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Journal of Healthcare Engineering
Volume 6, Issue 2, Pages 159-178
Research Article

Epileptic Seizure Detection and Prediction Based on Continuous Cerebral Blood Flow Monitoring – a Review

Senay Tewolde,1 Kalarickal Oommen,2 Donald Y. C. Lie,3 Yuanlin Zhang,4 and Ming-Chien Chyu1,5

1Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USA
2Covenant Comprehensive Epilepsy Center, Covenant Health System, Lubbock, Texas, USA
3Department of Electrical and Computer Engineering, USA
4Department of Computer Science, USA
5Graduate Healthcare Engineering, Texas Tech University, Lubbock, TX, USA

Received 1 January 2015; Accepted 1 April 2015

Copyright © 2015 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Epilepsy is the third most common neurological illness, affecting 1% of the world’s population. Despite advances in medicine, about 25 to 30% of the patients do not respond to or cannot tolerate the severe side effects of medical treatment, and surgery is not an option for the majority of patients with epilepsy. The objective of this article is to review the current state of research on seizure detection based on cerebral blood flow (CBF) data acquired by thermal diffusion flowmetry (TDF), and CBF-based seizure prediction. A discussion is provided on the applications, advantages, and disadvantages of TDF in detecting and localizing seizure foci, as well as its role in seizure prediction. Also presented are an overview of the present challenges and possible future research directions (along with methodological guidelines) of the CBF-based seizure detection and prediction methods.