Mahmoud Ehnesh

Dr. Mahmoud Ehnesh serves as a Postdoctoral Research Associate at Queen Mary University of London's School of Engineering and Materials Science, specializing in biomedical engineering with focus on cardiac electrophysiology. His research is supervised by Dr. Caroline Roney within the DERI research group, leveraging advanced computational techniques to address critical challenges in cardiac arrhythmia management.

His educational background includes:

• BSc in Engineering
• MSc in Biomedical/Electrical Engineering
• PhD in Biomedical Engineering
• Associate Fellow of the Higher Education Academy (AFHEA)

Ehnesh's research centers on cardiac arrhythmia mechanisms, particularly atrial fibrillation and ventricular tachycardia. He pioneers digital signal processing and intelligent algorithms for biomedical signal analysis, with emphasis on omnipolar mapping, conduction velocity characterization, and patient-specific computational modeling. His work bridges engineering innovation with clinical cardiology to develop novel substrate assessment techniques for personalized ablation therapy.

Analysis of his 15 most recent publications (2022-2025) reveals a strong trend toward quantitative substrate characterization using vector field heterogeneity metrics and omnipolar electrograms. His research spans atrial and ventricular arrhythmias, with increasing focus on patient-specific modeling and clinical translation of computational techniques. Key thematic areas include arrhythmia substrate mapping, fibrosis characterization, and optimization of ablation strategies through advanced signal processing.

No scientific awards or fellowships are documented in available materials.

As a postdoctoral researcher, Ehnesh operates under Dr. Caroline Roney's supervision within the DERI group, contributing to collaborative projects in cardiac engineering. His work involves significant computational resource utilization for modeling and signal analysis, though specific grant details remain undisclosed. Current activities focus on validating novel mapping metrics for clinical implementation and refining patient-specific ablation targeting protocols.

He is actively embedded in the cardiac electrophysiology research ecosystem at Queen Mary, collaborating with clinical cardiologists and engineers through the DERI group. Current projects emphasize translational development of omnipolar mapping techniques and computational models for real-world arrhythmia management, with strong ties to clinical electrophysiology laboratories.