BIROn - Birkbeck Institutional Research Online

    Deep learning topology-preserving EEG-based images for autism detection in infants

    Stamate, Cosmin and Magoulas, George and Thomas, Michael (2021) Deep learning topology-preserving EEG-based images for autism detection in infants. In: 22nd International Conference on Engineering Applications of Neural Networks, 25-27 June 2021, Crete, Greece (online).

    [img]
    Preview
    Text
    EANN_2021___Correct_Style_.pdf

    Download (823kB) | Preview
    Official URL: https://www.eann2021.eu/

    Abstract

    Developing digital biomarkers that would enable reliable detection of autism-ASD early in life is challenging because of the variability in the presentation of the autistic disorder and the need for simple measurements that could be implemented routinely during checkups. Electroencephalography, widely known as EEG, is an electrophysiological monitoring method that has been explored as a potential clinical tool for monitoring atypical brain function. EEG measurements were collected from 101 infants, beginning at 12 to 15 months of age and continuing until 36 months of age. In contrast to previous work in the literature that analysed EEG signals, our approach considers EEG-as-an-image using an appropriate signal transformation that preserves the spatial location of the EEG signals to create RGB images. It employs Residual neural networks and transfer learning to detect atypical brain function. Prediction of the clinical diagnostic outcome of ASD or not ASD at 36 months was accurate from as early as 12 months of age. This shows that using end-to-end deep learning is a viable way of extracting useful digital biomarkers from EEG measurements for predicting autism in infants.

    Metadata

    Item Type: Conference or Workshop Item (Paper)
    Keyword(s) / Subject(s): Autism spectrum disorder (ASD); Deep Learning; EEG; Residual Networks; Transfer Learning; Early Stopping
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: George Magoulas
    Date Deposited: 10 May 2022 13:45
    Last Modified: 11 May 2022 01:58
    URI: https://eprints.bbk.ac.uk/id/eprint/44088

    Statistics

    Activity Overview
    6 month trend
    34Downloads
    6 month trend
    43Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item