The wavelet entropy (WE) of rest electroencephalogram (EEG) and of event-related potentials (ERP) carries information about the degree of order or disorder associated with a multi-frequency brain electrophysiological activity. In the present study, WE, relative WE and WE change were estimated for the EEG and ERP signals recorded during a working memory task, from dyslectic children and healthy subjects. The analysis of the two groups (controls vs dyslectics) revealed differentiations mainly in relative WE and WE change that takes into account the variability of rest EEG. These findings indicate that the WE can be employed as a quantitative measure for monitoring EEG and ERP activities and may provide a useful tool in analyzing electrophysiological signals associated with dyslexia.

Valid characterization of carotid atherosclerosis (CA) is a crucial public health issue, which would limit the major risk held by CA for both patient safety and state economies. CA is typically diagnosed and assessed using duplex ultrasonography (US). Elastrography Imaging (EI) is a promising US technique for quantifying tissue elasticity (ES). In this work, we investigated the association between ES of carotid atherosclerotic lesions, derived from EI, and texture indices, calculated from US image analysis. US and EI images of 23 atherosclerotic plaques (16 patients) were analyzed. Texture features derived from US image analysis (Gray-Scale Median (GSM), plaque area (A) and co-occurrence-matrixderived features) were calculated. Statistical analysis revealed associations between US texture features and EI measured indices. This result indicates accordance in US and EI techniques and states the promising role of EI in diagnosis of CA.