2016
DOI: 10.1016/j.cmpb.2016.09.008
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Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating

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Cited by 212 publications
(58 citation statements)
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“…As a result, the use of TQWT-based multilevel filtering enhances the discriminating ability of the conventional K-NN entropy features. In the previous studies, the TQWT framework together with K-NN entropy [9] and spectral features [10] have been proposed for epileptic EEG classification. The proposed method in this paper presents a generalized form of K-NN entropy in the TQWT framework, which has also been studied for the classification of seizure and seizure-free EEG signals [9].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…As a result, the use of TQWT-based multilevel filtering enhances the discriminating ability of the conventional K-NN entropy features. In the previous studies, the TQWT framework together with K-NN entropy [9] and spectral features [10] have been proposed for epileptic EEG classification. The proposed method in this paper presents a generalized form of K-NN entropy in the TQWT framework, which has also been studied for the classification of seizure and seizure-free EEG signals [9].…”
Section: Discussionmentioning
confidence: 99%
“…They achieved classification accuracy of 97.75% with the least squares support vector machine (LS-SVM) classifier. In [10], the authors decomposed EEG signals using TQWT and extracted spectral features from the TQWT decomposed sub-band signals followed by the bagging algorithm to classify epileptic EEG signals. They achieved classification accuracy of 98.40% in classifying seizure, seizure-free and the normal classes of EEG signals.…”
Section: Introductionmentioning
confidence: 99%
“…Most techniques for the detection and prediction of epileptic seizures involve linear and non-linear processing of electroencephalographic (EEG) signals, which reflect the electrical activity in the brain (Acharya et al, 2012; Rana et al, 2012; Duque-Munoz et al, 2014; Hassan et al, 2016; Li et al, 2016). Some studies have achieved excellent results, with 100% accuracy for seizure detection (Alam and Bhuiyan, 2013).…”
Section: Introductionmentioning
confidence: 99%
“…Electroencephalography (EEG), which contains the information of most motor-sensory activities and cognitive processes, provides a signal source for an alternative approach. EEG recordings are particularly important in the diagnosis of epilepsy [3] and in brain computer interface (BCI) [4]. Several studies have shown that using EEG analysis can reveal pain responses from various stimulations such as heat or cold [5][6][7], electrical ones [8,9] and laser [10,11].…”
Section: Introductionmentioning
confidence: 99%