Key result
A novel method combining heart rate frequency estimation and notch filtering achieved a mean average absolute error of 0.92 bpm for HR estimation on an open source dataset during intensive exercise.
Why the study?
Removing motion artifacts from photoplethysmograph sensors during intensive exercise remains a major challenge for accurate heart rate calculation in wearable health monitoring.
The proposed signal processing method effectively removes motion artifacts and accurately estimates heart rate from PPG signals during intensive exercise.
May support wearable PPG monitoring during exercise; leaves open clinical validation before practice adoption.
With photoplethysmograph (PPG) sensors showing increasing potential in wearable health monitoring, the challenging problem of motion artifact (MA) removal during intensive exercise has become a popular research topic. In this study, a novel method that combines heart rate frequency (HRF) estimation and notch filtering is proposed. The proposed method applies a cascaded adaptive noise cancellation (ANC) based on the least mean squares (LMS)-Newton algorithm for preliminary motion artifacts reduction, and further adopts special heart rate frequency tracking and correction schemes for accurate HRF estimation. Finally, notch filters are employed to restore the PPG signal with estimated HRF based on its quasi-periodicity. On an open source data set that features intensive running exercise, the proposed method achieves a competitive mean average absolute error (AAE) result of 0.92 bpm for HR estimation. The practical experiments are carried out with the PPG evaluation platform developed by ourselves. Under three different intensive motion patterns, a 0.89 bpm average AAE result is achieved with the average correlation coefficient between recovered PPG signal and reference PPG signal reaching 0.86. The experimental results demonstrate the effectiveness of the proposed method for accurate HR estimation and robust MA removal in PPG during intensive exercise.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2019) studied Motion artifacts in photoplethysmograph (PPG) sensors during intensive exercise. Method combining heart rate frequency (HRF) estimation and notch filtering vs. Reference PPG signal was evaluated on Mean average absolute error (AAE) for heart rate estimation. A novel method combining heart rate frequency estimation and notch filtering achieved a mean average absolute error of 0.92 bpm for HR estimation on an open source dataset during intensive exercise.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: