Literature DB >> 21763347

EPILAB: a software package for studies on the prediction of epileptic seizures.

C A Teixeira1, B Direito, H Feldwisch-Drentrup, M Valderrama, R P Costa, C Alvarado-Rojas, S Nikolopoulos, M Le Van Quyen, J Timmer, B Schelter, A Dourado.   

Abstract

A Matlab®-based software package, EPILAB, was developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented. This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines. EPILAB proved to be an efficient tool for seizure prediction, and aims to be a way to communicate, evaluate, and compare results and data among the seizure prediction community.
Copyright © 2011 Elsevier B.V. All rights reserved.

Entities:  

Mesh:

Year:  2011        PMID: 21763347     DOI: 10.1016/j.jneumeth.2011.07.002

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  11 in total

Review 1.  Recording and analysis techniques for high-frequency oscillations.

Authors:  G A Worrell; K Jerbi; K Kobayashi; J M Lina; R Zelmann; M Le Van Quyen
Journal:  Prog Neurobiol       Date:  2012-03-07       Impact factor: 11.685

2.  Gender-Related Differences in Heart Rate Variability of Epileptic Patients.

Authors:  Soroor Behbahani; Nader Jafarnia Dabanloo; Ali Motie Nasrabadi; Antonio Dourado
Journal:  Am J Mens Health       Date:  2016-03-18

3.  Slow modulations of high-frequency activity (40-140-Hz) discriminate preictal changes in human focal epilepsy.

Authors:  C Alvarado-Rojas; M Valderrama; A Fouad-Ahmed; H Feldwisch-Drentrup; M Ihle; C A Teixeira; F Sales; A Schulze-Bonhage; C Adam; A Dourado; S Charpier; V Navarro; M Le Van Quyen
Journal:  Sci Rep       Date:  2014-04-01       Impact factor: 4.379

4.  MOBBED: a computational data infrastructure for handling large collections of event-rich time series datasets in MATLAB.

Authors:  Jeremy Cockfield; Kyungmin Su; Kay A Robbins
Journal:  Front Neuroinform       Date:  2013-10-10       Impact factor: 4.081

5.  Dual adaptive filtering by optimal projection applied to filter muscle artifacts on EEG and comparative study.

Authors:  Samuel Boudet; Laurent Peyrodie; William Szurhaj; Nicolas Bolo; Antonio Pinti; Philippe Gallois
Journal:  ScientificWorldJournal       Date:  2014-09-14

6.  Epileptic Seizure Prediction based on Ratio and Differential Linear Univariate Features.

Authors:  Jalil Rasekhi; Mohammad Reza Karami Mollaei; Mojtaba Bandarabadi; César A Teixeira; António Dourado
Journal:  J Med Signals Sens       Date:  2015 Jan-Mar

7.  Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index.

Authors:  Muhammad Tariq Sadiq; Xiaojun Yu; Zhaohui Yuan; Muhammad Zulkifal Aziz
Journal:  Sensors (Basel)       Date:  2020-09-16       Impact factor: 3.576

8.  Wearable, Multimodal, Biosignal Acquisition System for Potential Critical and Emergency Applications.

Authors:  Chin-Teng Lin; Chen-Yu Wang; Kuan-Chih Huang; Shi-Jinn Horng; Lun-De Liao
Journal:  Emerg Med Int       Date:  2021-06-10       Impact factor: 1.112

9.  Predicting epileptic seizures in advance.

Authors:  Negin Moghim; David W Corne
Journal:  PLoS One       Date:  2014-06-09       Impact factor: 3.240

Review 10.  Neural stimulation systems for the control of refractory epilepsy: a review.

Authors:  Matthew D Bigelow; Abbas Z Kouzani
Journal:  J Neuroeng Rehabil       Date:  2019-10-29       Impact factor: 4.262

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