1. Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors
- Author
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Masato Yasui, Yasue Mitsukura, Masaki Nagura, Koichi Fukunaga, and Brian Sumali
- Subjects
Adult ,Male ,biomedical informatics ,Heartbeat ,Computer science ,biomedical signal processing ,lcsh:Chemical technology ,complex mixtures ,biomedical equipment ,01 natural sciences ,Biochemistry ,Article ,Analytical Chemistry ,Ballistocardiography ,Electrocardiography ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Heart Rate ,medical information systems ,Photoplethysmogram ,Heart rate ,Humans ,Heart rate variability ,lcsh:TP1-1185 ,cardiovascular diseases ,Electrical and Electronic Engineering ,Photoplethysmography ,Instrumentation ,Leg ,Sleep Stages ,business.industry ,010401 analytical chemistry ,Signal Processing, Computer-Assisted ,cardiography ,Pattern recognition ,Models, Theoretical ,Sleep time ,Atomic and Molecular Physics, and Optics ,0104 chemical sciences ,Female ,Sleep (system call) ,Artificial intelligence ,business ,Algorithms ,psychological phenomena and processes ,030217 neurology & neurosurgery - Abstract
Ballistocardiogram (BCG) is a graphical representation of the subtle oscillations in body movements caused by cardiovascular activity. Although BCGs cause less burden to the user, electrocardiograms (ECGs) are still commonly used in the clinical scene due to BCG sensors&rsquo, noise sensitivity. In this paper, a robust method for sleep time BCG measurement and a mathematical model for predicting sleep stages using BCG are described. The novel BCG measurement algorithm can be described in three steps: preprocessing, creation of heartbeat signal template, and template matching for heart rate variability detection. The effectiveness of this algorithm was validated with 99 datasets from 36 subjects, with photoplethysmography (PPG) to compute ground truth heart rate variability (HRV). On average, 86.9% of the inter-beat intervals were detected and the mean error was 8.5ms. This shows that our method successfully extracted beat-to-beat intervals from BCG during sleep, making its usability comparable to those of clinical ECGs. Consequently, compared to other conventional BCG systems, even more accurate sleep heart rate monitoring with a smaller burden to the patient is available. Moreover, the accuracy of the sleep stages mathematical model, validated with 100 datasets from 25 subjects, is 80%, which is higher than conventional five-stage sleep classification algorithms (max: 69%). Although, in this paper, we applied the mathematical model to heart rate interval features from BCG, theoretically, this sleep stage prediction algorithm can also be applied to ECG-extracted heart rate intervals.
- Published
- 2020
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