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.
Project affiliated with The British Autism Study of Infant Siblings (BASIS) Research Network (www.basisnetwork.org).
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Acknowledgement
The investigators wish to sincerely thank all BASIS families for their enormous contribution and commitment to the project. BASIS is supported by the BASIS funding consortium led by Autistica (www.autistica.org.uk), Autism Speaks (grant no: 1292/MJ) and by the UK Medical Research Council (grant no: G0701484). Support of the NVIDIA Corporation that donated the Tesla K40 GPUs used for this research is also gratefully acknowledged.
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Stamate, C., Magoulas, G.D., Thomas, M.S.C., the BASIS Team. (2021). Deep Learning Topology–Preserving EEG–Based Images for Autism Detection in Infants. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_6
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