This page shows all publications that appeared in the IASI annual research reports. Authors currently affiliated with the Institute are always listed with the full name.

You can browse through them using either the links of the following line or those associated with author names.

Show all publications of the year  2014, with author Bertolazzi P., in the category IASI Research Reports (or show them all):

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IASI Research Report n. 14-10  (Next)  

Giulia Fiscon, Weitschek E., Paola Bertolazzi, De Cola M C, De Salvo S, Bramanti P, Giovanni Felici

EEG signals analysis to detect Alzheimer's disease patients

ABSTRACT

Alzheimer's disease (AD) is the most widespread form of dementia. It is a neurodegenerative disorder, for which actually no cure is known. Furthermore, a particular disease called Mild Cognitive Impairment (MCI) affects patients that suffer of some isolated cognitive deficit due to which they could develop AD. Advances in bioinformatics and clinical informatics assist the medical doctors to manage the exponential growth of demented patient data. Several clinical data sets are available from electronic health records in medical environments. In particular, Electroencephalography (EEG) appears as non-invasive and repeatable technique to diagnose brain abnormalities. The open challenge is to perform clinical studies in order to shed light on biological and medical questions related to AD and MCI. Despite of technological advances, the analysis of EEG continues to be carried out by experts, who are subject to laborious interpretation of the spectrum. Computational methods may lead to a quantitative analysis of these signals and hence to characterize EEG time series. The aim of this work is to achieve an automatic patient's classification from the EEG biomedical signals involved in AD and MCI in order to support medical doctors in the right diagnosis formulation. The analysis of the biological EEG signals requires effective and efficient computer science methods to extract relevant information. Data mining, which guides the automated knowledge discovery process, is a natural way to approach EEG data analysis. Specifically, in our work we apply the following analysis steps: (i) pre-processing of EEG data; (ii) processing of the EEG signals by the application of time-frequency transforms; and (iii) classification by means of new and well-known machine learning methods. We obtain promising results from the classification of AD, MCI and control patient samples and we plan to extend the analysis and the pre-processing step by using different Time-frequency Transform and dedicated tools.