4 papers
Attractor Image-Based Deep Learning of Arterial Pulse Waves for Age Classification
Sara Vardanega, Patrick Segers, Philip Aston +3
Arterial pulse waveform morphology evolves with age, reflecting structural and functional changes in the cardiovascular system. Thus, vascular age is a valuable surrogate marker of…
Benchmark Problems and Benchmark Datasets for the evaluation of Machine and Deep Learning methods on Photoplethysmography signals: the D4 report from the QUMPHY project
Urs Hackstein, Jordi Alastruey, Philip Aston +13
This report is part of the Qumphy project (22HLT01 Qumphy) that is funded by the European Union and is dedicated to the development of measures to quantify the uncertainties associ…
Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches
Mohammad Moulaeifard, Loic Coquelin, Mantas RinkeviÄius +13
Photoplethysmography (PPG) is a widely used non-invasive physiological sensing technique, suitable for various clinical applications. Such clinical applications are increasingly su…
The Impact of Aortic Valve Stenosis on Pulse Wave Morphology: An in silico study with 16,038 virtual subjects
Robert D Wilson, Sara Vardanega, Jiajie Chen +3
Aortic valve stenosis (AVS) presents challenges in asymptomatic detection, resulting in delayed intervention. This study aims to understand how AVS affects pulse wave (PW) morpholo…