Does the proposed EMD and LS-SVM based method improve classification accuracy of seizure and non-seizure EEG signals compared to existing methods?
A novel signal processing approach using empirical mode decomposition and support vector machines improves the automated classification of seizure versus non-seizure EEG signals.
In this paper, we present a new method for classification of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) method. The intrinsic mode functions (IMFs) generated by EMD method can be considered as a set of amplitude and frequency modulated (AM-FM) signals. The Hilbert transformation of IMFs provides an analytic signal representation of the IMFs. The two bandwidths, namely amplitude modulation bandwidth (B(AM)) and frequency modulation bandwidth (B(FM)), computed from the analytic IMFs, have been used as an input to least squares support vector machine (LS-SVM) for classifying seizure and non-seizure EEG signals. The proposed method for classification of EEG signals based on the bandwidth features (B(A M) and B (FM)) and the LS-SVM has provided better classification accuracy than the method of Liang et. al 20. The experimental results with the recorded EEG signals from a published dataset are included to show the effectiveness of the proposed method for EEG signal classification.
Bajaj et al. (2011) studied this question.