Automated Identification System for Focal EEG Signals Using Fractal Dimension of FAWT-Based Sub-bands Signals

Dalal, Mukesh; Tanveer, M.; Pachori, Ram Bilas

doi:10.1007/978-981-13-0923-6_50

Cited by 26 publications

(15 citation statements)

References 24 publications

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“…SVM [3,4,9] and KNN [4,7] are two well-known classifiers. SVM maps the input data to high dimensional space to construct an optimum hyper plane by using different kernels such as radial basis function (RBF) and quadratic kernel functions (QKF).…”

Section: Resultsmentioning

confidence: 99%

“…The EEG signals belong to five epilepsy patients who were candidates for the brain surgery. In this work, 50 pairs EEG signals from F and NF groups are chosen to evaluate the proposed method [1][2][3][4][5][6][7]9]. Fig.1.…”

Section: A Review Of 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%

“…Detection of F EEG signals using features derived from Fourier-based rhythms was proposed in [5], and various features of entropies derived from DWT coefficients were used in [6]. 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: Resultsmentioning

confidence: 99%

Section: A Review Of Dwt and Ewtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Akbari¹,

Ghofrani²

2019

IJIGSP

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“…In [28], the extracted entropy features were fed into the LS-SVM classifier to characterize the FC signal and achieved an accuracy of 82%. In [29], FAWT was developed to decompose the EEG signal into several sub-bands, and fractal dimension features were extracted. The obtained features were classified by a classifier.…”

Section: Introductionmentioning

confidence: 99%

Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

Prasanna

Subathra

Mohammed

et al. 2020

Sensors

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The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.

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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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Automated Identification System for Focal EEG Signals Using Fractal Dimension of FAWT-Based Sub-bands Signals

Cited by 26 publications

References 24 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

Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

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

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