Obstructive  sleep  apnea  syndrome  detection  based  on ballistocardiogram via machine learning approach

Weidong Gao; Yibin Xu; Shengshu Li; Yujun Fu; Dongyang Zheng; Yingjia She; Weidong Gao; Yibin Xu; Shengshu Li; Yujun Fu; Dongyang Zheng; Yingjia She

doi:10.3934/mbe.2019282

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 5672-5686. doi: 10.3934/mbe.2019282

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Research article Special Issues

Obstructive sleep apnea syndrome detection based on ballistocardiogram via machine learning approach

1.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Haidian District, Beijing 100876, China
2.
Hainan Hospital of PLA General Hospital, China

Received: 24 March 2019 Accepted: 30 May 2019 Published: 18 June 2019

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.
- obstructive sleep apnea,
- HRV,
- classification,
- decision tree,
- Model fusion
Citation: Weidong Gao, Yibin Xu, Shengshu Li, Yujun Fu, Dongyang Zheng, Yingjia She. Obstructive sleep apnea syndrome detection based on ballistocardiogram via machine learning approach[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 5672-5686. doi: 10.3934/mbe.2019282

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Abstract

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.

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