TY - GEN
T1 - EEG based Functional Connectivity Analysis of Alzheimer's Disease Subjects
AU - Vijayakumaran, P. P.
AU - Narzary, D.
AU - Gonuguntla, V.
AU - Lee, Ho Won
AU - Veluvolu, Kalyana C.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - In the past few years, the study on functional connectivity of the human brain has led to various innovations in neuroscience. Functional connectivity reveals the simultaneity between the electrode pairs while performing neurophysiological activities. In this paper, the functional connectivity networks of healthy subjects, mild cognitive impairment (MCI), Alzheimer's disease (AD), and dementia patients were analysed by measuring the irregularity of the signals through wavelet spectral entropy (WSE) and were quantified using network measures. Depending on the discordance between the networks of all the four conditions, significant electrode pairs and their reactive networks were identified. These networks demonstrate the importance of most reactive electrode pairs that show significant variations. The identified functional connectivity networks that respond to the different pathologies shows the progression of the disease in patients. Further, the quantification of networks with graph theory network measures highlight differences between various progressive stages of the AD and its potential for development of EEG network biomarker in future.
AB - In the past few years, the study on functional connectivity of the human brain has led to various innovations in neuroscience. Functional connectivity reveals the simultaneity between the electrode pairs while performing neurophysiological activities. In this paper, the functional connectivity networks of healthy subjects, mild cognitive impairment (MCI), Alzheimer's disease (AD), and dementia patients were analysed by measuring the irregularity of the signals through wavelet spectral entropy (WSE) and were quantified using network measures. Depending on the discordance between the networks of all the four conditions, significant electrode pairs and their reactive networks were identified. These networks demonstrate the importance of most reactive electrode pairs that show significant variations. The identified functional connectivity networks that respond to the different pathologies shows the progression of the disease in patients. Further, the quantification of networks with graph theory network measures highlight differences between various progressive stages of the AD and its potential for development of EEG network biomarker in future.
KW - Alzheimer's disease (AD)
KW - dementia
KW - mild cognitive impairment
KW - neural disorder
KW - spectral entropy (SE)
KW - wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=85084066646&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC48513.2020.9065285
DO - 10.1109/ICAIIC48513.2020.9065285
M3 - Conference contribution
AN - SCOPUS:85084066646
T3 - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
SP - 356
EP - 361
BT - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Y2 - 19 February 2020 through 21 February 2020
ER -