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Various epileptic seizure detection techniques using biomedical signals: a review - 2018

Various Epileptic Seizure Detection Techniques Using Biomedical Signals: A Review

Survey Paper on Various Epileptic Seizure Detection Techniques Using Biomedical Signals

Research Area:  Machine Learning


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.

Epileptic Seizure Detection
Biomedical Signals
Machine Learning
Deep Learning

Author(s) Name:  Yash Paul

Journal name:  Brain Informatics

Conferrence name:  

Publisher name:  Springer

DOI:  10.1186/s40708-018-0084-z

Volume Information:  volume 5, Article number: 6 (2018)