Classification of focal and non-focal EEG signals in VMD-DWT domain using ensemble stacking

Rahman, Md. Mosheyur; Bhuiyan, Mohammed Imamul Hassan; Das, Anindya Bijoy

doi:10.1016/j.bspc.2019.01.012

Cited by 63 publications

(38 citation statements)

References 45 publications

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“…In this paper, the spectrum of input signal in Fourier domain is segmented to extract the five EEG rhythms. Hz, or the filter bank band pass are [0, 4], [4,8], [8,13], [13,30], and [30, 60] corresponding to δ, θ, α, β and γ rhythms. Experimentally, the value of  in EWT (see Section 2) are set 0.2381 to avoid the overlapping of sub-bands.…”

Section: A Rhythm Separation By Dwt and Ewtmentioning

confidence: 99%

“…Therefore, distinguishing the F and NF EEG signals may help detecting the abnormal part of an epileptic patient's brain. So, in recent years, researchers have proposed methods [1][2][3][4][5][6][7][8]11] in order to classify the F and NF EEG signals.…”

Section: Introductionmentioning

confidence: 99%

“…In [7], fractal dimension of flexible analytic wavelet transform coefficients have been extracted as features and fed to robust energy based least square twin SVM (RELS-TSVM). Recently, autoregressive model and entropy based features have been computed in variation mode decomposition (VMD)-DWT domain for classification of F and NF EEG signals [8].…”

Section: Introductionmentioning

confidence: 99%

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Fast and Accurate Classification F and NF EEG by Using SODP and EWT

Akbari¹,

Ghofrani²

2019

IJIGSP

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Removing the brain part, as the epilepsy source attack, is a surgery solution for those patients who have drug resistant epilepsy. So, the epilepsy localization area is an essential step before brain surgery. The Electroencephalogram (EEG) signals of these areas are different and called as focal (F) whereas the EEG signals of other normal areas are known as non-focal (NF). Visual inspection of multi-channels for F EEG detection is time-consuming along with human error. In this paper, an automatic and adaptive method is proposed based on second order difference plot (SODP) of EEG rhythms in empirical wavelet transform (EWT) domain as an adaptive signal decomposition. SODP provides the data variability rate or gives a 2D projection for rhythms. The feature vector is obtained using the central tendency measure (CTM). Finally, significant features, chosen by Kruskal-Wallis statistical test, are fed to K nearest neighbor (KNN) and support vector machine (SVM) classifiers. The achieved results of the proposed method in terms of three objective criteria are compared with state-of-the-art papers demonstrating an outstanding algorithm here in.

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Section: A Rhythm Separation By Dwt and Ewtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast and Accurate Classification F and NF EEG by Using SODP and EWT

Akbari¹,

Ghofrani²

2019

IJIGSP

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“…Deep learning, convolution neural network (CNN) is giving an excellent performance in classifying the EEG data set. Although some traditional machine learning technique has given comparable accuracy, we have to merge other preprocessing techniques with it whereas CNN does not require preprocessing of reducing data (Avcu et al, 2019;Fukumori et al, 2019;Rahman et al, 2019;Resque et al, 2019;Wójcik et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Event-related EEG signal of 70 patients are implemented with five classifiers boosted decision tree, classical neural network, Bayes point machine, and logistic regression and average perception supervised machine learning technique, whereas logistic regression and boost decision tree has given good accuracy of classification. In another report (Rahman et al, 2019), 3750 focal EEG segments and 3750 non-focal EEG segments of five subjects are studied using the ensemble stack classifier and found better classification accuracy. An ensemble deep learning-based CNN classifies the seizure type, which is comparatively robotic than traditional techniques.…”

Section: Introductionmentioning

confidence: 99%

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques

Sahu,

Dash,

Cacha

et al. 2020

J. Integr. Neurosci.

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Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accurate diagnosis and appropriate drug therapy to patients. This work aims to categorize electroencephalography signals on different channels' recordings for classifying and predicting epileptic seizures. The collection of the electroencephalography recordings contained in the dataset attributes 179 information and 11,500 instances. Instances are of five categories, where one is the symptoms of epilepsy seizure. We have used traditional, ensemble methods and deep machine learning techniques highlighting their performance for the epilepsy seizure detection task. One dimensional convolutional neural network, ensemble machine learning techniques like bagging, boosting (AdaBoost, gradient boosting, and XG boosting), and stacking is implemented. Traditional machine learning techniques such as decision tree, random forest, extra tree, ridge classifier, logistic regression, K-Nearest Neighbor, Naive Bayes (gaussian), and Kernel Support Vector Machine (polynomial, gaussian) are used for classifying and predicting epilepsy seizure. Before using ensemble and traditional techniques, we have preprocessed the data set using the Karl Pearson coefficient of correlation to eliminate irrelevant attributes. Further accuracy of classification and prediction of the classifiers are manipulated using k-fold crossvalidation methods and represent the Receiver Operating Characteristic Area Under the Curve for each classifier. After sorting and comparing algorithms, we have found the convolutional neural network and extra tree bagging classifiers to have better performance than all other ensemble and traditional classifiers.

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Epilepsy disorder detection and diagnosis using empirical mode decomposition and deep learning architecture

Srinath

Rajagopal

2022

Concurrency and Computation

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Epilepsy neurological disorder is detected and diagnosed in this article using deep learning method by differentiating the focal electroencephalogram (EEG) signals and the non-focal EEG signals. The proposed method consists of time-scale signal decomposition, feature extraction, and classification with diagnosis. The empirical mode decomposition (EMD) method is used to decompose the EEG signal into six intrinsic mode function (IMF) sub bands. The external intrinsic features are computed for each of the decomposed IMF sub bands and these features along with the coefficients of each IMF sub bands are then trained and further classified using the proposed convolutional neural networks (CNN) architecture. The CNN classifier classifies the EEG signal into either focal or non-focal and finally the focal signal is diagnosed as mild case or severe case using the proposed CNN architecture. The proposed framework achieves a Se of 99.8%, Sp of 99.9%, and Acc of 99.8% on the EEG signals which are available in Bern-Barcelona dataset. The proposed framework for diagnosis of severe case signals achieves 99.2% of diagnosis rate (DR) and 99.6% of DR for mild case signals on Bern-Barcelona dataset. The proposed framework achieves a Se of 99.6%, Sp of 99.6%, and Acc of 99.7% on the EEG signals which are available in CHB-MIT dataset. The proposed framework for diagnosis of severe case signals achieves a DR of 97.3% and DR of 98.0% for mild case signals on CHB-MIT dataset. The performance of the proposed method is cross validated by k-fold method.

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Classification of focal and non-focal EEG signals in VMD-DWT domain using ensemble stacking

Cited by 63 publications

References 45 publications

Fast and Accurate Classification F and NF EEG by Using SODP and EWT

Fast and Accurate Classification F and NF EEG by Using SODP and EWT

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques

Epilepsy disorder detection and diagnosis using empirical mode decomposition and deep learning architecture

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