Deep Learning for Diagnosis and Classification of Obstructive Sleep Apnea: A Nasal Airflow-Based Multi-Resolution Residual Network

Abstract

Huijun Yue,1,* Yu Lin,1,* Yitao Wu,2,* Yongquan Wang,1 Yun Li,1 Xueqin Guo,1 Ying Huang,3 Weiping Wen,1 Gansen Zhao,2 Xiongwen Pang,2 Wenbin Lei1 1Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China; 2School of Computer Science, South China Normal University, Guangzhou, 510631, People’s Republic of China; 3Guangdong Province Traditional Chinese Medical Hospital, Guangzhou, 510000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wenbin LeiOtorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Road II, Guangzhou, Guangdong, 510080, People’s Republic of ChinaTel [phone]Email [email protected] PangSchool of Computer Science, South China Normal University, 55 West Zhongshan Avenue, Guangzhou, Guangdong, 510631, People’s Republic of ChinaTel [phone]Email [email protected]: This study evaluated a novel approach for diagnosis and classification of obstructive sleep apnea (OSA), called Obstructive Sleep Apnea Smart System (OSASS), using residual networks and single-channel nasal pressure airflow signals.Methods: Data were collected from the sleep center of the First Affiliated Hospital, Sun Yat-sen University, and the Integrative Department of Guangdong Province Traditional Chinese Medical Hospital. We developed a new model called the multi-resolution residual network (Mr-ResNet) based on a residual network to detect nasal pressure airflow signals recorded by polysomnography (PSG) automatically. The performance of the model was assessed by its sensitivity, specificity, accuracy, and F1-score. We built OSASS based on Mr-ResNet to estimate the apnea‒hypopnea index (AHI) and to classify the severity of OSA, and compared the agreement between OSASS output and the registered polysomnographic technologist (RPSGT) score, assessed by two technologists.Results: In the primary test set, the sensitivity, specificity, accuracy, and F1-score of Mr-ResNet were 90.8%, 90.5%, 91.2%, and 90.5%, respectively. In the independent test set, the Spearman correlation for AHI between OSASS and the RPSGT score determined by two technologists was 0.94 (p < 0.001) and 0.96 (p < 0.001), respectively. Cohen’s Kappa scores for classification between OSASS and the two technologists’ scores were 0.81 and 0.84, respectively.Conclusion: Our results indicated that OSASS can automatically diagnose and classify OSA using signals from a single-channel nasal pressure airflow, which is consistent with polysomnographic technologists’ findings. Thus, OSASS holds promise for clinical application.Keywords: deep learning, nasal airflow, obstructive sleep apnea, residual network

Keywords

• deep learning
• nasal airflow
• obstructive sleep apnea
• residual network