Key result
The proposed recursive Wiener filtering algorithm estimated heart rate from PPG signals during intensive physical exercise with an average absolute error of 1.85 beats per minute.
Why the study?
Accurate and reliable heart rate estimation from photoplethysmographic signals during moderate and vigorous physical activities is challenging due to intense motion artifacts.
Does a recursive Wiener filtering technique improve heart rate estimation accuracy from PPG signals during intensive physical exercise compared to existing algorithms?
Does a recursive Wiener filtering technique improve heart rate estimation accuracy from PPG signals during intensive physical exercise compared to existing algorithms?
A novel recursive Wiener filtering algorithm improves the accuracy of heart rate estimation from wearable PPG sensors during intensive physical exercise by effectively reducing motion artifacts.
Answers are grounded in this paper and cite their sources.
May facilitate reliable wearable HR monitoring during exercise; leaves open prospective clinical validation.
Accurate and reliable estimation of heart rate (HR) from photoplethysmographic (PPG) signals during moderate and vigorous physical activities is a challenging task, since intense motion artifacts can easily disguise the true HR. A novel method for estimating HR from PPG signal, during intensive physical exercise, is presented in this paper. The proposed method employs a recursive Wiener filtering technique for HR estimation from motion artifacts-corrupted PPG signal and simultaneously recorded the triaxial accelerometer signal. The experimental results demonstrated that the average relative error and the average absolute error of the proposed method on a public dataset (IEEE 2015 Signal Processing Cup Database) of 23 PPG recordings were 1.73 and 1.85 beats per minute, respectively. Our proposed approach is faster and more accurate than the existing proposals. Therefore, the proposed algorithm can be a reliable solution for HR estimation from noisy PPG signal.
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Motin et al. (2019) studied Intensive physical exercise (n=20). Recursive Wiener filtering (RWF) algorithm vs. Existing heart rate estimation algorithms (e.g., JOSS, TROIKA) was evaluated on Average absolute error (avAE) and average relative error (avRE) in heart rate estimation compared to ground truth ECG. The proposed recursive Wiener filtering algorithm estimated heart rate from PPG signals during intensive physical exercise with an average absolute error of 1.85 beats per minute.
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