Open AccessCombination of Coarse-Grained Procedure and Fractal Dimension for Epileptic EEG Classification
Author(s) -
Dien Rahmawati,
Achmad Rizal,
Desri Kristina Silalahi
Publication year - 2021
Publication title -
indonesian journal of computing and cybernetics systems
Language(s) - English
Resource type - Journals
eISSN - 2460-7258
pISSN - 1978-1520
DOI - 10.22146/ijccs.69845
Subject(s) - support vector machine , pattern recognition (psychology) , ictal , electroencephalography , fractal , fractal dimension , epilepsy , artificial intelligence , computer science , signal (programming language) , feature (linguistics) , abnormality , mathematics , medicine , neuroscience , psychology , mathematical analysis , linguistics , philosophy , programming language , psychiatry
Epilepsy, cured by some offered treatments such as medication, surgery, and dietary plan, is a neurological brain disorder due to disturbed nerve cell activity characterized by repeated seizures. Electroencephalographic (EEG) signal processing detects and classifies these seizures as one of the abnormality types in the brain within temporal and spectral content. The proposed method in this paper employed a combination of two feature extractions, namely coarse-grained and fractal dimension, a challenge to obtain a highly accurate procedure to evaluate and predict the epileptic EEG signal of normal, interictal, and seizure classes. The result of classification accuracy using variance fractal dimension (VFD) and quadratic support machine vector (SVM) with a number scale of 10 is 99% as the highest one, excellent performance of the predictive model in terms of the error rate. In addition, a higher scale number does not determine a higher accuracy in this study.