Classification of Dementia Associated Disorders Using EEG based Frequent Subgraph Technique

Dementia associated disorders such as vascular dementia, frontotemporal dementia and Alzheimer dementia lead to cognitive impairment. Discrimination of dementia associated disorders has reamined a challenging task as they have overlapping underlying complex structures and display similar clinical features. In this work, we explore an EEG based frequent subgraph searching technique to characterize stages of brain functional networks of mild cognitive impairment (MCI), Alzheimer's disease (AD) and vascular dementia (VD) subjects in comparison with healthy control (HC) subjects. To identify the frequent subgraph related to dementia, we first formulated the brain functional network based on the phase information of EEG with mutual information as a measure. The whole network is then divided into sub-regions and frequent sub-graph search is performed. The identified frequent subgraphs were employed to discriminate the dementia associated disorders from the data recorded from 10 healthy and 32 dementia subjects in various stages. Results show that the proposed method has the potential to quantify the disease progression using brain functional connectivity and the identified networks can aid in the diagnosis of dementia associated disorders.