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dc.contributor.authorPhang, Chun-Ren
dc.contributor.authorTing, Chee-Ming
dc.contributor.authorSamdin, S. Balqis
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2019-07-09T07:32:15Z
dc.date.available2019-07-09T07:32:15Z
dc.date.issued2019-03
dc.identifier.citationPhang, C.-R., Ting, C.-M., Samdin, S. B., & Ombao, H. (2019). Classification of EEG-based Effective Brain Connectivity in Schizophrenia using Deep Neural Networks. 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). doi:10.1109/ner.2019.8717087
dc.identifier.doi10.1109/NER.2019.8717087
dc.identifier.urihttp://hdl.handle.net/10754/655958
dc.description.abstractDisrupted functional connectivity patterns have been increasingly used as features in pattern recognition algorithms to discriminate neuropsychiatric patients from healthy subjects. Deep neural networks (DNNs) were employed to fMRI functional network classification only very recently and its application to EEG-based connectome is largely unexplored. We propose a DNN with deep belief network (DBN) architecture for automated classification of schizophrenia (SZ) based on EEG effective connectivity. We used vector-autoregression-based directed connectivity (DC), graph-theoretical complex network (CN) measures and combination of both as input features. On a large resting-state EEG dataset, we found a significant decrease in synchronization of neural oscillations measured by partial directed coherence, and a reduced network integration in terms of weighted degrees and transitivity in SZ compared to healthy controls. The proposed DNN-DBN significantly outperforms three other traditional classifiers, due to its inherent capability as feature extractor to learn hierarchical representations from the aberrant brain network structure. Combined DC-CN features gives further improvement over the raw DC and CN features alone, achieving remarkable classification accuracy of 95% for the theta and beta bands.
dc.description.sponsorshipThis work was supported by the Universiti Teknologi Malaysia and the Ministry of Higher Education, Malaysia under Grants Q.J130000.2545.19H3, R.J130000.7845.4L840, R.J130000.7809.4L841 and R.J130000.7831.4L845.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8717087/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8717087
dc.rights(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.titleClassification of EEG-based Effective Brain Connectivity in Schizophrenia using Deep Neural Networks
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics
dc.contributor.departmentStatistics Program
dc.conference.date20-23 March 2019
dc.conference.name2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)
dc.conference.locationSan Francisco, CA, USA
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Biomedical Engineering & Health Sciences, Universiti Teknologi Malaysia, Skudai, Johor, 81310, Malaysia
kaust.personSamdin, S. Balqis
kaust.personOmbao, Hernando


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