Journal of Biomedical Science and Engineering

Volume 4, Issue 12 (December 2011)

ISSN Print: 1937-6871   ISSN Online: 1937-688X

Google-based Impact Factor: 0.66  Citations  h5-index & Ranking

A review of developments of EEG-based automatic medical support systems for epilepsy diagnosis and seizure detection

HTML  Download Download as PDF (Size: 4610KB)  PP. 788-796  
DOI: 10.4236/jbise.2011.412097    8,002 Downloads   14,946 Views  Citations
Author(s)

Affiliation(s)

.

ABSTRACT

Epilepsy is one of the most common neurological disorders-approximately one in every 100 people worldwide are suffering from it. The electroencephalogram (EEG) is the most common source of information used to monitor, diagnose and manage neurological disorders related to epilepsy. Large amounts of data are produced by EEG monitoring devices, and analysis by visual inspection of long recordings of EEG in order to find traces of epilepsy is not routinely possible. Therefore, automated detection of epilepsy has been a goal of many researchers for a long time. Until now, reviews of epileptic seizure detection have been published but none of them has specifically reviewed developments of automatic medical support systems utilized for EEG-based epileptic seizure detection. This review aims at filling this lack. The main objective of this review will be to briefly discuss different methods used in this research field and describe their critical properties.

Share and Cite:

Song, Y. (2011) A review of developments of EEG-based automatic medical support systems for epilepsy diagnosis and seizure detection. Journal of Biomedical Science and Engineering, 4, 788-796. doi: 10.4236/jbise.2011.412097.

Cited by

[1] Detection of epileptic seizure disorder using EEG signals
Artificial Intelligence-Based Brain-Computer …, 2022
[2] Robust Optimization of electroencephalograph (EEG) Signals for Epilepsy Seizure Prediction by utilizing VSPO Genetic Algorithms with SVM and Machine …
2021
[3] Non-Invasive Testing of Physical Systems Using Topological Sensitivity
2021
[4] An efficient method for selecting the optimal features using evolutionary algorithms for epilepsy diagnosis
2020
[5] Sputtered porous Pt for wafer-scale manufacture of low-impedance flexible microelectrodes
2020
[6] Ultra-low-power implementation of a parameters identification algorithm for interpretable detection of epileptic seizures
2020
[7] Epileptic Seizure Detection and Experimental Treatment: A Review
2020
[8] Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
2020
[9] Automatic detection of cursor movements from the EEG signals via deep learning approach
2020
[10] Epileptic seizures detection based on some new Laguerre polynomial wavelets, artificial neural networks and support vector machines
2019
[11] Comparison of Fuzzy Output Optimization with Expectation Maximization Algorithm and Its Modification for Epilepsy Classification
Proceedings of International Conference on Cognition and Recognition, 2018
[12] EEG signals classification based on autoregressive and inherently quantum recurrent neural network
2018
[13] Advances in Pattern Recognition
2018
[14] Hilbert D?nü?ümü Ve Teager Enerji Kullanilarak EEG ??aretlerinde Epileptik N?bet Tespiti Detection of Epileptic Seizure from EEG Signals by Using Teager Energy and Hilbert Transform
2017
[15] Automatic Epileptic Seizure Detection in EEG Using Nonsubsampled Wavelet–Fourier Features
Journal of Medical and Biological Engineering, 2017
[16] An extensive review on development of EEG-based computer-aided diagnosis systems for epilepsy detection
Tellus A: Dynamic Meteorology and Oceanography, 2017
[17] Automated recognition of epilepsy from EEG signals
2017
[18] Detection of epileptic seizure from EEG signals by using teager energy and Hilbert transform
2017
[19] A Review of Automated Methodologies for the Detection of Epileptic Episodes Using Long-Term EEG Signals
2016
[20] Time-Frequency Analysis of Epileptic EEG for Seizure Detection
International Journal of Computer Science and Information Security, 2016
[21] Epileptic seizure detection from EEG signals by using wavelet and Hilbert transform
2016
[22] Classification of EEG data sets with Hilbert transform
2016
[23] Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network
International Journal of Electrical and Computer Engineering (IJECE), 2015
[24] Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features
Expert Systems with Applications, 2014
[25] Epilepsy Seizure Detection in EEG Signals Using Wavelet Transforms and Neural Networks
E Juárez-Guerra, V Alarcon-Aquino, P Gómez-Gil - ccc.inaoep.mx, 2014
[26] Automated Detection of Interictal Spikes in EEG: A literature review
FD de Moraes, DA Callegari - pucrs.br, 2014
[27] DALGACIK DÖNÜŞÜMÜ KULLANILARAK EEG İŞARETLERİNDE EPİLEPTİK NÖBET TESPİTİ DETECTION OF EPILEPTIC SEIZURES IN EEG SIGNALS VIA APPLYING WAVELET TRANSFORM
2014
[28] DALGACIK DÖNÜŞÜMÜ KULLANILARAK EEG İŞARETLERİNDE EPİLEPTİK NÖBET TESPİTİ DETECTION OF EPILEPTIC SEIZURES IN EEG SIGNALS VIA …
2014
[29] Selection of proper frequency band and compatible features for left and right hand movement from EEG signal analysis
2013 16th International Conference on Computer and Information Technology (ICCIT), 2013
[30] Biomedical Signal Processing Using Wavelet-Based Neural Networks
Special Issue: Advances in Pattern Recognition, 2013
[31] Artefact detection and removal algorithms for EEG diagnostic systems
2013
[32] Time-Frequency Techniques using Band Limited Wavelets Applied to Detect Epileptic Events in EEG
[33] DALGACIK DÖNÜŞÜMÜ KULLANILARAK EEG İŞARETLERİNDE EPİLEPTİK NÖBET TESPİTİ DETECTION OF EPILEPTIC SEIZURES IN EEG SIGNALS …
M NERGİZ, MS ÖZERDEM

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.