Affiliations: [a] The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China | [b] National Engineering Research Center for Healthcare Devices Guangzhou, Guangdong, China | [c] The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, Shaanxi, China | [d] The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
Correspondence:
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Corresponding author: Jin Li, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China. E-mail: jin.l@mail.xjtu.edu.cn.
Abstract: BACKGROUND: Simplified and easy-to-use monitoring approaches are crucial for the early diagnosis and prevention of obstructive sleep apnea (OSA) and its complications. OBJECTIVE: In this study, the OSA detection and arrhythmia classification algorithms based on single-channel photoplethysmography (PPG) are proposed for the early screening of OSA. METHODS: Thirty clinically diagnosed OSA patients participated in this study. Fourteen features were extracted from the PPG signals. The relationship between the number of features as inputs of the support vector machine (SVM) and performance of apnea events detection was evaluated. Also, a multi-classification algorithm based on the modified Hausdorff distance was proposed to recognize sinus rhythm and four arrhythmias highly related with SA. RESULTS: The feature set composed of meanPP, SDPP, RMSSD, meanAm, and meank1 could provide a satisfactory balance between the performance and complexity of the algorithm for OSA detection. Also, the arrhythmia classification algorithm achieves the average sensitivity, specificity and accuracy of 83.79%, 95.91% and 93.47%, respectively in the classification of all four types of arrhythmia and regular rhythm. CONCLUSION: Single channel PPG-based OSA detection and arrhythmia classification in this study can provide a feasible and promising approach for the early screening and diagnosis of OSA and OSA-related arrhythmias.
