%L scholars17242
%J 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022
%O cited By 0; Conference of 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:186671
%R 10.1109/ICFTSC57269.2022.10039735
%D 2022
%X 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.
%K Analysis of variance (ANOVA); Computer aided diagnosis; Correlation methods; Learning algorithms; Neurodegenerative diseases; Support vector machines, Alzheimers disease; BOLD signal; Disease diagnosis; Dynamic functional connectivity; Functional connectivity; Functional connectivity patterns; Neurological disorders; Resting state; Resting state fMRI; SVM, Frequency domain analysis
%P 49-54
%A M.I. Al-Hiyali
%A N. Yahya
%A I. Faye
%A A. Sadiq
%A M.N.M. Saad
%I Institute of Electrical and Electronics Engineers Inc.
%T Detection of Alzheimer's Disease Using Dynamic Functional Connectivity Patterns in Resting-State fMRI