Wavelet-based multi-class discrimination of EEG for seizure detection

The statistical properties of seizure EEG are found to be different from that of the normal EEG. This paper proposes the use of wavelet transform to select the frequency bands of interest and subsequently compute features based on energy and logarithmic weighting of the signal for the classification of EEG into normal and seizure classes. Five data sets of EEG (denoted A-E) were available for study. For this five-class problem, the EEG signal is decomposed up to the second level using Daubechies wavelet. Subsequently, features are calculated on each of these two bands per frame of EEG. The proposed method achieves a classification accuracy of 100% with low computational complexity.

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