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Epileptic Seizure Detection Using Singular Values And Classical Features Of EEG Signals

Ahmed, Ahmed Elsayed Elmahdy (2014) Epileptic Seizure Detection Using Singular Values And Classical Features Of EEG Signals. Universiti Teknologi PETRONAS. (Unpublished)

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This project aims at developing an automated epileptic seizure event detection algorithm. The proposed algorithm depends on using five features which are singular values, total power, delta band power, variance and mean. In this algorithm a sliding window of one second is utilized to check for epileptic seizure at each second and the classification method used is support vector machine (SVM). The proposed algorithm was tested through using CHB-MIT Scalp EEG Database which was recorded in the children hospital in Boston. The results were evaluated in terms of accuracy, sensitivity, specificity and failure rate. The results showed that the proposed algorithm is successful in identifying epileptic seizures. An average accuracy of 94.82% was achieved.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Design
Subject: Electrical and Electronics > Instrumentation and Control
Divisions: Engineering > Electrical and Electronic
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 05 Nov 2014 07:43
Last Modified: 25 Jan 2017 09:37
URI: http://utpedia.utp.edu.my/id/eprint/14405

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