Photoplethysmography (PPG) signals are non-invasive, cost-effective optical signals, easily obtained from the skin’s sur- face using a light source and photodetector which provide insights into blood volume fluctuations by monitoring changes in light absorption or reflection. With a growing interest in non-invasive physiological monitoring and a shift towards continuous and ubiquitous patient care, photoplethysmography has received increased interest due to an increased focus to monitor physiological measurements non-invasively, in parallel to a shift in demand to enable continuous and ubiquitous patient monitoring rather than exclusively in-hospital care. This trend has led to the pro- liferation of commercial and healthcare orientated devices using photoplethysmography data.

Despite its potential, photoplethysmography signals are prone to various forms of noise, which can distort the signal and hinder reliable feature extraction. As research in this field progresses and PPG data collection becomes more prevalent, effective data pre-processing approaches are becoming increasingly crucial for retrospective and real-time analysis.

Addressing this challenge and given my core research dataset I developed and evaluated a novel PPG denoising algorithm. The approach has been shown to be efficacious in the denoising of PPG data, reporting significant differences in the Signal-to-noise ratio, Variance, Total Variation and Entropy after denoising whilst processing 70.3 minutes of data per second. The evaluation is predicated upon a dataset of 0.486 billion instances, representing approximately 81,015.99 minutes or 1350.27 hours of data collected from 31 patients. The algorithm aims to provide a simple yet effective approach to denoising whilst fostering advancements in non-invasive physiological marker monitoring and promoting the broader adoption of PPG-based healthcare technologies