Epileptic EEG detection using the linear prediction error energy

Altunay, Semih; Telatar, Ziya; Eroğul, Osman

doi:10.1016/j.eswa.2010.02.045

Cited by 166 publications

(53 citation statements)

References 12 publications

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“…The authors obtained classification accuracy of 98.80% with a standard deviation (SD) of 0.11% in classifying seizure, seizure-free and normal groups of EEG signals. In [17], the authors modeled EEG signals by the linear prediction (LP) process, and the energy of the modeling error was used for the epileptic seizure EEG signal classification task. They achieved classification accuracy of 94% in classifying seizure and seizure-free EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

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This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high-and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

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Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

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“…Contrary to many studies using the statistical parameters to extract the features [2,4,6,[8][9][10][11][12][13][14][15][16][17][18][20][21][22][23], in this study, a new feature extraction approach is proposed, which have never been applied previously to EEG signals. The probability densities of EEG signals discretized in the time domain by using the equal frequency discretization (EFD) method were used as the inputs to a multilayer perceptron neural network (MLPNN) model in the detection of epileptic seizure.…”

Section: Introductionmentioning

confidence: 88%

“…3 The mean of probability densities of EEG segments for each set must be used together. ROC analysis is mostly preferred as a criterion of the success evaluation [17,22,40]. The success in a ROC analysis can be presented by (Specificity + Sensitivity)/2.…”

Section: Validity Criterionsmentioning

confidence: 99%

“…However, the accuracy of classification entirely depends on both the classifier model and the features to be applied to that classifier model. Many different classifiers such as autoregressive [11,15,16], approximate entropy [7], linear prediction error energy [17] and artificial neural network (ANN) [4,8,9,11,12,15,16,[18][19][20][21][22][23][24] are used in the analysis of EEG signals. Of these classifiers, ANNs are the most preferred models.…”

Section: Introductionmentioning

confidence: 99%

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Epileptic Seizure Detection Using Probability Distribution Based On Equal Frequency Discretization

2011

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In this study, we offered a new feature extraction approach called probability distribution based on equal frequency discretization (EFD) to be used in the detection of epileptic seizure from electroencephalogram (EEG) signals. Here, after EEG signals were discretized by using EFD method, the probability densities of the signals were computed according to the number of data points in each interval. Two different probability density functions were defined by means of the polynomial curve fitting for the subjects without epileptic seizure and the subjects with epileptic seizure, and then when using the mean square error criterion for these two functions, the success of epileptic seizure detection was 96.72%. In addition, when the probability densities of EEG segments were used as the inputs of a multilayer perceptron neural network (MLPNN) model, the success of epileptic seizure detection was 99.23%. This results show that non-linear classifiers can easily detect the epileptic seizures from EEG signals by means of probability distribution based on EFD.

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“…Just as a wide variety of features has been used, an equally varied set of classification methods can be found in the literature. The classification methods varied from simple threshold (Altunay, Telatar, & Erogul, 2010;Martinez-Vargas, et al, 2011), rule based decisions (Gotman, 1990(Gotman, , 1999, or linear classifiers (Ghosh-Dastidar, Iscan, et al, 2011;Liang, et al, 2010;Subasi & Gursoy, 2010) to ANNs , 2008Mousavi, et al, 2008;Nigam & Graupe, 2004;Srinivasan, et al, 2005Srinivasan, et al, , 2007Tzallas, et al, 2007aTzallas, et al, , 2007bTzallas, et al, , 2009Ubeyli, 2006Ubeyli, , 2009cUbeyli, 2010b) that have a complex shaped decision boundary. Other classification methods have been used using SVMs (Chandaka, et al, 2009;Iscan, et al, 2011;Liang, et al, 2010;Lima, et al, 2010;Subasi & Gursoy, 2010;Ubeyli, 2008a;Ubeyli, 2010a), k-nearest neighbour classifiers (Guo, et al, 2011;Iscan, et al, 2011;Liang, et al, 2010;Orhan, et al, 2011;Tzallas, et al, 2009), quadratic analysis (Iscan, et al, 2011), logistic regression Tzallas, et al, 2009), naive Bayes classifiers (Iscan, et al, 2011;Tzallas, et al, 2009), decision trees (Iscan, et al, 2011;Polat & Gunes, 2007;…”

Section: Automated Epileptic Seizure Analysismentioning

confidence: 99%