189 citations · 231 across the 7 of their papers we have counts for
7 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…
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 Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarking
Juan Miguel López Alcaraz, Xicoténcatl López Moran, Erick Dávila Zaragoza +4
Artificial intelligence holds strong potential to support clinical decision making in intensive care units where timely and accurate risk assessment is critical. However, many exis…
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
Patrick Wagner, Temesgen Mehari, Wilhelm Haverkamp +1
Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However…
Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models
Temesgen Mehari, Nils Strodthoff
The field of deep-learning-based ECG analysis has been largely dominated by convolutional architectures. This work explores the prospects of applying the recently introduced struct…
Self-supervised representation learning from 12-lead ECG data
Temesgen Mehari, Nils Strodthoff
Clinical 12-lead electrocardiography (ECG) is one of the most widely encountered kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity rema…