Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection

Das, Anindya Bijoy; Bhuiyan, Mohammed Imamul Hassan; Alam, Samiul

doi:10.1007/s11760-014-0736-2

Cited by 103 publications

(34 citation statements)

References 32 publications

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“…In many study, extracted signals are derived at the time of seizure and at normal periods. So, classification performance of these studies were quite high (9,(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). In fact, visual assessment might be sufficient at that time.…”

Section: Introductionmentioning

confidence: 92%

“…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%

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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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Section: Introductionmentioning

confidence: 92%

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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“…Anindya et al [37] 2016 Dual tree complex wavelet transform using SVM proposed model has been tested on different datasets from the same database.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

Classification of patient by analyzing EEG signal using DWT and least square support vector machine

Zuhair¹,

Thomas²

2017

Adv. sci. technol. eng. syst. j.

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Epilepsy is a neurological disorder which is most widespread in human DWT decomposes the signal into approximation and detail coefficients, the ApEn values the coefficients were computed using pattern length (m= 2 and 3) as an input feature for the Least square support vector machine (LS-SVM). The classification is done using LS-SVM and the resultswere compared using RBF and linear kernels. The proposed model has used the EEG data consisting of 5 classes and compared with using the approximate and detailed coefficients combined and individually. The classification accuracy of the LS-SVM using the RBF and Linear kernel with ApEn using different cases is compared and it is found that the best accuracy percentage is 100% with RBF kernel.

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“…For example, the stationary wavelet transform (SWT) overcame the lack of the translation-invariance of DWT [166]. The dual-tree complex wavelet transform (DTCWT) extended the real-value to complex-value and used two decomposition trees [167]. Discrete wavelet packet transform (DWPT) decomposed detail subbands at each level to create a full decomposition tree [168].…”

Section: Shapementioning

confidence: 99%

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Wang¹,

Zhang²,

Zhan³

et al. 2016

PIER

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Abstract-(Aim) Pathological brain detection (PBD) systems aim to assist and even replace neuroradiologists to make decisions for patients. This review offers a comprehensive and quantitative comparison for PBD systems by artificial intelligence in magnetic resonance imaging (MRI) scanning. (Method) We first investigated four categories of brain diseases, including neoplastic disease, neurodegenerative disease, cerebrovascular disease, and inflammation. Next, we introduced important MRI techniques, such as the shimming, water and fat suppression, and three advanced imaging modalities (functional MRI, diffusion tensor imaging, and magnetic resonance spectroscopic imaging). Then, we discussed four image preprocessing techniques (image denoising, slice selection, brain extraction, spatial normalization, and intensity normalization), seven feature representation techniques (shape, moment, wavelet, statistics, entropy, gray level co-occurrence matrix, and Fourier transform), and two dimension reduction techniques (feature selection and feature extraction). Afterwards, we studied classification related methods: six learning models (decision tree, extreme learning machine, k-nearest neighbors, naive Bayes classifier, support vector machine, feed-forward neural network), five kernel functions (linear, homogeneous and inhomogeneous polynomial, radial basis function, and sigmoid), and three types of optimization methods (evolutionary algorithm, stochastic optimization, and swarm intelligence). (Results) We introduced three benchmark datasets and used K-fold stratified cross validation to avoid overfitting. We presented a detailed quantitative comparison among 44 state-of-theart PBD algorithms and discussed their advantages and limitations. (Discussions) Artificial intelligence is now making stride in the PBD field and enjoys a fair amount of success. In the future, semi-supervised learning and transfer learning techniques may be potential breakthroughs to develop PBD systems.

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Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection

Cited by 103 publications

References 32 publications

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

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

Classification of patient by analyzing EEG signal using DWT and least square support vector machine

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

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