eprintid: 15369
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/53/69
datestamp: 2023-11-10 03:29:59
lastmod: 2023-11-10 03:29:59
status_changed: 2023-11-10 01:59:19
type: conference_item
metadata_visibility: show
creators_name: Sadiq, A.
creators_name: Yahya, N.
creators_name: Tang, T.B.
title: Classification of Alzheimer's Disease using Low Frequency Fluctuation of rs-fMRI Signals
ispublished: pub
keywords: Biomedical signal processing; Brain; Computer aided diagnosis; Functional neuroimaging; Machine learning; Magnetic resonance imaging, Alzheimers disease; Brain activity; Low Frequency Fluctuations; Lower frequencies; Minimum redundancy-maximum relevances; Normal controls; Oscillation; Power-spectra; ReliefF; Resting-state functional magnetic resonance imaging, Neurodegenerative diseases
note: cited By 0; Conference of 2021 International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2021 ; Conference Date: 1 December 2021 Through 2 December 2021; Conference Code:176965
abstract: 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.
date: 2021
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126600687&doi=10.1109%2fICICyTA53712.2021.9689209&partnerID=40&md5=8a50965909be805a977e529de46d0a27
id_number: 10.1109/ICICyTA53712.2021.9689209
full_text_status: none
publication: 2021 International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2021
pagerange: 40-45
refereed: TRUE
isbn: 9781665417778
citation:   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.