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

Autor: Sheng Shu Li, Ying Jia She, Yu Jun Fu, Dong Yang Zheng, Yi Bin Xu, Wei Dong Gao
Rok vydání: 2019
Předmět:
Risk
medicine.medical_specialty
Heartbeat
Respiratory rate
02 engineering and technology
Sensitivity and Specificity
Respiratory signal
Ballistocardiography
Machine Learning
Electrocardiography
Heart Rate
Internal medicine
0502 economics and business
Heart rate
0202 electrical engineering
electronic engineering
information engineering
medicine
Humans
Heart rate variability
False Positive Reactions
Diagnosis
Computer-Assisted
Fixed length
Sleep Apnea
Obstructive
business.industry
Applied Mathematics
Decision Trees
05 social sciences
Reproducibility of Results
Sleep apnea
Signal Processing
Computer-Assisted
General Medicine
medicine.disease
respiratory tract diseases
Obstructive sleep apnea
Computational Mathematics
Nonlinear Dynamics
Modeling and Simulation
Cardiology
020201 artificial intelligence & image processing
Sleep
General Agricultural and Biological Sciences
business
Algorithms
050203 business & management
Zdroj: Mathematical Biosciences and Engineering. 16:5672-5686
ISSN: 1551-0018
DOI: 10.3934/mbe.2019282
Popis: 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.
Databáze: OpenAIRE
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