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

Author:

Sharmila Ashok,Geethanjali Purusothaman

Abstract

Abstract Over 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.

Publisher

Walter de Gruyter GmbH

Subject

Biomedical Engineering

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1. A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network;Neuroscience Bulletin;2024-07-02

2. Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review;Artificial Intelligence Review;2024-06-21

3. Literature Survey Paper on Epilepsy and Autism Spectrum Disorder Detection and Analysis Using Machine Learning;International Journal of Advanced Research in Science, Communication and Technology;2024-02-06

4. A Machine Learning Framework for Robust Epileptic Seizure Detection from EEG Sign;2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON);2023-12-29

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