1D-local binary pattern based feature extraction for classification of epileptic EEG signals

Kaya, Yılmaz; Uyar, Murat; Tekin, Ramazan; Yildirim, Selcuk

doi:10.1016/j.amc.2014.05.128

Cited by 214 publications

(107 citation statements)

References 39 publications

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“…Local binary pattern (LBP)-based methods have been suggested in recent studies for the classification of epileptic seizure EEG signals. In [14], one-dimensional (1D) LBP-based features are extracted for the classification of epileptic seizure EEG signals and obtained classification accuracy of 95.67% when classifying seizure, seizure-free and the normal classes of EEG signals. In [15], the authors have developed a method based on the LBP of the Gabor filter-decomposed EEG signals followed by the nearest neighbor classifier and classified seizure-free and seizure EEG signals with a classification accuracy of 98.33%.…”

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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“…1B-YİD yöntemi metinlerden yeni öznitelik çıkarımı için görüntü işlemede yaygın bir şekilde kullanılan YİD metodundan geliştirilmiştir [37]. 1B-YİD yöntemi işleyiş olarak görüntü işlemede kullanılan YİD yöntemi ile benzerlik göstermektedir.…”

Section: Bir Boyutlu Yerel İkili Desenler Yöntemi (One Dimensional Lounclassified

“…1B-YİD yöntemi işleyiş olarak görüntü işlemede kullanılan YİD yöntemi ile benzerlik göstermektedir. Ancak, 1B-YİD yöntemi görüntü yerine zaman serisi şeklinde dizilmiş tek boyutlu sinyallere ve metinlere uygulanmıştır [37,38]. 1B-YİD yönteminde sinyaldeki her bir değer için ile komşuları arasında yapılan karşılaştırmalar sonucu ikili dizgeler üretilir.…”

Section: Bir Boyutlu Yerel İkili Desenler Yöntemi (One Dimensional Lounclassified

Doküman dili tanıma için yeni bir öznitelik çıkarım yaklaşımı: İkili Desenler

Kaya¹,

Ertuğrul²

2016

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…16 Kaya et al proposed a one-dimensional binary pattern based feature extraction technique for epileptic EEG signals classification. 17 However, all such BCI-related studies have been carried out on alert persons and do not report the performance degradation due to change in subject's mental alertness level.…”

Section: Introductionmentioning

confidence: 99%

Application of S-Transform for Automated Detection of Vigilance Level Using Eeg Signals

2016

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This paper presents an S-transform-based Electroencephalogram channel optimization and feature extraction methodology for monitoring mental vigilance level of humans. Vigilance level detection methodology consists of four steps. In the first stage, two types of Electroencephalogram signals (alert and drowsy) are acquired from 30 healthy subjects and decomposed into sub-bands using the S-transform. In the second stage, permutation entropy of the S-transform coefficients is calculated and Electroencephalogram channel optimization is performed. S-transform-based statistical features are computed from the optimized Electroencephalogram channels, in the third stage. In the fourth stage, artificial intelligence techniques such as Least Square-Support Vector Machine, Artificial Neural Network and Naive Bayes Classifier are used for the classification of Electroencephalogram signals using extracted features. The performance of the feature extraction methodology is tested on the Electroencephalogram data of 30 healthy subjects. Experimental results ensured the effectiveness of proposed methodology for the estimation of mental vigilance level by using Electroencephalogram signals. It is observed that the Artificial Neural Network classifier is a good candidate for pre-emptive automatic vigilance level detection system for Brain-Computer Interface applications.

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1D-local binary pattern based feature extraction for classification of epileptic EEG signals

Cited by 214 publications

References 39 publications

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

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

Doküman dili tanıma için yeni bir öznitelik çıkarım yaklaşımı: İkili Desenler

Application of S-Transform for Automated Detection of Vigilance Level Using Eeg Signals

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