Over many decades, research is being attempted for the detection of epileptic seizure to support for automatic diagnosis system to help clinicians from burdensome work. In this respect, an enormous number of research papers is published for identification of epileptic seizure. It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection of an epileptic seizure. More than 100 research papers have been discussed to discern the techniques for detecting the epileptic seizure. Further, the literature survey shows that the pattern recognition required to detect epileptic seizure varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG epileptic data from a various condition of EEG data. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Sharmila A

Department of Control and Automation

School of Electrical Engineering

Vellore Campus

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

Epilepsy detection from EEG signals: a review.

Journal of Medical Engineering & Technology

AbstractOver several years, research had been conducted for the detection of epileptic seizures to support an automatic diagnosis system to comfort the clinicians’ encumbrance. In this regard, a number of research papers have been published for the identification of epileptic seizures. A thorough review of all these papers is required. So, an attempt has been made to review on the pattern detection methods for epilepsy seizure detection from EEG signals. More than 150 research papers have been discussed to determine the techniques for detecting epileptic seizures. Further, the literature review confirms that the pattern recognition techniques required to detect epileptic seizures varies across the electroencephalogram (EEG) datasets of different conditions. This is mostly owing to the fact that EEG detected under different conditions have different characteristics. This consecutively necessitates the identification of the pattern recognition technique to efficiently differentiate EEG epileptic data from the EEG data of various conditions.

Fulltext

Biomedical Engineering / Biomedizinische Technik

A review on the pattern detection methods for epilepsy seizure detection from EEG signals

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.

IEEE Access

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

The focus of this paper is the development of a low-cost research platform for a surface electromyogram (EMG)-based prosthetic hand control to evaluate various pattern recognition techniques and to study the real-time implementation. This comprehensible research platform may enlighten the biomedical research in developing countries for analyzing and evaluating the surface EMG signals. Major challenges in this work were as follows: the design and development of an EMG signal conditioning module, a pattern recognition module, and a prosthetic hand at low cost. Besides, EMG pattern recognition techniques were evaluated for identifying six hand motions in offline with signals acquired from ten healthy subjects and two transradial amputees. Features calculated from EMG signals were grouped into six ensembles to apprehend the vitality of the ensemble in classifiers namely simple logistic regression (SLR), J48 algorithm for decision tree, logistic model tree, neural network, linear discriminant analysis, and support vector machine. The classification performance was also evaluated with the prolonged EMG data recorded on a day at every 1 h interval to study the robustness of the classifier. The results show the average classification accuracy, processing time and memory requirement of the SLR was found to be better and robust with time-domain features consisting of statistical as well as autoregression coefficients. The statistical analysis of variance test also showed that computation time and memory space required for SLR were significantly less compared to the other classifiers. The performance of the classifier was tested in online with transradial amputee for actuation of prosthetic hand for two intended motions with a TMS320F28335 controller. This proposed research platform for evaluation of EMG pattern recognition and real-time implementation has been achieved at a cost of 25 000 Indian rupee (INR). © 2014 IEEE.

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