Yen, H.T. and Hoang, V.-P. and Trinh, Q.-K. and Doan, V.S. and Sun, G. (2023) Sleep Apnea Patient Monitoring Using Continuous-wave Radar. In: 22nd IEEE Statistical Signal Processing Workshop, SSP 2023, 2 July 2023 Through 5 July 2023, Hanoi.
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Sleep apnea syndrome is a prevalent condition among the elderly people that is potentially dangerous and causes fatal complications. However, this syndrome is often undiagnosed since most patients do not know they have this condition because it only occurs during sleep. In this study, we proposed a non-contact sleep monitoring solution. The system used the support vector machines (SVM) model with three classes classification. The monitoring results give the ratios of three time durations, including the normal sleeping time, body movement time, and time of cessation of breathing. The training model obtained an accuracy of 96.1, and the model was applied to a patient with apnea syndrome in Yokohama Hospital, Japan, showing consistency with the hospital recordings. © 2023 IEEE.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Divisions: | Institutes > Institute of System Integration Faculties > Faculty of Radio-Electronic Engineering |
| Identification Number: | 10.1109/SSP53291.2023.10208017 |
| Uncontrolled Keywords: | Continuous wave radar; Hospitals; Patient monitoring; Sleep research, Condition; Elderly people; Monitoring results; Non-contact; Sleep apnea; Sleep apnea syndrome; Sleep monitoring; Support vector machine models; Support vectors machine; Three-class classification, Support vector machines |
| Additional Information: | Conference of 22nd IEEE Statistical Signal Processing Workshop, SSP 2023 ; Conference Date: 2 July 2023 Through 5 July 2023; Conference Code:191583 |
| URI: | http://eprints.lqdtu.edu.vn/id/eprint/10922 |
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