Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning
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 p...
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IEEE Computer Society
2018
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my.utp.eprints.217832018-11-09T01:12:04Z Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning Rasheed, W. Bhatti, M.S. Hisham Bin Hamid, N. Tang, T.B. Idris, Z. 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. IEEE Computer Society 2018 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046342691&doi=10.1109%2fPRIMEASIA.2017.8280355&partnerID=40&md5=037cff943cc5ef9f169de615b8f6e71b Rasheed, W. and Bhatti, M.S. and Hisham Bin Hamid, N. and Tang, T.B. and Idris, Z. (2018) Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning. Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics, 2017-O . pp. 25-28. http://eprints.utp.edu.my/21783/ |
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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. |
| format |
Article |
| author |
Rasheed, W. Bhatti, M.S. Hisham Bin Hamid, N. Tang, T.B. Idris, Z. |
| spellingShingle |
Rasheed, W. Bhatti, M.S. Hisham Bin Hamid, N. Tang, T.B. Idris, Z. Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning |
| author_facet |
Rasheed, W. Bhatti, M.S. Hisham Bin Hamid, N. Tang, T.B. Idris, Z. |
| author_sort |
Rasheed, W. |
| title |
Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning |
| title_short |
Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning |
| title_full |
Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning |
| title_fullStr |
Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning |
| title_full_unstemmed |
Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning |
| title_sort |
moderate traumatic brain injury identification for meg data using pu (positive and unseen) learning |
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IEEE Computer Society |
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2018 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046342691&doi=10.1109%2fPRIMEASIA.2017.8280355&partnerID=40&md5=037cff943cc5ef9f169de615b8f6e71b http://eprints.utp.edu.my/21783/ |
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13.211869 |