ArticleFrontiers in bioengineering and biotechnology2023
PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points.
Article in Frontiers in bioengineering and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Intrinsic PPG-ECG Coupling for Accurate and Low-Power Blood Pressure Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine Learning Insights into Age-Related Sex Differences in Blood Pressure: Influence of Diabetes and Body Mass Index.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2026Article
- Photoplethysmography acceleration indices as digital biomarkers of cardiometabolic health.Frontiers in digital health · 2026Article
- Prescreening depression using wearable electrocardiogram and photoplethysmogram data from a psycholinguistic experiment.Physiological measurement · 2025Article
- Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.Biosensors · 2025Article
- Feature extraction tool using temporal landmarks in arterial blood pressure and photoplethysmography waveforms.NPJ cardiovascular health · 2025Article
- pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis.Physiological measurement · 2024Article
- Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features.Frontiers in cardiovascular medicine · 2023Article
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Authors and funding
5 authors.
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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.
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