TY - GEN
T1 - Classification of Dementia Associated Disorders Using EEG based Frequent Subgraph Technique
AU - Adebisi, Abdulyekeen T.
AU - Gonuguntla, Venkateswarlu
AU - Lee, Ho Won
AU - Veluvolu, Kalyana C.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - Brain Functional Network
KW - Dementia Associated Disease
KW - EEG
KW - Frequent Subgraph Search
KW - Functional Connectivity
KW - Mutual Information (MI)
KW - Reactive Band
UR - http://www.scopus.com/inward/record.url?scp=85101362421&partnerID=8YFLogxK
U2 - 10.1109/ICDMW51313.2020.00087
DO - 10.1109/ICDMW51313.2020.00087
M3 - Conference contribution
AN - SCOPUS:85101362421
T3 - IEEE International Conference on Data Mining Workshops, ICDMW
SP - 613
EP - 620
BT - Proceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
A2 - Di Fatta, Giuseppe
A2 - Sheng, Victor
A2 - Cuzzocrea, Alfredo
A2 - Zaniolo, Carlo
A2 - Wu, Xindong
PB - IEEE Computer Society
T2 - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
Y2 - 17 November 2020 through 20 November 2020
ER -