Opening the envelope: Efficient envelope-based PPG denoising algorithm

Photoplethysmography (PPG) signals obtained from the skin’s surface offer valuable insights into blood volume fluctuations. With the rising interest in continuous non-invasive physiological monitoring, PPG has garnered significant attention. However, PPG signals are often affected by various forms of noise, impeding reliable feature extraction. Robust data pre-processing approaches are vital for both retrospective and real-time analysis. Existing denoising methods, including recent machine learning techniques, often suffer from implementation challenges, computational inefficiency, and limited interpretability. Addressing this challenge, we propose a novel PPG denoising algorithm. The algorithm was evaluated using a dataset representing approximately 81,015.99 min or 1360.27 h of PPG data collected from 31 patients. The evaluation involved the calculation and analysis of five key metrics: Signal-to-Noise Ratio (SNR), Variance, Total Variation (TV), Shannon entropy, and Instances-per-second (IPS). Our results demonstrate a notable increase in SNR after denoising, indicating effective noise reduction while preserving signal content. Variance and TV values showed a reduction post-denoising, suggesting smoother and less variable signals, validating the noise suppression efficacy. Additionally, Shannon entropy exhibited a decrease after denoising, indicating successful noise reduction and enhanced signal regularity. The nonparametric Wilcoxon signed-rank test (a = 0.05) was employed to assess the statistical significance of the observed differences of these metrics before and after denoising. Furthermore, the computational speed analysis revealed the EPDA’s potential for efficient processing of large datasets and real-time applications. This comprehensive evaluation approach allows for a thorough understanding of the EPDA’s effectiveness in denoising PPG data, fostering advancements in non-invasive physiological monitoring and promoting the broader adoption of PPG-based healthcare technologies.