10 citations · 10 across the 4 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…
Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks
Ciaran Bench, Vivek Desai, Mohammad Moulaeifard +3
Photoplethysmography (PPG) signals encode information about relative changes in blood volume that can be used to assess various aspects of cardiac health non-invasively, e.g.\ to d…
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…
Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study
Mohammad Moulaeifard, Peter H. Charlton, Nils Strodthoff
Photoplethysmography (PPG)-based blood pressure (BP) estimation represents a promising alternative to cuff-based BP measurements. Recently, an increasing number of deep learning mo…