RETRACTED ARTICLE: A novel approach for automated detection of focal EEG signals using empirical wavelet transform

Bhattacharyya, Abhijit; Sharma, Manish; Pachori, Ram Bilas; Sircar, Pradip; Acharya, U. Rajendra

doi:10.1007/s00521-016-2646-4

Cited by 169 publications

(100 citation statements)

References 32 publications

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“…We have performed the Kruskal-Wallis statistical test [45] to find the statistical significance (p < 0.05) of the computed features in different oscillatory levels of the analyzed signals. The Kruskal-Wallis statistical test has been used for finding the statistical significance of the features computed from EEG signals [14,46]. In this paper, we have fixed the redundancy parameter (R) as 3 and considered sufficiently many levels (at maximum J = 16) for decomposition of EEG signals using TQWT.…”

Section: Resultsmentioning

confidence: 99%

“…To find the optimal subset of features, we have applied a wrapper based feature selection technique [48] available in the WEKA machine learning toolbox (Weka 3.6.13, University of Waikato, Hamilton, New Zealand) [49]. Finally, we have used two classifiers-namely, random forest classifier [50] (available in WEKA) and least squares support vector machine classifier (LS-SVM) [51] with Morlet wavelet [14,52] and radial basis function (RBF) kernels. The chosen values of kernel parameters ω and a for Morlet wavelet kernel are 0.5 and 6, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The chosen values of kernel parameters ω and a for Morlet wavelet kernel are 0.5 and 6, respectively. The RBF [14] kernel parameter σ = 1 has been selected in this work. The performance of the proposed feature extraction method with the mentioned classifiers has been evaluated with a 10-fold cross-validation method [53].…”

Section: Resultsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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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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“…We have selected the optimal set of MSEn features using the wrapper-based feature selection method [48] and classified using the SVM [49] classifier with the radial basis function (RBF) kernel [50].…”

Section: Classification Of Eeg Recordsmentioning

confidence: 99%

“…The ten-fold cross-validation method has been used widely to get unbiased performance of the classifier in the area of bio-medical signal processing [50,51]. All of the classification tasks along with wrapper-based feature selections have been performed using the WEKA machine learning toolbox (Weka 3.6.13, University of Waikato, Hamilton, New Zealand) [53].…”

Section: Classification Of Eeg Recordsmentioning

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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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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RETRACTED ARTICLE: A novel approach for automated detection of focal EEG signals using empirical wavelet transform

Cited by 169 publications

References 32 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

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

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

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