Ma L, Yuan Y, Xie Y, Niu X, Su Y, Liu H, Hei X, Shi Z, Ren X, Shi Y

    Published 2025-01-01
    “…Yitong Zhang,1 Liang Zhou,2 Simin Zhu,1 Yanuo Zhou,1 Zitong Wang,1 Lina Ma,1 Yuqi Yuan,1 Yushan Xie,1 Xiaoxin Niu,1 Yonglong Su,1 Haiqin Liu,1 Xinhong Hei,2 Zhenghao Shi,2 Xiaoyong Ren,1 Yewen Shi1 1Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, People’s Republic of China; 2School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, Shaanxi Province, People’s Republic of ChinaCorrespondence: Xiaoyong Ren; Yewen Shi, Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Address: NO. 157 Xi Wu Road, Xi’an, Shaanxi Province, Email cor_renxiaoyong@126.com; shiyewen59@outlook.comPurpose: To develop a deep learning (DL) model for obstructive sleep apnea (OSA) detection and severity assessment and provide a new approach for convenient, economical, and accurate disease detection.Methods: Considering medical reliability and acquisition simplicity, we used electrocardiogram (ECG) and oxygen saturation (SpO2) signals to develop a multimodal signal fusion multiscale Transformer model for OSA detection and severity assessment. …”
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