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

• 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
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-018-1995-4

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.