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Automated heart rate detection in seismocardiograms using electrocardiogram-based algorithms : a feasibility study
Citation Link: https://doi.org/10.15480/882.13102
Publikationstyp
Journal Article
Date Issued
2024-06-11
Sprache
English
TORE-DOI
Journal
Volume
11
Issue
6
Article Number
596
Citation
Bioengineering 11 (6): 596 (2024)
Publisher DOI
Scopus ID
Publisher
Multidisciplinary Digital Publishing Institute
Peer Reviewed
true
In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge regarding heart rhythm and its underlying physiology. We implemented the experimental framework with methods developed for ECG signal processing and peak detection to be applied and evaluated on SCGs. Furthermore, we assessed and chose the best from all combinations of 15 peak detection and 6 preprocessing methods from the literature on the CEBS dataset available on Physionet. We then collected experimental data in the lab experiment to measure the applicability of the best-selected technique to the real-world data; the abovementioned method showed high precision for signals recorded during sitting rest (HR difference between SCG and ECG: 0.12 ± 0.35 bpm) and a moderate precision for signals recorded with interfering physical activity—reading out a book loud (HR difference between SCG and ECG: 6.45 ± 3.01 bpm) when compared to the results derived from the state-of-the-art photoplethysmographic (PPG) methods described in the literature. The study shows that computationally simple preprocessing and peak detection techniques initially developed for ECG could be utilized as the basis for HR detection on SCG, although they can be further improved.
Subjects
accelerometers
electrocardiogram
heart rhythm
seismocardiography
DDC Class
610: Medicine, Health
Publication version
publishedVersion
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bioengineering-11-00596-v2.pdf
Type
Main Article
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4.79 MB
Format
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