@inproceedings{scholars17242,
           title = {Detection of Alzheimer's Disease Using Dynamic Functional Connectivity Patterns in Resting-State fMRI},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022},
           pages = {49--54},
            note = {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},
             doi = {10.1109/ICFTSC57269.2022.10039735},
            year = {2022},
          author = {Al-Hiyali, M. I. and Yahya, N. and Faye, I. and Sadiq, A. and Saad, M. N. M.},
            isbn = {9798350334548},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149139750&doi=10.1109\%2fICFTSC57269.2022.10039735&partnerID=40&md5=276163977033d76e6b9b4216a9608936},
        keywords = {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},
        abstract = {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. {\^A}{\copyright} 2022 IEEE.}
}