Epileptic seizures are manifestations of epilepsy. Careful analysis of EEG records can provide valuable insight and improved understanding of the mechanism causing epileptic disorders. The detection of epileptic form discharges in EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional frequency and time domain analysis does not provide better accuracy. So, in this work an attempt has been made to provide an overview of the determination of epilepsy by implementation of Hurst exponent (HE)-based discrete wavelet transform techniques for feature extraction from EEG data sets obtained during ictal and pre ictal stages of affected person and finally classifying EEG signals using SVM and KNN Classifiers. The The highest accuracy of 99% is obtained using SVM. © 2017 Informa UK Limited, trading as Taylor & Francis Group.

Sharmila A

Department of Control and Automation

School of Electrical Engineering

Vellore Campus

Mahalakshmi P

Department of Instrumentation

Madan S

Srivastava K

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

A case study on Discrete Wavelet Transform based Hurst exponent for epilepsy detection.

Journal of Medical Engineering & Technology

Pattern recognition strategies are applied for detecting epileptic abnormality from Electroencephalogram (EEG) signals. So, in this work, it is applied for detecting epileptic abnormality from EEG signals of normal subjects and epileptic patients. In this paper, a novel pattern recognition is studied with statistical features derived from discrete wavelet transform (DWT) coefficients D3-D5 and A5 to detect epileptic seizure abnormality using naïve Bayes (NB) and k-nearest neighbor (k-NN) classifier for fourteen different combinations such as set A to D with set E. In this work, EEG data provided by University of Bonn, Germany, and Christian Medical College and Hospital (CMCH), India are used. Using feature selection and feature ranking based on the estimation of mutual information, the significant features required for the classifier to get higher accuracy is obtained. It is found that NB performs better for 9 different data sets combination and k-NN performs well for 5 data sets. Further, NB achieves 100% classification accuracy (CA) in distinguishing normal eyes open and epileptic dataset and k-NN gives 100% accuracy with only top ranked 14 features in using CMCH data. This shows that proposed system has potential in developing automatic computer-aided epileptic seizure detection systems with lesser computation time to enhance the patient's care and quality of life. © Copyright 2016 American Scientific Publishers.

Journal of Medical Imaging and Health Informatics

Detection of Epileptic Seizure from Electroencephalogram Signals Based on Feature Ranking and Best Feature Subset Using Mutual Information Estimation

Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the 14 different combinations of two-class epilepsy detection is studied using naïve Bayes (NB) and k-nearest neighbor (k-NN) classifiers for the derived statistical features from DWT. It has been found that the NB classifier performs better and shows an accuracy of 100% for the individual and combined statistical features derived from the DWT values of normal eyes open and epileptic EEG data provided by the University of Bonn, Germany. It has been found that the computation time of NB classifier is lesser than k-NN to provide better accuracy. So, the detection of an epileptic seizure based on DWT statistical features using NB classifiers is more suitable in real time for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life. © 2013 IEEE.

Fulltext

IEEE Access

DWT Based Detection of Epileptic Seizure From EEG Signals Using Naive Bayes and k-NN Classifiers

Biocybernetics and Biomedical Engineering

Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM

IEEE Transactions on Neural Systems and Rehabilitation Engineering

EMD-Based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning

Applied Mathematics and Computation

1D-local binary pattern based feature extraction for classification of epileptic EEG signals

A case study on Discrete Wavelet Transform based Hurst exponent for epilepsy detection

Journal	Journal of Medical Engineering & Technology
Publisher	Informa UK Limited
ISSN	0309-1902
Open Access	No