Ambient Light Contribution as a Reference for Motion Artefacts Reduction in Photoplethysmography

Ferreira, Nicolas De Pinho; Géhin, Claudine; Massot, Bertrand

doi:10.5220/0008878800230032

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Since one of the effects of artifacts is the different number of detected heartbeats in the synchronized ECG and PPG signals, a direct comparison of both derived HRV and PRV signals is not trivial. Rather, to avoid this problem, different averaged metrics were used for the assumed time intervals, such as the heart rate (HR) or statistical measures, so that the number of samples in both signals was the same [34,[36][37][38][39][40][41][42][43][44][45][46]. Direct comparisons between PRV and HRV extracted from the PPG and ECG have been studied only a few times.…”

Section: Introductionmentioning

confidence: 99%

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

Polak

Klich

Saganowski

et al. 2022

Sensors

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Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to heart rate variability (HRV) derived from the accompanying ECG. The results indicate that filtering PPG signals using the discrete wavelet transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.

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Section: Introductionmentioning

confidence: 99%

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

Polak

Klich

Saganowski

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Since one of the effects of artifacts is the different number of detected heartbeats in the synchronized ECG and PPG signals, a direct comparison of both derived HRV and PRV signals is not trivial. Rather, to avoid this problem, different averaged metrics were used for the assumed time intervals, such as heart rate (HR) or statistical measures, so that the number of samples in both signals was the same [34,[36][37][38][39][40][41][42][43][44][45][46]. Direct comparisons between PRV and HRV extracted from PPG and ECG have been studied only a few times.…”

Section: Introductionmentioning

confidence: 99%

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

Polak¹,

Klich²,

Saganowski³

et al. 2022

Preprint

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Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts Pulse Rate Variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to Heart Rate Variability (HRV) derived from the accompanying recorded ECG. The results indicate that filtering PPG signals using the Discrete Wavelet Transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Postprocessing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the AR model is only important when PPG is of low-quality and has no effect under good signal quality. The main conclusion is that DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms. However, postprocessing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.

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The Use of Wrist EMG Increases the PPG Heart Rate Accuracy in Smartwatches

Friman¹,

Vehkaoja

Perez-Macias

2022

IEEE Sensors J.

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Ambient Light Contribution as a Reference for Motion Artefacts Reduction in Photoplethysmography

Cited by 4 publications

References 8 publications

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

The Use of Wrist EMG Increases the PPG Heart Rate Accuracy in Smartwatches

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