Testing Graph Robustness Indexes for EEG Analysis in Alzheimer’s Disease Diagnosis

Alzheimer’s Disease (AD) is an incurable neurodegenerative disorder which mainly affects older adults. An early diagnosis essential because medical treatments can slow down the progression of disease only if provided during first stage, called Mild Cognitive Impairment (MCI). Starting from study electroencephalografic signals, brain functional connectivity analyses be performed with support graph theory. In particular, purpose this work to verify performances three indexes, typically adopted evaluate robustness, in order estimate for groups subjects: healthy controls and people affected by dementia at two different stages (MCI AD). The results obtained Connection Density Index, Randić a normalized version Kirchhoff Index revealed higher robustness networks people, followed MCI and, finally, AD patients, consistent hallmarks disease. statistical analysis showed that there significant difference between all indexes. Finally, indexes were compared, revealing outperformed other These preliminary outcomes will exploited address further in-depth time-expensive improving