Publications — Radcliffe Department of Medicine

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Temporal patterns of pre- and post-natal target organ damage associated with hypertensive pregnancy: a systematic review.

Cutler HR. et al, (2024), Eur J Prev Cardiol, 31, 77 - 99

Prediction of fetal RR intervals from maternal factors using machine learning models.

Widatalla N. et al, (2023), Sci Rep, 13

Modelling relations between blood pressure, cardiovascular phenotype, and clinical factors using large scale imaging data.

Kart T. et al, (2023), Eur Heart J Cardiovasc Imaging, 24, 1361 - 1362

Emotional Climate Recognition in Conversations using Peers' Speech-based Bispectral Features and Affect Dynamics.

Alhussein G. et al, (2023), Annu Int Conf IEEE Eng Med Biol Soc, 2023, 1 - 5

Heart Failure Assessment Using Multiparameter Polar Representations and Deep Learning.

Alkhodari M. et al, (2023), Annu Int Conf IEEE Eng Med Biol Soc, 2023, 1 - 4

Recognition of Pediatric Congenital Heart Diseases by Using Phonocardiogram Signals and Transformer-Based Neural Networks.

Hassanuzzaman M. et al, (2023), Annu Int Conf IEEE Eng Med Biol Soc, 2023, 1 - 4

The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare.

Alkhodari M. et al, (2023), Expert Rev Cardiovasc Ther

Investigating the effects of beta-blockers on circadian heart rhythm using heart rate variability in ischemic heart disease with preserved ejection fraction.

Saleem S. et al, (2023), Sci Rep, 13

Novel Speech-Based Emotion Climate Recognition in Peers’ Conversations Incorporating Affect Dynamics and Temporal Convolutional Neural Networks

Alhussein G. et al, (2023)

Novel Speech-Based Emotion Climate Recognition in Peers’ Conversations Incorporating Affect Dynamics and Temporal Convolutional Neural Networks

Alhussein G. et al, (2023)

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features.

Al Younis SM. et al, (2023), PLoS One, 18

A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis.

Saleem S. et al, (2022), Sci Rep, 12

Deep learning identifies cardiac coupling between mother and fetus during gestation

Alkhodari M. et al, (2022), Frontiers in Cardiovascular Medicine

Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles.

Rashid M. et al, (2022), J Clin Med, 11

Using prior information to enhance microwave tomography images in bone health assessment.

Alkhodari M. et al, (2022), Biomed Eng Online, 21

Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study.

Dhou S. et al, (2022), J Imaging, 8

Association between maternal-fetal cardiac coupling strengths with maternal and fetal parameters

Alkhodari M. et al, (2022), 2022 12th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2022

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool.

Alkhodari M. and Khandoker AH., (2022), PLoS One, 17

Effects of Beta-Blocker on Heart Rate Variability in Heart Failure with Preserved Ejection Fraction

Saleem S. et al, (2022), Computing in Cardiology, 2022-September

Emotional Climate Recognition in Interactive Conversational Speech Using Deep Learning

Alhussein G. et al, (2022), Proceedings - 2022 IEEE International Conference on Digital Health, ICDH 2022, 96 - 103

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