Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition

• Published in: IEEE journal of biomedical and health informatics 2012-11, Vol.16 (6), p.1135-1142
• Main Authors: Bajaj, V., Pachori, R. B.
• Format: Article
• Language: English
• Subjects: EEG signal analysis; electroencephalogram (EEG) signal; Electroencephalography; Empirical mode decomposition; Epilepsy; Kernel; Signal analysis; Studies; Support vector machines
• ISSN: 1089-7771; 2168-2194; 1558-0032; 2168-2208
• DOI: 10.1109/TITB.2011.2181403

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 nonseizure EEG signals. The proposed method for classification of EEG signals based on the bandwidth features ( B AM and B FM ) and the LS-SVM has provided better classification accuracy than the method adopted by Liang and coworkers in their study published in 2010. 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.