Various epileptic seizure detection techniques using biomedical signals: a review

被引:60
|
作者
Paul Y. [1 ]
机构
[1] School of Informatics, Eötvös Loránd University, Budapest
关键词
Electroencephalogram (EEG); Empirical mode decomposition; Epilepsy; Fourier transform; Hilbert transform; Particle swarm optimization (PSO); Rational function; Wavelet;
D O I
10.1186/s40708-018-0084-z
中图分类号
学科分类号
摘要
Epilepsy is a chronic chaos of the central nervous system that influences individual’s daily life by putting it at risk due to repeated seizures. Epilepsy affects more than 2% people worldwide of which developing countries are affected worse. A seizure is a transient irregularity in the brain’s electrical activity that produces disturbing physical symptoms such as a lapse in attention and memory, a sensory illusion, etc. Approximately one out of every three patients have frequent seizures, despite treatment with multiple anti-epileptic drugs. According to a survey, population aged 65 or above in European Union is predicted to rise from 16.4% (2004) to 29.9% (2050) and also this tremendous increase in aged population is also predicted for other countries by 2050. In this paper, seizure detection techniques are classified as time, frequency, wavelet (time–frequency), empirical mode decomposition and rational function techniques. The aim of this review paper is to present state-of-the-art methods and ideas that will lead to valid future research direction in the field of seizure detection. © 2018, The Author(s).
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