Title
Analyzing Heart Rate Estimation From Vibrational Cardiography With Different Orientations
Abstract
Remote health monitoring is a widely discussed topic due to its potential to improve quality and delivery of medical treatment and the global increase in cardiovascular diseases. Objective: Seismocardiography and Gyrocardiography have been shown to provide reliable heart rate information. A simple and efficient setup was developed for the monitoring of mechanical signals at the sternum. An algorithm based in autocorrelation was run on subjects with different orientations in order to detect heart rate. Methods: Subjects performed several tests where both SCG and GCG were recorded using an inertial measurement unit, a Raspberry Pi and a BIOPAC acquisition system. A total of 2335 cardiac cycles were obtained from 5 subjects. Heart rate was determined on a per second basis and compared with an electrocardiography (ECG) reference by correlation coefficients. Ensemble averages were used to visualize differences in VCG morphology. Results: Heart rate estimation obtained from VCG signals across all 5 subjects was referenced with ECG and achieved an r-squared correlation coefficient of 0.956 when supine and 0.975 when standing, compared to 0.965 across the entire dataset. Conclusion: Autocorrelated Differential Algorithm was able to successfully detect heart rate, regardless of orientation and posture. Significance: Changes in orientation of the body during measurement introduce inaccuracies. This work shows that the algorithm is resistant to orientation and more adaptable to everyday life.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9175255
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
DocType
Volume
ISSN
Conference
2020
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Ezz Aboulezz101.01
James Skoric201.01
Yannick D'Mello301.01
Siddiqui Hakim401.01
Nathan Clairmonte501.01
Michel Lortie600.34
David V. Plant73017.63