A wavelet based analysis of energy redistribution in scalp EEG during epileptic seizures

In this work, wavelet decomposition and multiresolution analysis are used to explore the changes in scalp EEG signals during epileptic seizures. EEG tracings, which include non-epileptic periods, the beginning of seizure and the peak of seizure, have been decomposed in five details using order 10 Daubechies orthonormal wavelets. Energy has then been computed, at each detail, from square wavelet coefficients, in order to unmask the presence of brief episodes of energy relocation among different scales. Results reveal the existence of significant changes in energy distribution at seizure onset; this redistribution, however, exhibits significant differences from one patient to another, and also among different channels in the same patient. Some channels exhibit a significant energy increase at low scales (high frequencies greater than 20 Hz) at seizure onset, whereas energy drops at higher scales. Other channels, however, exhibit energy increase at high scales (frequency less than 15 Hz) revealing a predominance of low-frequency activity. These two patterns may be simultaneously present at seizure onset and may change with different spatial evolution during the subsequent seizure progression. Wavelet analysis appears as a powerful tool for extracting features relative to seizure, and to study their propagation among different regions in the scalp.