Accurate Heart Rate Monitoring During Physical Exercises Using PPG

• Published in: IEEE transactions on biomedical engineering Vol. 64; no. 9; pp. 2016 - 2024
• Main Author: Temko, Andriy
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TBME.2017.2676243

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.