Detection of seizure onset using wavelet analysis

The spectrum of the normal electroencephalogram (EEG) follows an inverse power law attenuation over a band of clinically relevant frequencies. This suggests that EEG exhibits self-similar fluctuations over a multiplicity of scales, hence, can be characterized by measures which capture the scale-invariant nature of the signal. Here, the authors investigate the use of the discrete wavelet transform as a multiscale decomposition tool to monitor the statistical scale invariant properties of the EEG in long-term monitoring aimed to localize epileptic foci. The objective is to detect the onset of seizure marked by the loss of scale-invariance