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 China

Correspondence: 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.com

Purpose: 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. The proposed model comprises signal preprocessing, feature extraction, cross-modal interaction, and classification modules. A total of 510 patients who underwent polysomnography were included in the hospital dataset. The model was tested on hospital and public datasets. The hospital dataset was utilized to demonstrate the applicability and generalizability of the model. Two public datasets, Apnea-ECG dataset (consisting of 8 recordings) and UCD dataset (consisting of 21 recordings), were used to compare the results with those of previous studies.
Results: In the hospital dataset, the accuracy (Acc) values of per-segment and per-recording detection were 91.38 and 96.08%, respectively. The Acc values for mild, moderate, and severe OSA were 90.20, 88.24, and 92.16%, respectively. The Bland‒Altman plots revealed the consistency of the true apnea–hypopnea index (AHI) and the predicted AHI. In the public datasets, the per-segment detection Acc values of the Apnea-ECG and UCD datasets were 95.04 and 90.56%, respectively.
Conclusion: The experiments on hospital and public datasets have demonstrated that the proposed model is more advanced, accurate, and applicable in OSA detection and severity assessment than previous models.

Keywords: obstructive sleep apnea, multimodal signals fusion, deep learning, detection model