Classification of epileptic EEG signals based on simple random sampling and sequential feature selection

Ghayab, Hadi Ratham Al; Li, Yan; Abdulla, Shahab; Diykh, Mohammed; Wan, Xiaolin

doi:10.1007/s40708-016-0039-1

Cited by 80 publications

(35 citation statements)

References 23 publications

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“…Table II compares the effect of WP bases selection on the classification accuracy (CA) for ABCD vs E classification based on a 2-fold cross validation. The results showed that the WP coefficients (5,1), (4,1), (4,2) performed better than the other WP basis candidates. Therefore; the WP coefficients (5,1), (4,1), (4,2) were fixed as the best WP bases for all other analyses in this work.…”

Section: Discussionmentioning

confidence: 97%

A new method for epileptic seizure classification in EEG using adapted wavelet packets

Ahmadi

Shalchyan

Daliri

2017

2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)

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Electroencephalography (EEG), as the most common tool for epileptic seizure classification, contains useful information about different physiological states of the brain. Seizure related features in EEG signals can be better identified when localized in time-frequency basis projections. In this work, a novel method for epileptic seizure classification based on wavelet packets (WPs) is presented in which both mother wavelet function and WP bases are adapted a posteriori to improve the seizure classification. A support vector machine (SVM) as classifier is used for seizure versus non-seizure EEG segment classification. In order to evaluate the proposed algorithm, a publicly available dataset containing different groups' patient with epilepsy and healthy individuals are used. The obtained results indicate that the proposed method outperforms some previously proposed algorithms in epileptic seizure classification.

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Section: Discussionmentioning

confidence: 97%

A new method for epileptic seizure classification in EEG using adapted wavelet packets

Ahmadi

Shalchyan

Daliri

2017

2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)

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“…Data Accuracy (%) Ghayab et al [32] Case I: Set A vs Set E 99.00 Siuly et al [12] 99.90 Zhu et al [7] 99.00 Nicolaou and Georgiou [33] 93.42 Our proposed technique 100.0 Siuly et al [12] Case II: Set B vs Set E 93.60 Zhu et al [7] 97.25 Our proposed technique 99.00 Siuly et al [12] Case III: Set C vs Set E 96.20 Zhu et al [7] 98.00 Our proposed technique 99. 25…”

Section: Methodsmentioning

confidence: 99%

A computer aided analysis scheme for detecting epileptic seizure from EEG data

Kabir¹,

Siuly²,

Cao³

et al. 2018

IJCIS

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This paper presents a computer aided analysis system for detecting epileptic seizure from electroencephalogram (EEG) signal data. As EEG recordings contain a vast amount of data, which is heterogeneous with respect to a time-period, we intend to introduce a clustering technique to discover different groups of data according to similarities or dissimilarities among the patterns. In the proposed methodology, we use K-means clustering for partitioning each category EEG data set (e.g. healthy; epileptic seizure) into several clusters and then extract some representative characteristics from each cluster. Subsequently, we integrate all the features from all the clusters in one feature set and then evaluate that feature set by three well-known machine learning methods: Support Vector Machine (SVM), Naive bayes and Logistic regression. The proposed method is tested by a publicly available benchmark database: 'Epileptic EEG database'. The experimental results show that the proposed scheme with SVM classifier yields overall accuracy of 100% for classifying healthy vs epileptic seizure signals and outperforms all the recent reported existing methods in the literature. The major finding of this research is that the proposed K-means clustering based approach has an ability to efficiently handle EEG data for the detection of epileptic seizure.

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“…The least square support vector machine (LS-SVM) was first developed by Suyken and Vandewalle [61,62] as a modified version of the original support vector machine [5,6]. It was used by Siuly et al [38] for the motor image classification, also by Al Ghayab et al [4] for detecting the epileptic EEG signals.…”

Section: Least Square Support Vector Machine (Ls-svm)mentioning

confidence: 99%