Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures

Dhiman, Rohtash; Saini, J.S.; Priyanka, .

doi:10.1016/j.asoc.2014.01.029

Cited by 61 publications

(26 citation statements)

References 37 publications

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“…Ahila et al [27] present a PSO method for tuning Extreme Learning Machines (ELM) and for FS in the classification of power system disturbances. Dhiman et al [28] propose a hybrid approach with wavelet packet decomposition and a GA-SVM scheme for FS and MPO to obtain classification models capable of detecting epileptic seizures from background electroencephalogram signals. Castillo et al [29,30] optimize membership functions of complex fuzzy controllers with ant colony optimization (ACO).…”

Section: Related Research On Parsimonious Modeling With Soft Computingmentioning

confidence: 99%

GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace

Sanz-García

Ceniceros

Antoñanzas-Torres

et al. 2015

Applied Soft Computing

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Section: Related Research On Parsimonious Modeling With Soft Computingmentioning

confidence: 99%

GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace

Sanz-García

Ceniceros

Antoñanzas-Torres

et al. 2015

Applied Soft Computing

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“…The significant differences between epileptic seizure and healthy states are generally highlighted as the frequency and patterns of neurons, meaning the spatial-temporal patterns of neurons gradually increase from the normal state to epileptic seizure-free state and then to the epileptic seizure state [7,9]. The World Health Organization (WHO) stipulates that epilepsy is caused by a group of brain cells with unexpected, uncontrolled electrical discharges, termed 'epileptic seizures' [6,9,10]. In 1929, Berger first measured the spontaneous electrical activity in the brain using electrodes, with electroencephalographic (EEG) signals being produced.…”

Section: Introductionmentioning

confidence: 99%

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

Ren

Chai

et al. 2018

Applied Sciences

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This study analyzes bioelectrical signals to achieve automatic epileptic seizure detection. Electroencephalographic (EEG) signals were recorded with electrodes on healthy, epileptic seizure-free, and epileptic seizure patients. The challenges in this field are generally regarded to be the impacts of non-stationarity and nonlinearity in EEG signals. To address these challenges, this study attempts to recognize different brain statuses. The idea originated from a novel hypothesis that considers EEG signals as convolution signals and regards itself as the generation mechanism of EEG signals, to some extent. Based on this hypothesis, the nonlinear problem can be viewed as a deconvolution procedure. As such, the method can be simplified into three parts: eliminating non-stationary is used to catch high-frequency to low-frequency signals, which is followed by a local mean decomposition (LMD) algorithm; these signals are deconvoluted to form ultra-high-dimensional feature sets, which is completely terminated by the mel-frequency cepstrum coefficients (MFCC) algorithm; and several classifiers are combined to achieve highly accurate recognition results and to verify the superiority and reasonableness of this method. The publicly available EEG database from the University of Bonn, Germany is employed to demonstrate the effectiveness and outstanding performance of this method. According to the results, the method has the ability to attain a higher average classification accuracy than other methods in all of the four following cases: healthy (datasets A and B) versus epileptic seizure (dataset E), epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), healthy (datasets A and B) versus epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), and healthy (dataset A) versus healthy (dataset B) versus epileptic seizure-free (dataset C) versus epileptic seizure-free (dataset D) versus epileptic seizure (dataset E).

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“…Once the features are extracted, these features are processed for classification. The final step employs different classifiers, such as adaptive neuro‐fuzzy inference system classifiers (Güler et al, ), neural networks (Alam & Bhuiyan, ; Guo, Rivero, Dorado, et al, ; Guo, Rivero, & Pasos, ; Kumar et al, ; Pachori & Patidar, ; Srinivasan et al, ; Übeyli, ; Übeyli, ), and support vector machines (Dhiman et al, ; Fu et al, ; Joshi, Pachori, & Vijesh, ; Parvez & Paul, ; Sharma & Pachori, ; Xiang et al, ). Feature analysis is often ambiguous and computationally complex.…”

Section: Introductionmentioning

confidence: 99%

“…Frequency analysis typically employs Fourier transform, although Fourier transform is now often replaced by time/frequency analyses. For time/frequency analyses, the time series signal is transformed through wavelet transforms/wavelet packet decompositions (Chen, 2014;Dhiman et al, 2014;Güler, Übeyli, & Güler, 2005;Guo, Rivero, Dorado, Rabunal, & Pasos, 2010b;Guo, Rivero, & Pasos, 2010a;Kumar, Dewal, & Anand, 2012a;Ocak, 2009;Wang, Miao, & Xie, 2011). The features are then extracted through transformations in each selected subband using energy, entropies, or a modified mixture of methods.…”

mentioning

confidence: 99%

Electroencephalography‐based feature extraction using complex network for automated epileptic seizure detection

2017

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Electroencephalography signals are typically used for analyzing epileptic seizures. These signals are highly nonlinear and nonstationary, and some specific patterns exist for certain disease types that are hard to develop an automatic epileptic seizure detection system. This paper discussed statistical mechanics of complex networks, which inherit the characteristic properties of electroencephalography signals, for feature extraction via a horizontal visibility algorithm in order to reduce processing time and complexity. The algorithm transforms a time series signal into a complex network, which some features are abbreviated. The statistical mechanics are calculated to capture distinctions pertaining to certain diseases to form a feature vector. The feature vector is classified by multiclass classification via a k‐nearest neighbor classifier, a multilayer perceptron neural network, and a support vector machine with a 10‐fold cross‐validation criterion. In performance evaluation of proposed method with healthy, seizure‐free interval, and seizure signals, firstly, input data length is regarded among some practical signal samples by optimizing between accuracy‐processing time, and the proposed method yields outstanding performance on the average classification accuracy for 3‐class problems mainly for detection of seizure‐free interval and seizure signals and acceptable results for 2‐class and 5‐class problems comparing with conventional methods. The proposed method is another tool that can be used for classifying signal patterns, as an alternative to time/frequency analyses.

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Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures

Cited by 61 publications

References 37 publications

GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace

GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

Electroencephalography‐based feature extraction using complex network for automated epileptic seizure detection

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