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

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

Abstrakti

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.

Alkuperäiskieli	englanti
Otsikko	2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
Kustantaja	Institute of Electrical and Electronics Engineers Inc.
Julkaisupäivä	2023
ISBN (elektroninen)	979-8-3503-2447-1
DOI - pysyväislinkit	https://doi.org/10.1109/EMBC40787.2023.10340663
Tila	Julkaistu - 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Sydney, Australia Kesto: 24 heinäk. 2023 → 27 heinäk. 2023

Julkaisusarja

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

Lisätietoja

Publisher Copyright:
© 2023 IEEE.

Tieteenalat

3112 Neurotieteet
3123 Naisten- ja lastentaudit

Pääsy asiakirjaan

10.1109/EMBC40787.2023.10340663

Siteeraa tätä

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. teoksessa 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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title = "Optimization of time series features to estimate brain age in children from electroencephalography",

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.",

keywords = "3112 Neurosciences, 3123 Gynaecology and paediatrics",

author = "Iyer, {Kartik K.} and Roberts, {James A.} and Michaela Waak and Ajay Kevat and Jasneek Chawla and Leena Lauronen and Sampsa Vanhatalo and Stevenson, {Nathan J.}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 ; Conference date: 24-07-2023 Through 27-07-2023",

year = "2023",

doi = "10.1109/EMBC40787.2023.10340663",

language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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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. julkaisussa 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, Australia, 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).

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

TY - GEN

T1 - Optimization of time series features to estimate brain age in children from electroencephalography

AU - Iyer, Kartik K.

AU - Roberts, James A.

AU - Waak, Michaela

AU - Kevat, Ajay

AU - Chawla, Jasneek

AU - Lauronen, Leena

AU - Vanhatalo, Sampsa

AU - Stevenson, Nathan J.

PY - 2023

Y1 - 2023

N2 - 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.

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.

KW - 3112 Neurosciences

KW - 3123 Gynaecology and paediatrics

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DO - 10.1109/EMBC40787.2023.10340663

M3 - Conference contribution

C2 - 38082782

AN - SCOPUS:85179639722

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

BT - 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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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. julkaisussa 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