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Towards Photoplethysmography-Based Estimation of Instantaneous Heart Rate During Physical Activity

Jarchi, Delaram, Casson, Alexander J. (2017) Towards Photoplethysmography-Based Estimation of Instantaneous Heart Rate During Physical Activity. IEEE Transactions on Biomedical Engineering, 64 (9). pp. 2042-2053. ISSN 0018-9294. (doi:10.1109/TBME.2017.2668763) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

Objective: Recently numerous methods have been proposed for estimating average heart rate using photoplethysmography (PPG) during physical activity, overcoming the significant interference that motion causes in PPG traces. We propose a new algorithm framework for extracting instantaneous heart rate from wearable PPG and Electrocardiogram (ECG) signals to provide an estimate of heart rate variability during exercise. Methods: For ECG signals, we propose a new spectral masking approach which modifies a particle filter tracking algorithm, and for PPG signals constrains the instantaneous frequency obtained from the Hilbert transform to a region of interest around a candidate heart rate measure. Performance is verified using accelerometry and wearable ECG and PPG data from subjects while biking and running on a treadmill. Results: Instantaneous heart rate provides more information than average heart rate alone. The instantaneous heart rate can be extracted during motion to an accuracy of 1.75 beats per min (bpm) from PPG signals and 0.27 bpm from ECG signals. Conclusion: Estimates of instantaneous heart rate can now be generated from PPG signals during motion. These estimates can provide more information on the human body during exercise. Significance: Instantaneous heart rate provides a direct measure of vagal nerve and sympathetic nervous system activity and is of substantial use in a number of analyzes and applications. Previously it has not been possible to estimate instantaneous heart rate from wrist wearable PPG signals.

Item Type: Article
DOI/Identification number: 10.1109/TBME.2017.2668763
Uncontrolled keywords: Photoplethysmography (PPG), electrocardiogram (ECG), particle filter, empirical mode decomposition (EMD), singular spectrum analysis (SSA)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
R Medicine
Divisions: Faculties > Sciences > School of Computing
Depositing User: Delaram Jarchi
Date Deposited: 18 Oct 2018 12:16 UTC
Last Modified: 29 May 2019 21:18 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69647 (The current URI for this page, for reference purposes)
Jarchi, Delaram: https://orcid.org/0000-0001-6699-8721
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