Mehdi Razavi, MD

Dr. Razavi is involved with numerous clinical trials assessing the efficacy of cutting edge technology in the field of cardiac electrophysiology. His innovations have been featured in Scientific American, InnovationMap, Voice of America News, and Texas Heart Institute News. In addition, he develops, oversees, and executes long-term collaborations with engineering colleagues at The Texas Heart Institute’s Cardiovascular Research Laboratory, and with exceptional collaborators at Rice University, the University of Houston, Texas A&M University, and UCLA. Show full bio

Dr. Razavi has founded multiple medical device startups for the treatment of arrhythmia and other cardiovascular conditions. Additionally, Dr. Razavi has supervised the development of a number of technologies in collaboration with Rice University and Texas A&M and has 18 patents to his name.

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Education

  • Medical School:

    Medical College Of Pennsylvania

  • Residency:

    Mayo Graduate School of Medicine

  • Fellowships:

    Mayo Graduate School of Medicine

Academic & Clinical Affiliations

Certifications

  • American Board of Internal Medicine, Cardiology (2001, 2011)
  • American Board of Internal Medicine, Clinical Cardiology Electrophysiology (2002, 2012)

Honors, Awards and Memberships

Publications

Segar, M. W., Zhang, A., Paisley, R. D. et al. (2023). Risk stratification in patients who underwent percutaneous left atrial appendage occlusion. Am J Cardiol 200, 50–56. https://doi.org/10.1016/j.amjcard.2023.05.019.
Kim, S. E., Khawaja, M., Kim, J. A. et al. (2023). Detection of atrial fibrillation in real world setting in patients with cryptogenic stroke and an implantable loop recorder. Pacing Clin Electrophysiol 46, 788–795. https://doi.org/10.1111/pace.14757.
Elgalad, A., Hanafy, A. E., Moctezuma-Ramirez, A. et al. (2023). Use of ethanol injections to create a complete atrioventricular block in a rat model. Surgeries 4, 188–195. https://doi.org/10.3390/surgeries4020020.
Zhang, Y., Banta, A., Fu, Y. et al. (2022). RT-RCG: Neural network and accelerator search towards effective and real-time ECG reconstruction from intracardiac electrograms. J Emerg Technol Comput Syst 18, 1–25. https://doi.org/10.1145/3465372.
Toloczko, A., Buchan, S., John, M. et al. (2022). Insurance lesions: Does a second lesion make a difference? J Cardiovasc Electrophysiol. https://doi.org/10.1111/jce.15497.
Paisley, R. D., Tan, M. C., Razavi, M. et al. (2022). Iatrogenic macroreentry arising after transseptal puncture: A case series. HeartRhythm Case Rep 8, 270–274. https://doi.org/10.1016/j.hrcr.2022.01.013.
Hellar, J., Cosentino, R., John, M. et al. (2022). Manifold approximating graph interpolation of cardiac local activation time. IEEE Trans Biomed Eng PP. https://doi.org/10.1109/TBME.2022.3166447.
John, M. M., Banta, A., Post, A. et al. (2022). Artificial intelligence and machine learning in cardiac electrophysiology. Tex Heart Inst J 49, e217576. https://doi.org/10.14503/THIJ-21-7576.
Hellar, J., Cosentino, R., John, M. M. et al. (2021). Graph-based interpolation of local activation time on the cardiac surface. 2021 55th Asilomar Conference on Signals, Systems, and Computers, 274–278. https://doi.org/10.1109/IEEECONF53345.2021.9723133.
Post, A. D., Buchan, S., John, M. et al. (2021). Reconstituting electrical conduction in soft tissue: the path to replace the ablationist. Europace, euab187. https://doi.org/10.1093/europace/euab187.

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