Identification of children at risk of schizophrenia via deep learning and EEG responses
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Description
The prospective identification of children likely to develop schizophrenia is a vital tool to support early interventions that can mitigate the risk of progression to clinical psychosis. Electroencephalographic (EEG) patterns from brain activity and deep learning techniques are valuable resources in achieving this identification. We propose automated techniques that can process raw EEG waveforms to identify children who may have an increased risk of schizophrenia compared to typically developing children. We also analyse abnormal features that remain during developmental follow-up over a period of ~4 years in children with a vulnerability to schizophrenia initially assessed when aged 9 to 12 years. EEG data from participants were captured during the recording of a passive auditory oddball paradigm. We undertake a holistic study to identify brain abnormalities, first by exploring traditional machine learning algorithms using classification methods applied to hand-engineered features (event-related potential components). Then, we compare the performance of these methods with end-to-end deep learning techniques applied to raw data. We demonstrate via average cross-validation performance measures that recurrent deep convolutional neural networks can outperform traditional machine learning methods for sequence modeling. We illustrate the intuitive salient information of the model with the location of the most relevant attributes of a post-stimulus window. This baseline identification system in the area of mental illness supports the evidence of developmental and disease effects in a pre-prodromal phase of psychosis. These results reinforce the benefits of deep learning to support psychiatric classification and neuroscientific research more broadly.
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ID Code: | 200002 | ||||||||||||
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Item Type: | Contribution to Journal (Journal Article) | ||||||||||||
Refereed: | Yes | ||||||||||||
ORCID iD: |
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Measurements or Duration: | 8 pages | ||||||||||||
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DOI: | 10.1109/JBHI.2020.2984238 | ||||||||||||
ISSN: | 2168-2194 | ||||||||||||
Pure ID: | 58493645 | ||||||||||||
Divisions: | Current > Research Centres > Centre for Data Science Current > Research Centres > Centre for Biomedical Technologies Current > Research Centres > Centre for Inclusive Education Current > QUT Faculties and Divisions > Faculty of Science Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics Current > QUT Faculties and Divisions > Faculty of Creative Industries, Education & Social Justice Current > QUT Faculties and Divisions > Faculty of Health Current > Schools > School of Psychology & Counselling |
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Funding Information: | Manuscript received August 15, 2019; revised February 21, 2020 and March 22, 2020; accepted March 26, 2020. Date of publication April 17, 2020; date of current version January 5, 2021. This work was supported in part by the National Institute for Health Research (UK) Career Development Fellowship (CDF/08/01/015) and in part by BIAL Foundation Research under Grants (36/06 and 194/12). The work of K. Laurens was supported by an Australian Research Council Future Fellowship under Grant FT170100294. (Corresponding author: David Ahmedt Aristizabal.) David Ahmedt-Aristizabal is with the CSIRO, Canberra, ACT 2601, Australia, and also with the Image and Video Research Lab, SAIVT, Queensland University of Technology, Brisbane City, QLD 4000, Australia (e-mail: david.ahmedtaristizabal@csiro.au). | ||||||||||||
Copyright Owner: | IEEE 2020 | ||||||||||||
Copyright Statement: | Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. | ||||||||||||
Deposited On: | 15 May 2020 01:34 | ||||||||||||
Last Modified: | 27 Jul 2024 21:11 |
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