Automated EEG analysis of epilepsy: A review

Acharya, U. Rajendra; Sree, S. Vinitha; Swapna, G.; Martis, Roshan Joy; Suri, Jasjit S.

doi:10.1016/j.knosys.2013.02.014

Cited by 658 publications

(343 citation statements)

References 116 publications

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“…Electroencephalogram (EEG) signals are generally used for the medical investigations of the epilepsy disorder [2]. Epilepsy is broadly classified into two categories-namely, focal and entropy in the EMD-DWT domain.…”

Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

Bhattacharyya

Pachori

Acharya

2017

Entropy

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This paper analyses the complexity of multivariate electroencephalogram (EEG) signals in different frequency scales for the analysis and classification of focal and non-focal EEG signals. The proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT). In the field of multivariate entropy analysis, recent studies have performed analysis of biomedical signals with a multi-level filtering approach. This approach has become a useful tool for measuring inherent complexity of the biomedical signals. However, these methods may not be well suited for quantifying the complexity of the individual multivariate sub-bands of the analysed signal. In this present study, we have tried to resolve this difficulty by employing TQWT for analysing the sub-band signals of the analysed multivariate signal. It should be noted that higher value of Q factor is suitable for analysing signals with oscillatory nature, whereas the lower value of Q factor is suitable for analysing signals with non-oscillatory transients in nature. Moreover, with an increased number of sub-bands and a higher value of Q-factor, a reasonably good resolution can be achieved simultaneously in high and low frequency regions of the considered signals. Finally, we have employed multivariate fuzzy entropy (mvFE) to the multivariate sub-band signals obtained from the analysed signal. The proposed Q-based multivariate sub-band entropy has been studied on the publicly available bivariate Bern Barcelona focal and non-focal EEG signals database to investigate the statistical significance of the proposed features in different time segmented signals. Finally, the features are fed to random forest and least squares support vector machine (LS-SVM) classifiers to select the best classifier. Our method has achieved the highest classification accuracy of 84.67% in classifying focal and non-focal EEG signals with LS-SVM classifier. The proposed multivariate sub-band fuzzy entropy can also be applied to measure complexity of other multivariate biomedical signals.

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Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

Bhattacharyya

Pachori

Acharya

2017

Entropy

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“…The seizure detection process can be made on a single-or multi-channel basis [3,4]. Single-channel seizure detection requires selecting the channel containing the strongest EEG signal collected from the closest point to the seizure spot.…”

Section: Introductionmentioning

confidence: 99%

EEG seizure detection and prediction algorithms: a survey

Alshebeili

Alshawi

Ahmad

et al. 2014

EURASIP J. Adv. Signal Process.

227

115

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Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.

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“…To characterize the EEG signals, statistics over the set of the wavelet coefficients were calculated (5,6,13,27,28). The following statistics were calculated from the wavelet coefficients:…”

Section: Methodsmentioning

confidence: 99%

“…There is also inter-reader differences during the visual analysis and it suggest that the visual analysis could be insufficient. With that reason, new computer evaluation techniques are developed and performed in healthy and diseased individuals (5)(6)(7)(8)(9)(10)(11)(12)(13). Most of these studies consist of two steps: Feature extraction from the EEG signals and then classification of these features.…”

Section: Introductionmentioning

confidence: 99%

The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

Öztürk¹,

Türe²,

Kıylıoğlu³

et al. 2018

meandros

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Objective: Electroencephalogram (EEG) signals have been broadly utilized for the diagnosis of epilepsy. Expert physicians must monitor long-term EEG signals that is sometimes difficult and time consuming process for epilepsy diagnosis. In this study, classification performances of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA), which are widely used in computer supported epilepsy diagnosis, were compared by using wavelet-based features of extracted from EEG signals which were derived in either normal or inter-ictal periods. In addition, Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were used to determine the effects of dimension reduction on classification success. Materials and Methods:The EEG data were sampled from the EEG laboratory of the Department of Neurology and Clinical Neurophysiology in Adnan Menderes University. Study was approved by local ethics committee with protocol number 2016/873. Ten patients with epilepsy (male -female, age range) and 10 normal (male -female, age range) were the study group. EEG signals of patients with epilepsy were contains only seizure free-epochs. EEG signals were first decomposed into frequency sub-bands by using Discrete Wavelet Transform (DWT) and then some statistical features were calculated from those to classify it's as normal or epileptic. Results: In classification of the EEG signals, it's as normal or epileptic, we achieved %88.9 accuracy rate using SVM with Radial Basis Function (RBF) kernel without dimension reduction. Conclusion: Results showed that, SVM was a powerful tool in classifying EEG signals if it's normal or epileptic.Amaç: Bu çalışmada, epileptik ve epileptik olmayan EEG (Elektroensefelografi) sinyallerinden elde edilen özniteliklerin boyutlarının Temel Bileşenler Analizi (TBA) ve Bağımsız Bileşenler Analizi (BBA) yöntemleri ile indirgenmesinin sınıflandırma başarısı üzerine etkilerinin belirlenmesi ve Doğrusal Ayırma Analizi (DAA) ile Destek Vektör Makinesi (DVM) yöntemlerinin sınıflandırma performanslarının karşılaştırılması amaçlandı. Gereç ve Yöntem: Çalışmaya 10 kontrol ve uzman hekim tarafından epilepsi tanısı konmuş 10 hasta olmak üzere toplam 20 kişi dahil edildi. Epilepsi tanısı konmuş hastalardan alınan EEG kayıtları nöbet geçirmedikleri sırada alınan kayıtlardı. Epileptik

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Automated EEG analysis of epilepsy: A review

Cited by 658 publications

References 116 publications

Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

EEG seizure detection and prediction algorithms: a survey

The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

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