Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification

Murugavel, A. S. Muthanantha; Ramakrishnan, S.

doi:10.1007/s11517-015-1351-2

Cited by 86 publications

(37 citation statements)

References 48 publications

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“…In contrast to linear SVM, RBF-SVM can provide a nonlinear boundary using the kernel trick to transform a nonlinear space into a higher dimensional space [45]. The RBF is commonly seen in ( , ) = exp(−‖ − ‖ 2 /2 2 ), where 2 is the variance of the Gaussian kernel [5,46]. Principal component analysis (PCA) was used to further reduce the resulting feature space [47].…”

Section: Support Vector Machinementioning

confidence: 99%

“…Indeed, approximately one-third of TLE patients are unable to control their seizures, even with the best available medications and surgery. Correct clinical diagnosis for TLE is critical for improving surgical outcomes and requires highly trained professionals [5]. Manual diagnosis of unilateral TLE using brain-neuroimaging methods is time-consuming, and different experts may give contradictory diagnoses for the same data [6].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

Chen

et al. 2018

Complexity

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Correct lateralization of temporal lobe epilepsy (TLE) is critical for improving surgical outcomes. As a relatively new noninvasive clinical recording system, magnetoencephalography (MEG) has rarely been applied for determining lateralization of unilateral TLE. Here we propose a framework for using resting-state brain-network features and support vector machine (SVM) for TLE lateralization based on MEG. We recruited 15 patients with left TLE, 15 patients with right TLE, and 15 age-and sex-matched healthy controls. The lateralization problem was then transferred into a series of binary classification problems, including left TLE versus healthy control, right TLE versus healthy control, and left TLE versus right TLE. Brain-network features were extracted for each participant using three network metrics (nodal degree, betweenness centrality, and nodal efficiency). A radial basis function kernel SVM (RBF-SVM) was employed as the classifier. The leave-one-subject-out cross-validation strategy was used to test the ability of this approach to overcome individual differences. The results revealed that the nodal degree performed best for left TLE versus healthy control and right TLE versus healthy control, with accuracy of 80.76% and 75.00%, respectively. Betweenness centrality performed best for left TLE versus right TLE with an accuracy of 88.10%. The proposed approach demonstrated that MEG is a good candidate for solving the lateralization problem in unilateral TLE using various brain-network features.

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Section: Support Vector Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

Chen

et al. 2018

Complexity

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“…Wang et al [12] combined crossvalidation (CV) with k-nearest neighbor (k-NN) to construct a hierarchical knowledge base to detect epilepsy. Murugavel et al [13] also proposed a novel hierarchical multi-class SVM, with extreme learning machine as kernel, to classify EEG signals. For improvement in the accuracy of epilepsy detection researchers have also studied the application of machine learning and optimization algorithms.…”

Section: Feature Classification Methodsmentioning

confidence: 99%

A comparative study on feature extraction and classification of mind waves for brain computerinterface (BCI)

Manjula¹,

Anandaraju²

2018

IJET

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Brain Computer Interfacing (BCI) is a methodology which imparts a path for communication from external world using brain signals through computer. BCI identifies the specific patterns in a person's changing brain activity to initiate control which relates to the person's intention. The BCI system paraphrases these signal patterns into meaningful control command. In evolving BCI system, numerous signal processing algorithms are proposed. Non-invasive Electroencephalogram (EEG) signals or mind waves are used to extract the distinguished features and further they are classified choosing an appropriate classifier. A study on different feature extraction & Classification algorithms is used in EEG-based BCI exploration and to identify their distinct properties. This paper proposes different methodologies of feature extraction and feature Classification. It also addresses the methods and technology adapted in every phase of the EEG signal processing. This comparative survey also helps in selecting suitable algorithm for the development and accomplishment of further classification of signals.

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“…Wang et al [20] combined cross-validation (CV) with k-nearest neighbor (k-NN) to construct a hierarchical knowledge base to detect epilepsy. Murugavel et al [21] also proposed a novel hierarchical multi-class SVM, with extreme learning machine as kernel, to classify EEG signals. To classify multi-subject EEG signals, Choi [22] used multi-task learning, which treated subjects as tasks to capture inter-subject relatedness in the Bayesian treatment of probabilistic common spatial patterns.…”

Section: Introductionmentioning

confidence: 99%

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

Wen

Zhang

2017

Medicine

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In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.

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Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification

Cited by 86 publications

References 48 publications

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

A comparative study on feature extraction and classification of mind waves for brain computerinterface (BCI)

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

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