EEG time series learning and classification using a hybrid forecasting model calibrated with GVNS

Coelho, Vitor N.; Coelho, Igor Machado; Coelho, Vitor Nazário; Souza, Marcone Jamilson Freitas; Guimarães, Frederico Gadelha; Luz, Eduardo; Barbosa, Alexandre Carvalho; Coelho, Mateus N.; Netto, Guilherme Gaigher; Costa, R.C.; Pinto, Alysson A.; Figueiredo, Augusto; Elias, Mohammed; Filho, D.C.O.G.; Oliveira, Thays A.

doi:10.1016/j.endm.2017.03.011

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…A comprehensive visual analysis of EEG recordings by doctors is cumbersome as it takes too much time and can be subjective and prone to human error. Hence, an automated seizure detection approach is required to accelerate the analysis of EEG recordings and obtain more accurate predictions [ 25 , 26 , 27 , 28 ].…”

Section: Medical Applicationsmentioning

confidence: 99%

Medical Applications of Nonadditive Entropies

Tsallis

Pasechnik

2023

Entropy

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The Boltzmann–Gibbs additive entropy SBG=−k∑ipilnpi and associated statistical mechanics were generalized in 1988 into nonadditive entropy Sq=k1−∑ipiqq−1 and nonextensive statistical mechanics, respectively. Since then, a plethora of medical applications have emerged. In the present review, we illustrate them by briefly presenting image and signal processings, tissue radiation responses, and modeling of disease kinetics, such as for the COVID-19 pandemic.

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Section: Medical Applicationsmentioning

confidence: 99%

Medical Applications of Nonadditive Entropies

Tsallis

Pasechnik

2023

Entropy

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“…In that study, the prediction of EEG was supported with a classification process extraction done by linear discriminant analysis (LDA). By following the concept of brain fingerprinting, Coelho et al used the General variable neighborhood search (GVNS) technique to optimize fuzzy rules to achieve a classification of individuals [64]. Although the study is not directly about predicting EEG time series, it can be evaluated within the same scope because of the prediction done with current EEG patterns to catch differences.…”

Section: A Brief Review Of the Literaturementioning

confidence: 99%

An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

Köse

2018

Applied Sciences

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The prediction of future events based on available time series measurements is a relevant research area specifically for healthcare, such as prognostics and assessments of intervention applications. A measure of brain dynamics, electroencephalogram time series, are routinely analyzed to obtain information about current, as well as future, mental states, and to detect and diagnose diseases or environmental factors. Due to their chaotic nature, electroencephalogram time series require specialized techniques for effective prediction. The objective of this study was to introduce a hybrid system developed by artificial intelligence techniques to deal with electroencephalogram time series. Both artificial neural networks and the ant-lion optimizer, which is a recent intelligent optimization technique, were employed to comprehend the related system and perform some prediction applications over electroencephalogram time series. According to the obtained findings, the system can successfully predict the future states of target time series and it even outperforms some other hybrid artificial neural network-based systems and alternative time series prediction approaches from the literature.

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