IJPR  articles are Indexed in SCOPUSClick Here     Impact Factor for Five Years is 1.55 (2012 - 2016).    Offical Sponser for International Conference in malaysia ,  https://www.icmhas.com/

logo

INTERNATIONAL JOURNAL OF PHARMACEUTICAL RESEARCH

A Step Towards Excellence

IJPR included in UGC-Approved List of Journals - Ref. No. is SL. No. 4812 & J. No. 63703

ISSN
0975-2366
5 - Years Impact Factor

Year 2012 - 2016

Impact Factor: 1.55

Total Publications: 317

Total Citation: 491

Year 2011 - 2015

Impact Factor: 1.46

Total Publications: 326

Total Citation: 477

Year 2010 - 2014

Impact Factor: 1.3

Total Publications: 313

Total Citation: 407

Year 2009 - 2013

Impact Factor: 0.973

Total Publications: 293

Total Citation: 285

Current Issue
22nd NATIONAL CONVENTION

22nd National Convention of Society of Pharmacognosy & International Conference. For more details visit


For More Detail Visit ncsp.ganpatuniversity.ac.in
Article In Press
ADOBE READER

(Require Adobe Acrobat Reader to open, If you don't have Adobe Acrobat Reader)

Index Page 1
Click here to Download
IJPRT ISSUE

January - March 6 [1] 2014

Click to download
IJPR 9[3] July - September 2017 Special Issue

July - September 9[3] 2017

Click to download
PIPPHARMACON & IJPR 2018

PIPPHARMACON & IJPR 2018

Click to download
 

Article Detail

Label
Label
Epileptic Seizure Classification Using Feed Forward Neural Network Based on Parametric Features

Author: RAJENDRAN T, SRIDHAR K P
Abstract: Globally, epilepsy is a severe neural disorder occurring among 0.6-0.8% of the population. The formation of patternchange from normal to disturbed factors that all gets triggered at once is called seizure. Many researchers introduced different techniques, but the problem of detecting epileptic seizures remains unsolved. This paper presents a new technique for detection of epileptic seizure-based Electroencephalogram (EEG) signals. The detection scheme adapts the non-invasive measure of the brain’s electrical activity by placing the electrodes on the scalp. The collection of such electrical activity and diagnosing is a complex task because the brain is composed of numerous classes with numerous overlying features. Feature extraction based on parametric and non-parametric method is employed to extract the features vectors from EEG signals. The extracted features are forwarded to machine learning algorithms. Feed Forward Neural Network (FFNN) is implemented to detect the epileptic seizure. The performance results are evaluated by comparison of previous Modified Back Propagation Neural Network, Multilayer perceptron neural network, combined neural network, Probabilistic Neural Network and FFNN methods with respect to feature extraction in terms of accuracy, specificity and sensitivity. The FFNN has the higher classification accuracy as 97.23% demonstrates that it has a great potentiality of the real-time epileptic seizure detection.
Keyword: Epilepsy Seizure, Electroencephalogram, Feature Extraction, Neural Network and Auto Regressive
DOI: https://doi.org/10.31838/ijpr/2018.10.04.046
Download: Request For Article
 












ONLINE SUBMISSION
USER LOGIN


Username
Password
Login | Register
AICTE INTERNATIONAL CONFERENCE

AICTE Sponsored International Conference on Challenges, Opportunities and Newer Directions of Pharmacovigilance and Clinical Research in India

Download Brochure


For More Detail Visit www.pippharmacon.org
News & Events
hit counters free
0.07
2017CiteScore
 
11th percentile
Powered by  Scopus
Impact Factor for five years is 1.55 (2012 - 2016)

Year 2011 - 2015 Impact Factor - 1.46 Total Publications - 326 Total Citations - 477