eprintid: 8540
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/85/40
datestamp: 2023-11-09 16:20:27
lastmod: 2023-11-09 16:20:27
status_changed: 2023-11-09 16:12:54
type: conference_item
metadata_visibility: show
creators_name: Rasheed, W.
creators_name: Bhatti, M.S.
creators_name: Hisham Bin Hamid, N.
creators_name: Tang, T.B.
creators_name: Idris, Z.
title: Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning
ispublished: pub
keywords: Brain; Brain mapping; Learning systems; Magnetic resonance imaging; Patient monitoring; Population statistics, Cognitive impairment; Contact recording; Datamining; Functional connectivity; Functional magnetic resonance imaging; Mild traumatic brain injuries; Non-contact; Positive and unseen learning; Time frame; Traumatic Brain Injuries, Magnetoencephalography
note: cited By 0; Conference of 2017 IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics, PrimeAsia 2017 ; Conference Date: 31 October 2017 Through 2 November 2017; Conference Code:134541
abstract: Traumatic brain injury (TBI) is a source of disability and morbidity worldwide. Mild cognitive impairment (MCI) and mild TBI cause functional connectivity interruption for a very limited time frame; however, the patient diagnosed with moderate to severe forms of TBI requires quick, hassle free and precise identification of functional deficits in order to provide timely care. Magnetoencephalography (MEG) is the neuroimaging modality that provides the required information, and is useful for non-contact recording of functional connectivity assessment of TBI subjects. Default mode network (DMN) has been studied and described using functional magnetic resonance imaging (fMRI). This paper proposes a method to develop a default model of biomagnetic activations, as sensed over cortical region using MEG scans. The model is used to classify and assess TBI subjects. The classification is performed by devising default coherence limits between all pairs of MEG sensors for positive (control) group, and the assessment of severity is carried out by using PU learning method (single class model), where P (positive) data is from control population is utilized to compute significant functional connectivity deficits. © 2017 IEEE.
date: 2017
publisher: IEEE Computer Society
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046342691&doi=10.1109%2fPRIMEASIA.2017.8280355&partnerID=40&md5=037cff943cc5ef9f169de615b8f6e71b
id_number: 10.1109/PRIMEASIA.2017.8280355
full_text_status: none
publication: Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics
volume: 2017-O
pagerange: 25-28
refereed: TRUE
isbn: 9781538605240
issn: 21592144
citation:   Rasheed, W. and Bhatti, M.S. and Hisham Bin Hamid, N. and Tang, T.B. and Idris, Z.  (2017) Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning.  In: UNSPECIFIED.