machine with RBF kernel is used for the classification task. An overall diagram of
the work is given in Figure 1.
The empirical mode decomposition produces six IMFs in total, though we have
considered only first four IMFs. The reason behind this selection procedure is the
non-availability of useful information in last IMFs. In this work we have extracted
various features such as approximate entropy (ApEn), sample entropy (SmEn),
Shannon entropy (ShEn), Rényi entropy (RnEn), Petrosian fractal dimension,
Higuchi fractal dimension, Katz fractal dimension, exponential energy from four
IMFs and statistical feature (mean, standard deviation and energy).
Figure 2.
Focal EEG signals and six IMFs obtained from focal EEG signal.
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Empirical Mode Decomposition of EEG Signals for the Effectual Classification of Seizures
DOI: http://dx.doi.org/10.5772/intechopen.89017