Accurate Heart Rate Monitoring During Physical Exercises Using PPG

Objective: The challenging task of heart rate (HR) estimation from the photoplethysmographic (PPG) signal, during intensive physical exercises, is tackled in this paper. Methods: The study presents a detailed analysis of a novel algorithm (WFPV) that exploits a Wiener filter to attenuate the motion...

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Vydáno v:IEEE transactions on biomedical engineering Ročník 64; číslo 9; s. 2016 - 2024
Hlavní autor: Temko, Andriy
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.09.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accelerometers
Adolescent
Adult
Algorithm design and analysis
Algorithms
Computer applications
Diagnosis, Computer-Assisted - methods
Estimation
Exercise
Exercise - physiology
Female
Fitness
Heart rate
Heart Rate - physiology
Heart Rate Determination - methods
Humans
Internet
Male
Medical electronics
Middle Aged
Monitoring
Monitoring, Ambulatory - methods
Pattern Recognition, Automated - methods
phase vocoder
photoplethysmographic
Photoplethysmography - methods
Physical Exertion - physiology
Physical fitness
Post-production processing
Reproducibility of Results
Sensitivity and Specificity
spectrum estimation
Tracking
Viterbi decoding
Vocoders
Wearable technology
wiener filter
Wiener filtering
Wiener filters
Young Adult
ISSN:0018-9294, 1558-2531, 1558-2531
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Popis
Shrnutí:Objective: The challenging task of heart rate (HR) estimation from the photoplethysmographic (PPG) signal, during intensive physical exercises, is tackled in this paper. Methods: The study presents a detailed analysis of a novel algorithm (WFPV) that exploits a Wiener filter to attenuate the motion artifacts, a phase vocoder to refine the HR estimate and user-adaptive post-processing to track the subject physiology. Additionally, an offline version of the HR estimation algorithm that uses Viterbi decoding is designed for scenarios that do not require online HR monitoring (WFPV+VD). The performance of the HR estimation systems is rigorously compared with existing algorithms on the publically available database of 23 PPG recordings. Results: On the whole dataset of 23 PPG recordings, the algorithms result in average absolute errors of 1.97 and 1.37 BPM in the online and offline modes, respectively. On the test dataset of 10 PPG recordings which were most corrupted with motion artifacts, WFPV has an error of 2.95 BPM on its own and 2.32 BPM in an ensemble with two existing algorithms. Conclusion: The error rate is significantly reduced when compared with the state-of-the art PPG-based HR estimation methods. Significance: The proposed system is shown to be accurate in the presence of strong motion artifacts and in contrast to existing alternatives has very few free parameters to tune. The algorithm has a low computational cost and can be used for fitness tracking and health monitoring in wearable devices. The MATLAB implementation of the algorithm is provided online.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2017.2676243