Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
Weipeng Xuan
Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
Ding Chen
Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
Yexin Gu
Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
Fuhai Liu
Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
Jinkai Chen
Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
Shudong Xia
The Fourth Affiliated Hospital Zhejiang University School of Medicine, Yiwu 322000, China
Shurong Dong
Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China
Jikui Luo
Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
Sleep apnea syndrome (SAS) is a common but underdiagnosed health problem related to impaired quality of life and increased cardiovascular risk. In order to solve the problem of complicated and expensive operation procedures for clinical diagnosis of sleep apnea, here we propose a small and low-cost wearable apnea diagnostic system. The system uses a photoplethysmography (PPG) optical sensor to collect human pulse wave signals and blood oxygen saturation synchronously. Then multiscale entropy and random forest algorithms are used to process the PPG signal for analysis and diagnosis of sleep apnea. The SAS determination is based on the comprehensive diagnosis of the PPG signal and blood oxygen saturation signal, and the blood oxygen is used to exclude the error induced by non-pathological factors. The performance of the system is compared with the Compumedics Grael PSG (Polysomnography) sleep monitoring system. This simple diagnostic system provides a feasible technical solution for portable and low-cost screening and diagnosis of SAS patients with a high accuracy of over 85%.
