Epilepsy EEG classification using morphological component analysis

Epilepsy EEG classification using morphological component analysis

Abstract In this paper, we have proposed an application of sparse-based morphological component analysis (MCA) to address the problem of classification of the epileptic seizure using time series electroencephalogram (EEG). MCA was employed to decompose the EEG signal segments considering its morphol...

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Bibliographic Details
Main Authors: Arindam Gajendra Mahapatra, Balbir Singh, Hiroaki Wagatsuma, Keiichi Horio
Format: Article
Language:English
Published: SpringerOpen 2018-08-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Electroencephalogram (EEG)
Morphological component analysis (MCA)
Undecimated wavelet transform (UDWT)
Local discrete cosine transform (LDCT)
Dirac
Root mean instantaneous frequency square (RMIFS)
Online Access:http://link.springer.com/article/10.1186/s13634-018-0568-2

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http://link.springer.com/article/10.1186/s13634-018-0568-2

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