Automated seizure detection in scalp EEG using multiple wavelet scales

The proposed research work designs a detector algorithm for automatic detection of epileptic seizures. In this work a wavelet based feature extraction technique has been adopted. Epochs of EEG are decomposed using discrete wavelet transform (DWT) up to 5 level of wavelet decomposition. Relative values of energy and a normalized coefficient of variation (NCOV) based measure, (σ<sup>2</sup>/μ<sub>a</sub>) are computed on the wavelet coefficients acquired in the frequency range of 0-32 Hz from both seizure and non-seizure segments. The performance of NCOV over the traditionally used coefficient of variation, COV (σ<sup>2</sup>/μ<sup>2</sup>) was studied. The feature NCOV yielded better performance than the commonly used COV, σ<sup>2</sup>/μ<sup>2</sup>. The algorithm was evaluated on 5 subjects from CHB-MIT scalp EEG database.

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