Gao Wei Dong, Xu Yi Bin, Li Sheng Shu, Fu Yu Jun, Zheng Dong Yang, She Ying Jia
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Haidian District, Beijing 100876, China.
Hainan Hospital of PLA General Hospital, China.
Math Biosci Eng. 2019 Jun 19;16(5):5672-5686. doi: 10.3934/mbe.2019282.
Obstructive sleep apnea (OSA) is a common sleep-related respiratory disease that affects people's health, especially in the elderly. In the traditional PSG-based OSA detection, people's sleep may be disturbed, meanwhile the electrode slices are easily to fall off. In this paper, we study a sleep apnea detection method based on non-contact mattress, which can detect OSA accurately without disturbing sleep. Piezoelectric ceramics sensors are used to capture pressure changes in the chest and abdomen of the human body. Then heart rate and respiratory rate are extracted from impulse waveforms and respiratory waveforms that converted by filtering and processing of the pressure signals. Finally, the Heart Rate Variability (HRV) is obtained by processing the obtained heartbeat signals. The features of the heartbeat interval signal and the respiratory signal are extracted over a fixed length of time, wherein a classification model is used to predict whether sleep apnea will occur during this time interval. Model fusion technology is adopted to improve the detection accuracy of sleep apnea. Results show that the proposed algorithm can be used as an effective method to detect OSA.
阻塞性睡眠呼吸暂停（OSA）是一种常见的与睡眠相关的呼吸系统疾病，会影响人们的健康，尤其是老年人。在传统的基于多导睡眠图（PSG）的OSA检测中，人们的睡眠可能会受到干扰，同时电极片也容易脱落。在本文中，我们研究了一种基于非接触式床垫的睡眠呼吸暂停检测方法，该方法可以在不干扰睡眠的情况下准确检测OSA。使用压电陶瓷传感器捕获人体胸部和腹部的压力变化。然后从通过对压力信号进行滤波和处理而转换得到的脉冲波形和呼吸波形中提取心率和呼吸率。最后，通过对获得的心跳信号进行处理得到心率变异性（HRV）。在固定的时间段内提取心跳间隔信号和呼吸信号的特征，其中使用分类模型预测在此时间间隔内是否会发生睡眠呼吸暂停。采用模型融合技术提高睡眠呼吸暂停的检测精度。结果表明，所提出的算法可作为检测OSA的有效方法。
