Contactless continuous heart rate monitoring system using ballistocardiography

• Published in: PloS one Vol. 17; no. 7; p. e0272072
• Main Authors: Sumali, Brian, Mitsukura, Yasue, Nishimura, Toshihiko
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 29.07.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Ballistocardiograms; Ballistocardiography; Bioethics; Biology and Life Sciences; Blood pressure; Cardiovascular disease; Cardiovascular diseases; Coronary vessels; Diagnosis; EKG; Electrocardiography; Evaluation; Heart beat; Heart rate; Load distribution; Measurement; Medicine and Health Sciences; Monitoring; Monitoring systems; Myocardial infarction; Nervous system; Patient monitoring equipment; Physical Sciences; Research and Analysis Methods; Respiration; Resuscitation; Sensors; Social Sciences; Survival; Synchronism; Synchronization; Telemedicine; Waveforms; Japan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0272072

Cardiovascular disease is the number one cause of death in the world and is a serious problem. In the case of cardiopulmonary arrest due to myocardial infarction, the survival rate is as low as 13.3% one month after resuscitation, which birthed the need for continuous heart monitoring. In this study, we develop a Ballistocardiogram (BCG) measurement system using a load cell installed on a chair and a heart rate estimation algorithm that is robust to waveform changes, with the aim of constructing a non-contact heart rate acquisition system. The proposed system was evaluated by utilizing data obtained from 13 healthy subjects and 1 subject with abnormal ECG who were simultaneously measured with ECG. The output of the BCG system was confirmed to change with the same period as the ECG data obtained as the correct answer, and the synchronization of the R-peak positions was confirmed for all cases. As a result of comparing the heart rate intervals estimated from BCG and those obtained from ECG, it was confirmed that the same heart rate variability (HRV) features could be obtained even for abnormal ECG subject.