MCA Based Epilepsy EEG Classification Using Time Frequency Domain Features

In this work, we proposed a morphological component analysis (MCA) based method for epilepsy classification using the explicit dictionary of independent redundant transforms to decomposes the electroencephalogram (EEG) by considering it's morphology. Output components of MCA are represented int...

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Published in2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2018; pp. 3398 - 3401
Main Authors Mahapatra, Arindam Gajendra, Singh, Balbir, Horio, Keiichi, Wagatsuma, Hiroaki
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2018
Subjects
Bandwidth
Electroencephalography
Epilepsy
Feature extraction
Frequency modulation
Support vector machines
Transforms
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Summary:In this work, we proposed a morphological component analysis (MCA) based method for epilepsy classification using the explicit dictionary of independent redundant transforms to decomposes the electroencephalogram (EEG) by considering it's morphology. Output components of MCA are represented into analytical form by using Hilbert transform. Then features, parameter's ratio of bandwidth square, mean square frequency and fractional contributions to dominant frequency were extracted to discriminate epilepsy EEG by support vector machine (SVM). These features have shown classification results comparable to previous works.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2018.8513017