relation: https://khub.utp.edu.my/scholars/15369/
title: Classification of Alzheimer's Disease using Low Frequency Fluctuation of rs-fMRI Signals
creator: Sadiq, A.
creator: Yahya, N.
creator: Tang, T.B.
description: The resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging modality to measure brain activity and helps in the diagnosis of various brain-related disorders. Given the 1/f power spectrum characteristic of brain dynamics, where the energy value is higher at a low frequency than high frequency, it is established that low-frequency oscillations (LFO) provide a better representation of the spontaneous neuronal activity of the brain. In this research, a combination of the amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) from the resting-state blood oxygen level-dependent (BOLD) signal in the classic band i.e., 0.01-0.1 Hz is used for the classification of Alzheimer's disease (AD) from normal controls (NC). A total of 60 subjects participated in this study consisting of 30 AD patients and 30 NC from Alzheimer's disease neuroimaging initiative (ADNI). The feature selection is performed using minimum-redundancy maximum-relevance (mRMR) and ReliefF algorithm due to the large dimension of rs-fMRI data to be fed to the machine learning (ML) classifier. The proposed AD classification method employing the fusion of ALFF and fALFF obtained the highest classification accuracy of 96.36, indicating the good potential of the proposed method for the diagnosis of AD, as well as other neurological conditions. © 2021 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2021
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Sadiq, A. and Yahya, N. and Tang, T.B.  (2021) Classification of Alzheimer's Disease using Low Frequency Fluctuation of rs-fMRI Signals.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126600687&doi=10.1109%2fICICyTA53712.2021.9689209&partnerID=40&md5=8a50965909be805a977e529de46d0a27
relation: 10.1109/ICICyTA53712.2021.9689209
identifier: 10.1109/ICICyTA53712.2021.9689209