Key result
The PARHELIA algorithm achieved an average absolute estimation error of 1.17 BPM compared to 1.28 BPM for the conventional JOSS method, with a 20 times shorter estimation time.
The PARHELIA algorithm provides slightly more accurate and significantly faster heart rate estimation from PPG signals during exercise compared to the conventional JOSS method.
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May enable faster real-time PPG HR monitoring during exercise; leaves open prospective clinical validation.
The photoplethysmographic (PPG) signal is an important source of information for estimating heart rate (HR). However, the PPG signal could be strongly contaminated by the motion artifact of the subjects, making HR estimation a particularly difficult problem. In this paper, we propose PARHELIA, a PARticle filter-based algorithm for HEart rate estimation using photopLethysmographIc signAls. The proposed method employs a particle filter, and utilizes the simultaneously recorded acceleration signals from a wrist-type sensor, to keep track of multiple HR candidates. This achieves quick recovery from incorrect HR estimations under the strong influence of the MA. Experimental results for a dataset of 12 subjects recorded during fast running showed that the average absolute estimation error was 1.17 beats per minute (BPM) whereas that of the best-known conventional method, JOSS, is 1.28 BPM. Furthermore, the estimation time of PARHELIA is 20 times shorter than JOSS.
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Fujita et al. (2017) studied Heart rate estimation during physical exercise (n=12). PARHELIA (particle filter-based algorithm) vs. JOSS (conventional method) was evaluated on Average absolute estimation error. The PARHELIA algorithm achieved an average absolute estimation error of 1.17 BPM compared to 1.28 BPM for the conventional JOSS method, with a 20 times shorter estimation time.
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