Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

Analysis of Epileptic Seizure Using Wavelet Transform

Mustaffa, Muhammad Dinie Akmal (2016) Analysis of Epileptic Seizure Using Wavelet Transform. IRC, Universiti Teknologi PETRONAS. (Submitted)

[img] PDF
Download (1772Kb)


In this work, wavelet transform (WT) is used to analyze epileptic seizure in recorded EEG signals. Wavelets allow non-stationary EEG signals to be decomposed into elementary forms at different positions and scales. The extracted features from the WT decomposition is then expressed in terms wavelet based and classical based features to be further analyzed. In general, the coefficients of a 1-D wavelet decomposition comprises of approximate and detail coefficient, arranged in a single row. The number of wavelet coefficients depends on the decomposition level with more coefficients at high decomposition level. The features generated from wavelet transform is tested in terms of discriminatory information and the highly informative features will be identified. To select the best features, Fisher Discriminant Ratio (FDR) is implemented and classification error was calculated using Support Vector Machine (SVM). When FDR is applied, amongst all the 23 channels, certain channels will be dominant over the other channels in terms of value and these channels are then be chosen for the reduced feature analysis.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Design
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 19 Jan 2017 15:38
Last Modified: 25 Jan 2017 09:34
URI: http://utpedia.utp.edu.my/id/eprint/17099

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...