PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points

Abstract

Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat’s performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis

“…Two databases were used to validate the pyPPG toolbox (see table 2 ). The multi-ethnic study of atherosclerosis (MESA) database (Dean et al 2016 , Zhang et al 2018 ) was used to validate the peak detector, and the PPG and blood pressure (PPG-BP) database (Liang et al 2018 , Abdullah et al 2023 ) was used to validate the fiducial point detection algorithm. The MESA database consists of polysomnography (PSG) recordings from 2056 adults, aged 54–95 years, with subclinical cardiovascular disease, including 19 998 h of PPG recordings (Chen et al 2015 , Rinkevičius et al 2023 ).…”

“…The human heart rate ranges between 30 and 200 beats per minute (Paliakaitė et al 2020 ). Therefore, in PPG signal analysis, it is common to apply bandpass filtering such 0.5−8 Hz (Abdullah et al 2023 ), 0.5−10 Hz (Finnegan et al 2023 ), 0.5−15 Hz (Mejia-Mejia et al 2022 ), 0.5−20 Hz (Allen and Murray 2000 , Liang et al 2018 ), or 0.5−25 Hz (Chowdhury et al 2020 ), to conserve the frequency content of the PPG pulse waves while filtering out lower-frequency content (e.g. baseline wander due to respiration) and higher-frequency content (e.g.…”

“…To assess the performance of the fiducial point detection algorithm, pyPPG was benchmarked against two publicly available PPG toolboxes capable of detecting fiducial points ( PulseAnalyse (Charlton et al 2019 ) and PPGFeat (Abdullah et al 2023 )). Both benchmarked toolboxes were implemented in Matlab.…”

Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features

“…In this study, we aim to develop a ML-based approach for estimating four different hypertension stages: (normal, prehypertension stage, stage 1 hypertension, stage 2 hypertension), using 19 features derived from statistical analysis of the APG waveform and the clinical parameters age, heart rate and systolic blood pressure. The features were extracted using the fiducial point extraction algorithms explained in our recently released MATLAB toolbox, PPGFeat ( 18 ).…”

“…Numerous studies have demonstrated that the amplitude and timing of these points can provide valuable information about the aging process and support the assessment of cardiovascular disease ( 11 , 13 – 17 ). The identification of the c and d points, however, is not trivial since they can be undetectable or non-prominent in the APG waveform ( 18 , 19 ).…”