Optimal filtering and smoothing thresholds for high-frequency photoplethysmography signals

Photoplethysmography (PPG) is a non-invasive optical technique used to measure cardiovascular activity in both clinical and consumer applications. PPG signals are notoriously susceptible to noise and motion artefacts and in applications where high-frequency PPG signals are required, these issues are even more pronounced. In this work, we propose a two-stage algorithm to process these PPG waveforms without prior knowledge of their noise characteristics. To evaluate its performance, we utilized a set of PPG signal quality indices used to classify waveform quality. We found that using a Chebyshev type-II band-pass filter with cutoff frequencies of 0.35 Hz for high pass and 10 Hz for low pass performs best across six measured Signal Quality Indices (SQIs), achieving a 12% improvement in the dynamic time-warping (DTW) SQI. Furthermore, using a modified Savitzky-Golay smoothing algorithm with a frequency domain-derived window parameter leads to additional improvements in SQI-derived features of 14.09% and 12.22% in DTW and skewness respectively. Our proposed approach provides a universal solution for processing high-frequency raw PPG recordings and prepares them for the extraction of morphological features.