alexa Enhanced Technique via Filters for Seizure Prediction | OMICS International | Abstract
ISSN: 2155-9538

Journal of Bioengineering & Biomedical Science
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Research Article

Enhanced Technique via Filters for Seizure Prediction

Ahmed S*, El-Khobby H, Mahmoud A and Abd El-Samie FE

Electronics and Communications Department, Faculty of Engineering, Tanta University, Egypt

*Corresponding Author:
Ahmed S
Electronics and Communications Department
Faculty of Engineering, Tanta University, Egypt
Tel: +201226521508
E-mail: [email protected]

Received date: December 07, 2016; Accepted date: February 28, 2017; Published date: March 05, 2017

Citation: Ahmed S, El-Khobby H, Mahmoud A, El-Samie AFE (2017) Enhanced Technique via Filters for Seizure Prediction. J Bioengineer & Biomedical Sci 7:218. doi: 10.4172/2155-9538.1000218

Copyright: © 2017 Ahmed S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

This research study reports on the effective band of EEG signal to be used in seizure prediction, such as gamma, beta, alpha etc. The exercises were performed on a patient-specific framework for Electroencephalography (EEG) channel selection and seizure prediction, based on statistical probability distributions of the EEG signals. This framework is an enhanced method consists of two major phases, training and testing. Our objective was to distinguish between predicted and normal EEG signals. We achieved high prediction efficiency in reasonable time with low false alarm rate considering the parameters of seizure prediction techniques. Overall, we reached an efficiency of 96.2485% with prediction time of 54.012 min and false alarm rate of 0.10526/h. This approach is having considerable significance. It is a simple method which depends on all filtering technique. This method can be implemented easily in future work and it doesn’t have much computational load.

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