An extensive review on development of EEG-based computer-aided diagnosis systems for epilepsy detection. (2nd January 2017)

• Record Type: Journal Article
• Authors: Saini, Jagriti; Dutta, Maitreyee
• Is Part Of: Network. Volume 28:Number 1(2017)
• Page Start: 1
• Page End: 27
• Publication Date: 2017-01-02
• Subjects: Artificial neural network (ANN) -- epilepsy -- electroencephalogram (EEG) -- neural disorders -- object recognition -- wavelets; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; 006.32
• DOI: 10.1080/0954898X.2017.1325527
• Languages: English
• ISSNs: 0954-898X

Abstract

ABSTRACT: Epilepsy is considered as fourth most prominent neurological disorder in the world that can affect people of all age groups. Currently, around 65 million people throughout the world are suffering from epilepsy. It is evident that electroencephalograph (EEG) signals are most commonly used for detection of epileptic seizures but today many modern techniques have been developed to analyze underlying features of these EEG signals. As EEG contains a large amount of complicated information, so many researchers are trying to develop automatic systems for complete feature extraction. This paper provides a generalized review and performance comparison of popular seizure detection algorithms that are developed in the last decade. The main objective of this paper is to briefly discuss all existing developments in the field of computer-aided diagnosis system for epilepsy detection so that future researchers can find a better track for the new invention.