Optimization of time series features to estimate brain age in children from electroencephalography

Kartik K. Iyer; James A. Roberts; Michaela Waak; Ajay Kevat; Jasneek Chawla; Leena Lauronen; Sampsa Vanhatalo; Nathan J. Stevenson

doi:10.1109/EMBC40787.2023.10340663

Optimization of time series features to estimate brain age in children from electroencephalography

Kartik K. Iyer, James A. Roberts, Michaela Waak, Ajay Kevat, Jasneek Chawla, Leena Lauronen, Sampsa Vanhatalo, Nathan J. Stevenson

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

Sammanfattning

Functional brain age measures in children, derived from the electroencephalogram (EEG), offer direct and objective measures in assessing neurodevelopmental status. Here we explored the effectiveness of 32 preselected 'handcrafted' EEG features in predicting brain age in children. These features were benchmarked against a large library of highly comparative multivariate time series features (>7000 features). Results showed that age predictors based on handcrafted EEG features consistently outperformed a generic set of time series features. These findings suggest that optimization of brain age estimation in children benefits from careful preselection of EEG features that are related to age and neurodevelopmental trajectory. This approach shows potential for clinical translation in the future.Clinical Relevance-Handcrafted EEG features provide an accurate functional neurodevelopmental biomarker that tracks brain function maturity in children.

Originalspråk	engelska
Titel på värdpublikation	2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
Förlag	Institute of Electrical and Electronics Engineers Inc.
Utgivningsdatum	2023
ISBN (elektroniskt)	979-8-3503-2447-1
DOI	https://doi.org/10.1109/EMBC40787.2023.10340663
Status	Publicerad - 2023
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Sydney, Australien Varaktighet: 24 juli 2023 → 27 juli 2023

Publikationsserier

Namn	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (tryckt)	1557-170X

Bibliografisk information

Publisher Copyright:
© 2023 IEEE.

Vetenskapsgrenar

3112 Neurovetenskaper
3123 Kvinno- och barnsjukdomar

Åtkomst av dokument

10.1109/EMBC40787.2023.10340663

Citera det här

Iyer, K. K., Roberts, J. A., Waak, M., Kevat, A., Chawla, J., Lauronen, L., Vanhatalo, S., & Stevenson, N. J. (2023). Optimization of time series features to estimate brain age in children from electroencephalography. I 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC40787.2023.10340663

Iyer, Kartik K. ; Roberts, James A. ; Waak, Michaela et al. / Optimization of time series features to estimate brain age in children from electroencephalography. 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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abstract = "Functional brain age measures in children, derived from the electroencephalogram (EEG), offer direct and objective measures in assessing neurodevelopmental status. Here we explored the effectiveness of 32 preselected 'handcrafted' EEG features in predicting brain age in children. These features were benchmarked against a large library of highly comparative multivariate time series features (>7000 features). Results showed that age predictors based on handcrafted EEG features consistently outperformed a generic set of time series features. These findings suggest that optimization of brain age estimation in children benefits from careful preselection of EEG features that are related to age and neurodevelopmental trajectory. This approach shows potential for clinical translation in the future.Clinical Relevance-Handcrafted EEG features provide an accurate functional neurodevelopmental biomarker that tracks brain function maturity in children.",

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Iyer, KK, Roberts, JA, Waak, M, Kevat, A, Chawla, J, Lauronen, L , Vanhatalo, S & Stevenson, NJ 2023, Optimization of time series features to estimate brain age in children from electroencephalography. i 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023, Sydney, Australien, 24/07/2023. https://doi.org/10.1109/EMBC40787.2023.10340663

Optimization of time series features to estimate brain age in children from electroencephalography. / Iyer, Kartik K.; Roberts, James A.; Waak, Michaela et al.
2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

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AU - Vanhatalo, Sampsa

AU - Stevenson, Nathan J.

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AB - Functional brain age measures in children, derived from the electroencephalogram (EEG), offer direct and objective measures in assessing neurodevelopmental status. Here we explored the effectiveness of 32 preselected 'handcrafted' EEG features in predicting brain age in children. These features were benchmarked against a large library of highly comparative multivariate time series features (>7000 features). Results showed that age predictors based on handcrafted EEG features consistently outperformed a generic set of time series features. These findings suggest that optimization of brain age estimation in children benefits from careful preselection of EEG features that are related to age and neurodevelopmental trajectory. This approach shows potential for clinical translation in the future.Clinical Relevance-Handcrafted EEG features provide an accurate functional neurodevelopmental biomarker that tracks brain function maturity in children.

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Iyer KK, Roberts JA, Waak M, Kevat A, Chawla J, Lauronen L et al. Optimization of time series features to estimate brain age in children from electroencephalography. I 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC40787.2023.10340663