Epileptic seizures classification in EEG signal based on semantic features and variational mode decomposition

Electroencephalogram (EEG) is the most important monitoring methodology for the detection of epileptic seizure diseases. In this paper, EEG based epileptic seizure detection is assessed by employing Bern-Barcelona EEG and Bonn University EEG database. The proposed technique contains three major step...

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Bibliographic Details
Published in:Cluster computing 2019-11, Vol.22 (Suppl 6), p.13521-13531
Main Authors: Ravi Kumar, M., Srinivasa Rao, Y.
Format: Article
Language:English
Subjects:
Accuracy
Brain research
Classification
Computer Communication Networks
Computer Science
Convulsions & seizures
Datasets
Decomposition
Electroencephalography
Entropy
Epilepsy
Feature extraction
Linear programming
Medical research
Methods
Neural networks
Neurological disorders
Operating Systems
Processor Architectures
Semantics
Signal classification
Signal processing
Support vector machines
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Summary:Electroencephalogram (EEG) is the most important monitoring methodology for the detection of epileptic seizure diseases. In this paper, EEG based epileptic seizure detection is assessed by employing Bern-Barcelona EEG and Bonn University EEG database. The proposed technique contains three major steps: decomposition, feature extraction and classification. Initially, decomposition using variational mode decomposition delivers an effective frequency localization. After decomposition, semantic feature extraction is carried-out by employing differential entropy and peak-magnitude of root mean square ratio for achieving optimal feature subsets and also for the rejection of irrelevant and redundant features. After finding the feature information, a superior classifier named as random forest is employed for classifying the normality and abnormality of seizure. The experimental result shows that the proposed approach distinguishes the normality and abnormality of seizure EEG signals in terms of sensitivity, specificity, accuracy, positive predictive value and negative predictive value with a superior recognition accuracy.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-018-1995-4