TY - GEN
T1 - A non-contact and unconstrained sleep health monitoring system
AU - Chen, Zeyu
AU - Tian, Fuze
AU - Zhao, Qinglin
AU - Hu, Bin
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Clinically, polysomnography (PSG) is used to assess sleep quality by monitoring various parameters, such as Electroencephalogram (EEG), electrocardiogram (ECG), Electrooculography (EOG), Electromyography (EMG), pulse, oxygen saturation, and respiratory rate. However, in order to assess these parameters, PSG requires a variety of sensors that must make direct contact with patients’ bodies, which can affect patients’ quality of sleep during testing. Thus, the use of PSG to assess sleep quality can yield invalid and inaccurate results. To address this gap, this paper proposes a sleep health monitoring system that has no restraints and does not interfere with sleep. This method collects ballistocardiogram (BCG) due to ejection by placing a piezoelectric film sensor under a sleeping cushion and evaluates three indicators: heart rate variability (HRV), respiration, and body movements. The ECG and BCG of 10 subjects were collected synchronously while the subjects were lying flat. Specifically, power-line interference was eliminated by adaptive digital filtering with a minimum mean square. A paired t-test revealed that there were no significant differences between BCG and standard ECG signals in the time-domain, frequency-domain, and nonlinear parameters of HRV. Respiration and body motion were extracted from the BCG in order to effectively monitoring of sleep apnea and nighttime bed-off times. Compared with other non-contact monitoring methods, such as acceleration sensors, coupling electrodes, Doppler radar and camera, the system presented in this paper is superior, as it has high signal quality, strong anti-interference ability, and low cost. Moreover, it does not interfere with normal sleep.
AB - Clinically, polysomnography (PSG) is used to assess sleep quality by monitoring various parameters, such as Electroencephalogram (EEG), electrocardiogram (ECG), Electrooculography (EOG), Electromyography (EMG), pulse, oxygen saturation, and respiratory rate. However, in order to assess these parameters, PSG requires a variety of sensors that must make direct contact with patients’ bodies, which can affect patients’ quality of sleep during testing. Thus, the use of PSG to assess sleep quality can yield invalid and inaccurate results. To address this gap, this paper proposes a sleep health monitoring system that has no restraints and does not interfere with sleep. This method collects ballistocardiogram (BCG) due to ejection by placing a piezoelectric film sensor under a sleeping cushion and evaluates three indicators: heart rate variability (HRV), respiration, and body movements. The ECG and BCG of 10 subjects were collected synchronously while the subjects were lying flat. Specifically, power-line interference was eliminated by adaptive digital filtering with a minimum mean square. A paired t-test revealed that there were no significant differences between BCG and standard ECG signals in the time-domain, frequency-domain, and nonlinear parameters of HRV. Respiration and body motion were extracted from the BCG in order to effectively monitoring of sleep apnea and nighttime bed-off times. Compared with other non-contact monitoring methods, such as acceleration sensors, coupling electrodes, Doppler radar and camera, the system presented in this paper is superior, as it has high signal quality, strong anti-interference ability, and low cost. Moreover, it does not interfere with normal sleep.
KW - BCG
KW - HRV
KW - Non-contact
KW - Sleep
UR - http://www.scopus.com/inward/record.url?scp=85081905720&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-37429-7_6
DO - 10.1007/978-3-030-37429-7_6
M3 - Conference contribution
AN - SCOPUS:85081905720
SN - 9783030374280
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 56
EP - 66
BT - Human Centered Computing - 5th International Conference, HCC 2019, Revised Selected Papers
A2 - Miloševic, Danijela
A2 - Tang, Yong
A2 - Zu, Qiaohong
PB - Springer
T2 - 5th International Conference on Human Centered Computing, HCC 2019
Y2 - 5 August 2019 through 7 August 2019
ER -