Epilepsy EEG classification using morphological component analysis

Abstract

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 morphology during epileptic events using undecimated wavelet transform (UDWT), local discrete cosine transform (LDCT), and Dirac bases forming the over-complete dictionary. Frequency-modulated time frequency features were extracted after applying the Hilbert transform. Feature root mean instantaneous frequency square (RMIFS) and its parameters and parameters ratio are used in two different pairs for classification using support vector machine (SVM), showing good and comparable results.

Keywords

• Electroencephalogram (EEG)
• Morphological component analysis (MCA)
• Undecimated wavelet transform (UDWT)
• Local discrete cosine transform (LDCT)
• Dirac
• Root mean instantaneous frequency square (RMIFS)