10 citations · 10 across the 8 of their papers we have counts for
8 papers
Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography
Mohammad Moulaeifard, Ciaran Bench, Philip J. Aston +1
Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution (OOD) conditions. Here, we a…
Electroencephalography and Electromyography as a Non-Invasive Biomarker of Neural Regeneration: A Review of Central and Peripheral Nervous System Injury and Regeneration
Maryam Kheyrollah, Reza Khanbabaie, Chris Ullrich +1
Regeneration of the nervous system after injury remains an important therapeutic objective, especially in the central nervous system (CNS), in which regeneration is restricted by b…
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…
Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG
Mohammad Moulaeifard, Philip J. Aston, Peter H. Charlton +1
Photoplethysmography (PPG) is one of the most widely captured biosignals for clinical prediction tasks, yet PPG-based algorithms are typically trained on small-scale datasets of un…
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…