Dr Mahmoud Ehnesh

Profile

Dr Mahmud Ehnesh is a Lecturer in Biomedical Systems Engineering at Queen Mary University of London. His research combines biomedical sensing, signal and image processing, and patient-specific computational modelling to understand cardiac arrhythmias and inform catheter ablation strategies. He develops methods that integrate cardiac imaging and electrophysiological measurements with computer simulations, alongside software for real-time acquisition, analysis and visualisation of clinical signals. He holds a PhD in Engineering from the University of Leicester, an MSc in Electrical and Electronic Engineering from Coventry University, and an Associate Fellowship of the Higher Education Academy. His teaching connects engineering theory with practical instrumentation and clinical applications.

Research

Research Interests:

Mahmud’s research focuses on biomedical signal processing and patient-specific computational modelling of cardiac arrhythmias. His work combines clinical measurements, cardiac imaging and computer simulations to understand arrhythmia mechanisms and support personalised treatment. His research includes the following topics:

Biomedical Sensing and Wearable Cardiac Monitoring.

Mahmud is developing embedded and wearable sensing tools for continuous cardiac monitoring, aiming to enable earlier, more accurate arrhythmia detection through low-cost, patient-friendly designs.

Patient-Specific Cardiac Digital Twins.

This work develops patient-specific computational models using cardiac imaging and electrophysiological measurements to improve personalised treatment planning.

Cardiac Signal Processing and Arrhythmia Mapping.

This research uses signal-processing methods to characterise the electrical activity underlying atrial fibrillation and ventricular arrhythmias, aiming to improve the identification of catheter ablation targets.

Publications

Full list of publications can be found on Google Scholar

Papers:

1. Ehnesh M, Valli H, Jaffery OA, et al. Comparative Multimodal Calibration of Patient-Specific Atrial Fibrillation Models: Impact of Imaging and Electrophysiology Data on Arrhythmogenic Substrate Identification. The Journal of Physiology. 2026. In press.
2. Tonko JB, Ehnesh M, Vigmond E, Chow A, Roney C, Lambiase PD. Omnipolar conduction velocity mapping for ventricular substrate characterization: impact of CV estimation method and EGM type on in vivo conduction velocity measurements. Heart Rhythm. 2024;21(12):2499–2508.
3. Valli H, Ehnesh M, Coveney S, et al. High-density evaluation of the arrhythmogenic substrate in persistent atrial fibrillation. Heart Rhythm O2. 2025;6(10):1536–1545.
4. Ehnesh M, Li X, Almeida TP, Chu GS, Dastagir N, Stafford PJ, Ng GA, Schlindwein FS. Evaluating spatial disparities of rotor sites and high dominant frequency regions during catheter ablation for PersAF patients. Frontiers in Physiology. 2022;13:946718.
5. Ehnesh M, Abatis P, Schlindwein FS. A portable electrocardiogram for real-time monitoring of cardiac signals. SN Applied Sciences. 2020;2:1419.