1 citations · 2 across the 6 of their papers we have counts for
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A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis
Ciaran Bench, Oskar Pfeffer, Vivek Desai +7
In principle, deep learning models trained on medical time-series, including wearable photoplethysmography (PPG) sensor data, can provide a means to continuously monitor physiologi…
Estimating systematic errors in Bayesian inversion using transport maps
Maren Casfor, Philipp Trunschke, Nando Hegemann +1
In indirect measurements, the sought parameters have to be determined by solving an inverse problem, typically in a Bayesian framework. Often, the accurate numerical simulation of…
Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches
Mohammad Moulaeifard, Loic Coquelin, Mantas Rinkevičius +13
Photoplethysmography (PPG) is a widely used non-invasive physiological sensing technique, suitable for various clinical applications. Such clinical applications are increasingly su…