World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , 

World Journal of Engineering
Research and Technology

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.218

ICV : 79.45

News & Updation

  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

  • WJERT New Impact Factor

    Its our Pleasure to Inform you that WJERT Impact Factor has been increased from 4.236 to 5.218 due to high quality Publication at International Level

  • ICV

    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level

  • WJERT SEPTEMBER ISSUE PUBLISHED

    SEPTEMBER 2018 Issue has been successfully launched on 1 September 2018

  • New Issue Published

    Its Our pleasure to inform you that, WJERT 1 September 2018 Issue has been Published, Kindly check it on http://wjert.org/home/current_issues

Indexing

Abstract

A NON-INVASIVE DETECTION OF EPILEPTIC SEIZURE IN HUMAN SCALP ELECTROENCEPHALOGRAM USING DISCRETE WAVELET TRANSFORM

Opiarighodare D. K., *Ajala F. A., Oludipe O. and Ojebamigbe V. I.

ABSTRACT

This study shows the effectiveness of Discrete Wavelet Transform (DWT) technique in the analysis of Electroencephalogram (EEG) by performing decomposition of the EEG signals and extracting unique features for efficient diagnosis of epilepsy using standard benchmarked EEG datasets. The total dataset comprising 250 segments was grouped into five sets (A–E). The datasets used in this technique were chosen from records of EEG after artifacts caused by muscle movements and blinking of the eyes were removed. The datasets were used as inputs of the system. The technique applied includes signals preprocessing which involves synthesizing the signals which were originally represented in ASCII form, decomposition of the signals and extraction of features characterizing seizures. The researchestablished that though the decomposed EEG signals were imprecise, the wavelet detail coefficients computedgave an acceptable representation thatreveals the energy distribution of the EEGsignals in both time and frequency domain while the wavelet approximate coefficients computed gave a clearer view of spikes which indicate the presence of epileptiform activities in the EEG signals sub - band.

[Full Text Article]