relation: https://khub.utp.edu.my/scholars/17242/
title: Detection of Alzheimer's Disease Using Dynamic Functional Connectivity Patterns in Resting-State fMRI
creator: Al-Hiyali, M.I.
creator: Yahya, N.
creator: Faye, I.
creator: Sadiq, A.
creator: Saad, M.N.M.
description: Alzheimer's disease (AD) is a slowly progressive neurological disorder associated with impaired functional connectivity of the brain. A common approach is to examine functional connectivity patterns (FC) for AD diagnosis either statically based on Pearson correlation coefficients (PCC) or dynamically based on time-frequency coefficients of resting-state fMRI BOLD signals. However, there is still a need to develop a AD diagnostic model with dynamic FC patterns that can improve the performance of the classifier. In this paper, a classification of AD from normal cases is proposed by combining a machine learning algorithm with dynamic FC patterns (DFC). The proposed method introduces a new feature vector for the maximum value of variation in the time-frequency domain, called (MWCF). Moreover, analysis of variance (ANOVA) is used to select the most informative features. Compared to previous studies, the proposed method outperforms state-of-The-Art methods with an accuracy of 98.4. The proposed method is an efficient predictor for the classification of AD vs. NC and can be used as a potential biomarker in AD diagnosis. © 2022 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Al-Hiyali, M.I. and Yahya, N. and Faye, I. and Sadiq, A. and Saad, M.N.M.  (2022) Detection of Alzheimer's Disease Using Dynamic Functional Connectivity Patterns in Resting-State fMRI.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149139750&doi=10.1109%2fICFTSC57269.2022.10039735&partnerID=40&md5=276163977033d76e6b9b4216a9608936
relation: 10.1109/ICFTSC57269.2022.10039735
identifier: 10.1109/ICFTSC57269.2022.10039735