Key result
Parallel RLS and wavelet denoising limits pulse rate error to ~3 BPM during intensive exercise.
Why the study?
PPG signals detected during movement are easily affected by random noises and motion artifacts, reducing the accuracy of pulse rate estimation.
The proposed parallel adaptive filtering and wavelet threshold denoising method maintains high pulse rate estimation accuracy from PPG signals even during strong motion artifacts.
Answers are grounded in this paper and cite their sources.
May enhance motion-robust PPG pulse rate monitoring during exercise; hypothesis-generating and requires prospective clinical validation.
Real-time pulse rate (PR) monitoring based on photoplethysmography (PPG) has been drawn much attention in recent years. However, PPG signal detected under movement is easily affected by random noises, especially motion artifacts (MA), affecting the accuracy of PR estimation. In this paper, a parallel method structure is proposed, which effectively combines wavelet threshold denoising with recursive least squares (RLS) adaptive filtering to remove interference signals, and uses spectral peak tracking algorithm to estimate real-time PR. Furthermore, we propose a parallel structure RLS adaptive filtering to increase the amplitude of spectral peak associated with PR for PR estimation. This method is evaluated by using the PPG datasets of the 2015 IEEE Signal Processing Cup. Experimental results on the 12 training datasets during subjects' walking or running show that the average absolute error (AAE) is 1.08 beats per minute (BPM) and standard deviation (SD) is 1.45 BPM. In addition, the AAE of PR on the 10 testing datasets during subjects' fast running accompanied with wrist movements can reach 2.90 BPM. Furthermore, the results indicate that the proposed approach keeps high estimation accuracy of PPG signal even with strong MA.
No takes yet. Share an insight, caveat, or question.
Wan et al. (2020) studied Healthy subjects (exercising) (n=22). Parallel RLS adaptive filtering and wavelet threshold denoising vs. Other pulse rate estimation algorithms (e.g., TROIKA, JOSS) was evaluated on Average absolute error (AAE) of pulse rate estimation in beats per minute (BPM). The proposed parallel RLS adaptive filtering and wavelet threshold denoising method achieved an average absolute error of 1.08 BPM on training datasets and 2.90 BPM on testing datasets during intensive physical exercise.
Synapse has enriched 10 related papers. Consider them for comparative context: