Author:
Wong Shi-Bing,Tsao Yu,Tsai Wen-Hsin,Wang Tzong-Shi,Wu Hsin-Chi,Wang Syu-Siang
Abstract
AbstractElectroencephalography (EEG) measures changes in neuronal activity and can reveal significant changes from infancy to adulthood concomitant with brain maturation, making it a potential physiological marker of brain maturation and cognition. To investigate a promising deep learning tool for EEG classification, we applied the bidirectional long short-term memory (BLSTM) algorithm to analyze EEG data from the pediatric EEG laboratory of Taipei Tzu Chi Hospital. The trained BLSTM model was 86% accurate when identifying EEGs from young children (8 months–6 years) and adolescents (12–20 years). However, there was only a modest classification accuracy (69.3%) when categorizing EEG samples into three age groups (8 months–6 years, 6–12 years, and 12–20 years). For EEG samples from patients with intellectual disability, the prediction accuracy of the trained BLSTM model was 46.4%, which was significantly lower than its accuracy for EEGs from neurotypical patients, indicating that the individual’s intelligence plays a major role in the age prediction. This study confirmed that scalp EEG can reflect brain maturation and the BLSTM algorithm is a feasible deep learning tool for the identification of cognitive age. The trained model can potentially be applied to clinical services as a supportive measurement of neurodevelopmental status.
Funder
Ministry of Science and Technology, Taiwan
Taipei Tzu Chi Hospital
Publisher
Springer Science and Business Media LLC
Reference46 articles.
1. Swaiman, K. F., Ashwal, S., Ferriero, D. M. & Schor, N. F. Swaiman’s Pediatric Neurology: Principles and Practice (Elsevier, 2018).
2. Parenti, I., Rabaneda, L. G., Schoen, H. & Novarino, G. Neurodevelopmental disorders: From genetics to functional pathways. Trends Neurosci. 43, 608–621 (2020).
3. Patel, D. R., Cabral, M. D., Ho, A. & Merrick, J. A clinical primer on intellectual disability. Transl. Pediatr. 9(Suppl. 1), S23–S35 (2020).
4. Anatürk, M. et al. Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging. Hum. Brain Mapp. 42, 1626–1640 (2021).
5. Shokri-Kojori, E., Bennett, I. J., Tomeldan, Z. A., Krawczyk, D. C. & Rypma, B. Estimates of brain age for gray matter and white matter in younger and older adults: Insights into human intelligence. Brain Res. 1763, 147431 (2021).
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献