Article Per Year (5 Year)
p-Index From 2019 - 2024
0
P-Index
This Author published in this journals
All Journal International Journal of Public Health Science (IJPHS)
Sharanreddy Mallikarjun Akareddy
PDA College of Engineering
Health Professions

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
EEG signal classification for Epilepsy Seizure Detection using Improved Approximate Entropy Sharanreddy Mallikarjun Akareddy; P.K. Kulkarni
International Journal of Public Health Science (IJPHS) Vol 2, No 1: March 2013
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (218.787 KB)

Abstract

Epilepsy is a common chronic neurological disorder. Epilepsy seizures are the result of the transient and unexpected electrical disturbance of the brain. About 50 million people worldwide have epilepsy, and nearly two out of every three new cases are discovered in developing countries. Epilepsy is more likely to occur in young children or people over the age of 65 years; however, it can occur at any age. The detection of epilepsy is possible by analyzing EEG signals. This paper, presents a hybrid technique to classification EEG signals for identification of epilepsy seizure. Proposed system is combination of multi-wavelet transform and artificial neural network. Approximate Entropy algorithm is enhanced (called as Improved Approximate Entropy: IApE) to measure irregularities present in the EEG signals. The proposed technique is implemented, tested and compared with existing method, based on performance indices such as sensitivity, specificity, accuracy parameters. EEG signals are classified as normal and epilepsy seizures with an accuracy of ~90%.DOI: http://dx.doi.org/10.11591/ijphs.v2i1.1836
Co-Authors P.K. Kulkarni