A systematic review on detection and estimation algorithms of EEG signal for epilepsy

S. Hasan, Ameya K. Kulkarni, Sebamai Parija, P. Dash
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Abstract

Epilepsy is the most common neurological disorder characterised by a sudden and recurrent neuronal firing in the brain. As EEG records the electrical activity of the brain so it helps to detect epilepsy of the subject. Early detection of epileptic seizure using EEG signal is most useful in several diagnoses. So aim of the work is to study and compare the different techniques used for feature extraction and classification algorithm. Epilepsy detection research is oriented to develop non-invasive and precise methods to allow accurate and quick diagnose. In this paper, we present a review of significant researches where we can find most suitable method among existing members to improve computing efficiency and detect epilepsy of the subject efficiently and accurately with lesser computational time. The database which is publicly available at Bonn University is taken.
癫痫脑电信号检测与估计算法的系统综述
癫痫是最常见的神经系统疾病,其特征是大脑中突然和反复的神经元放电。由于脑电图记录了大脑的电活动,所以它有助于检测受试者的癫痫。利用脑电图信号早期发现癫痫发作在几种诊断中是最有用的。因此,本文的目的是研究和比较不同的特征提取技术和分类算法。癫痫检测研究的方向是发展无创和精确的方法,以实现准确和快速的诊断。在本文中,我们回顾了一些重要的研究成果,在现有的成员中找到最合适的方法来提高计算效率,以更少的计算时间高效准确地检测受试者的癫痫。这是波恩大学公开提供的数据库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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