Wavelet coherence-based clustering of EEG signals to estimate the brain connectivity in absence epileptic patients

In this paper, the need of novel methods to extract diagnostic information from the Electroencephalographic (EEG) recordings of epileptic patients was addressed. A novel method, based on Wavelet Coherence (WC) between EEG signals and Hierarchical Clustering (HC), was proposed to estimate the EEG network connectivity density in Childhood Absence Epilepsy (CAE) patients. The EEG recordings of four patients affected by CAE were partitioned into non overlapping windows and WC was estimated window by window. The behaviour of WC was analysed over the time, for every couple of EEG electrodes. The ictal states (seizures) resulted associated to increased WC levels, thus reflecting an increased synchronization between electrodes during the seizure. A WC-based dissimilarity index was then defined and HC was fed with the dissimilarity indices between every pair of electrodes with the aim of finding possible correlations between changes in electrode clustering and changes in the brain state. For every window under analysis, a dendrogram was constructed, the corresponding set of electrode clusters was determined and the subsequent network density values were calculated. Seizures resulted typically associated to increased network density, reflecting an increased connectivity during the ictal states.