214 citations · 442 across the 48 of their papers we have counts for
14 papers · 1 filter
Cross-Modal Generative Framework for Signal Translation from Fetal-Maternal Electrocardiograms to Fetal Doppler Waveforms
Tongli Su, Alireza Rafiei, Marly van Assen +4
Fetal electrocardiogram (fECG) and Doppler ultrasound provide complementary views of fetal cardiovascular function: fECG captures electrical activity while Doppler reflects mechani…
SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models
Feng Wu, Harsh Deep, Eric Lehman +5
Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichannel signal complexity, inherent…
Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025
Matthew A. Reyna, Zuzana Koscova, Jan Pavlus +11
Objective: Chagas disease is a parasitic infection that is endemic to South America, Central America, and, more recently, the U.S., primarily transmitted by insects. Chronic Chagas…
Cross-Learning Between ECG and PCG: Exploring Common and Exclusive Characteristics of Bimodal Electromechanical Cardiac Waveforms
Sajjad Karimi, Amit J. Shah, Gari D. Clifford +1
Simultaneous electrocardiography (ECG) and phonocardiogram (PCG) provide a comprehensive, multimodal perspective on cardiac function by capturing the heart's electrical and mechani…
Auto-FEDUS: Autoregressive Generative Modeling of Doppler Ultrasound Signals from Fetal Electrocardiograms
Alireza Rafiei, Gari D. Clifford, Nasim Katebi
Fetal health monitoring through one-dimensional Doppler ultrasound (DUS) signals offers a cost-effective and accessible approach that is increasingly gaining interest. Despite its…
Edge AI for Real-time Fetal Assessment in Rural Guatemala
Nasim Katebi, Mohammad Ahmad, Mohsen Motie-Shirazi +14
Perinatal complications, defined as conditions that arise during pregnancy, childbirth, and the immediate postpartum period, represent a significant burden on maternal and neonatal…
An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains
Jun Li, Aaron Aguirre, Junior Moura +6
Artificial intelligence (AI) has demonstrated significant potential in ECG analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role…
Benchmarking changepoint detection algorithms on cardiac time series
Ayse Cakmak, Erik Reinertsen, Shamim Nemati +1
The pattern of state changes in a biomedical time series can be related to health or disease. This work presents a principled approach for selecting a changepoint detection algorit…
Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models
Ismail Sadiq, Erick A. Perez-Alday, Amit J. Shah +3
Objective: To determine if a realistic, but computationally efficient model of the electrocardiogram can be used to pre-train a deep neural network (DNN) with a wide range of morph…
An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms
Salman Seyedi, Li Xiong, Shamim Nemati +1
The increasing complexity of algorithms for analyzing medical data, including de-identification tasks, raises the possibility that complex algorithms are learning not just the gene…
Voting of predictive models for clinical outcomes: consensus of algorithms for the early prediction of sepsis from clinical data and an analysis of the PhysioNet/Computing in Cardiology Challenge 2019
Matthew A. Reyna, Gari D. Clifford
Although there has been significant research in boosting of weak learners, there has been little work in the field of boosting from strong learners. This latter paradigm is a form…
Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data
Ayse S. Cakmak, Nina Thigpen, Garrett Honke +9
Depression and post-traumatic stress disorder (PTSD) are psychiatric conditions commonly associated with experiencing a traumatic event. Estimating mental health status through non…
Subject Selection on a Riemannian Manifold for Unsupervised Cross-subject Seizure Detection
Samaneh Nasiri Ghosheh Bolagh, Gari. D. Clifford
Inter-subject variability between individuals poses a challenge in inter-subject brain signal analysis problems. A new algorithm for subject-selection based on clustering covarianc…
Fusing Continuous-valued Medical Labels using a Bayesian Model
Tingting Zhu, Nic Dunkley, Joachim Behar +2
With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels i…