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

Abstract

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.

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

“…Factors affecting PPG signal quality include changes in tissue properties due to muscle movement, sensor displacement, loose band attachment, and environmental factors such as ambient light [ 4 , 5 , 12 ]. Movement is the most common source of artifacts, called motion artifacts (MAs) [ 1 ].…”

“…Notwithstanding, wavelet decomposition or comb filtering might be more efficient preprocessing methods leading to a higher signal-to-noise ratio (SNR) [ 14 , 15 ]. It is worth noting that the frequency spectrum of MAs can overlap with HR frequencies, necessitating additional processing for their detection and removal, which remains a challenging task [ 4 , 5 , 7 , 12 , 13 , 16 ].…”

“…A vast number of studies has dealt with artifact detection in PPGs [ 12 , 17 , 18 ]. Algorithms for artifact detection and/or removal can be classified into three main categories [ 12 ].…”

“…A vast number of studies has dealt with artifact detection in PPGs [ 12 , 17 , 18 ]. Algorithms for artifact detection and/or removal can be classified into three main categories [ 12 ]. The first category includes the signal decomposition methods, based on the suppression of noisy components at different frequency bands [ 12 , 17 , 18 ].…”

“…Algorithms for artifact detection and/or removal can be classified into three main categories [ 12 ]. The first category includes the signal decomposition methods, based on the suppression of noisy components at different frequency bands [ 12 , 17 , 18 ]. Common signal decomposition methods are discrete wavelet transform, empirical mode decomposition, independent component analysis, variational mode decomposition, and short-time Fourier transform [ 12 ].…”