Seizure detection exploiting EMD-wavelet analysis of EEG signals

• Published in: 2015 IEEE International Symposium on Circuits and Systems (ISCAS) pp. 57 - 60
• Main Authors: Shahnaz, C., Rafi, R. H. Md, Fattah, S. A., Zhu, W.-P, Ahmad, M. O.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2015
• Subjects: Accuracy; Discrete Wavelet Transform; Discrete wavelet transforms; EEG Signal Analysis; Electroencephalography; empirical mode decomposition; Feature extraction; Higher order statistics; k-Nearest Neighbors; seizure; Sensitivity; Training
• ISSN: 0271-4302; 2158-1525
• DOI: 10.1109/ISCAS.2015.7168569

In his paper a method of seizure detection has been proposed based on the Discrete Wavelet Transform (DWT) analysis of the dominant Intrinsic mode function(IMF) resulting from the Empirical Mode Decomposition(EMD) of the EEG signals. Considering the normalized energy, Fourier spectrum and cross-correlation coefficient analysis, only the 4th Level DWT coefficients of the dominant IMF is found reasonable for feature computation. In order to reduce the dimension of the feature vector, Higher order statistics of these coefficients are employed to form he feature vector. The reduced feature vector thus formed is found effective for distinguishing seizure and non-seizure EEG signals when fed to a k-nearest neighborhood (k-NN) classifier. Extensive simulations are carried out using a benchmark EEG dataset. It is shown that the proposed method is capable of producing greater sensitivity, specificity and accuracy in comparison to that obtained by a sate-of-the-art method using the same EEG dataset and classifier.