Optimal Channel and Feature Selection for Automatic Prediction of Functional Brain Age of Preterm Infant Based on EEG

Overview

Journal Front Neurosci

Date 2025 Feb 12

PMID 39935839

Authors

Ling Li

Jiahui Li

Hui Wu

Yanping Zhao

Qinmei Liu

Hairong Zhang

Wei Xu

Affiliations

Soon will be listed here.

Abstract

Introduction: Approximately 15 million premature infants are born each year, many of whom face risks of neurological impairments. Accurate assessment of brain maturity is crucial for timely intervention and treatment planning. Electroencephalography (EEG) is a noninvasive method commonly used for this purpose. However, using all channels and features for brain maturity assessment can lead to high computational burden and overfitting, which can decrease the performance of the prediction system.

Methods: In this study, we propose an automatic prediction framework based on EEG to predict functional brain age (FBA) for assessing brain maturity in preterm infants. To optimize channel selection, we combine Binary Particle Swarm Optimization (BPSO) with Forward Addition (FA) and Backward Elimination (BE) methods. For feature selection, we combine the Pearson Correlation Coefficient (PCC), Recursive Feature Elimination (RFE), and Support Vector Regression (SVR) model.

Results: The proposed framework achieved a prediction accuracy of 76.71% within ±1 week and 94.52% within ±2 weeks. Effective channel and feature selection significantly improved model performance while reducing computational costs.

Discussion: These results demonstrate that optimizing channel and feature selection can enhance the performance of FBA prediction in preterm infants, offering a more efficient and accurate tool for brain maturity assessment.

References

Shen J, Zhang X, Huang X, Wu M, Gao J, Lu D . An Optimal Channel Selection for EEG-Based Depression Detection via Kernel-Target Alignment. IEEE J Biomed Health Inform. 2020; 25(7):2545-2556. DOI: 10.1109/JBHI.2020.3045718. View

Andre M, Lamblin M, dAllest A, Curzi-Dascalova L, Moussalli-Salefranque F, S Nguyen The T . Electroencephalography in premature and full-term infants. Developmental features and glossary. Neurophysiol Clin. 2010; 40(2):59-124. DOI: 10.1016/j.neucli.2010.02.002. View

Chang C, Hsu S, Pion-Tonachini L, Jung T . Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings. IEEE Trans Biomed Eng. 2019; 67(4):1114-1121. DOI: 10.1109/TBME.2019.2930186. View

Webb L, Kauppila M, Roberts J, Vanhatalo S, Stevenson N . Automated detection of artefacts in neonatal EEG with residual neural networks. Comput Methods Programs Biomed. 2021; 208:106194. DOI: 10.1016/j.cmpb.2021.106194. View

Dempsey E, Kooi E, Boylan G . It's All About the Brain-Neuromonitoring During Newborn Transition. Semin Pediatr Neurol. 2018; 28:48-59. DOI: 10.1016/j.spen.2018.05.006. View

Varsehi H, Firoozabadi S . An EEG channel selection method for motor imagery based brain-computer interface and neurofeedback using Granger causality. Neural Netw. 2020; 133:193-206. DOI: 10.1016/j.neunet.2020.11.002. View

Gschwandtner L, Hartmann M, Oberdorfer L, Furbass F, Klebermas-Schrehof K, Werther T . Deep learning for estimation of functional brain maturation from EEG of premature neonates. Annu Int Conf IEEE Eng Med Biol Soc. 2020; 2020:104-107. DOI: 10.1109/EMBC44109.2020.9175380. View

Rogers C, Lean R, Wheelock M, Smyser C . Aberrant structural and functional connectivity and neurodevelopmental impairment in preterm children. J Neurodev Disord. 2018; 10(1):38. PMC: 6291944. DOI: 10.1186/s11689-018-9253-x. View

Ream M, Lehwald L . Neurologic Consequences of Preterm Birth. Curr Neurol Neurosci Rep. 2018; 18(8):48. DOI: 10.1007/s11910-018-0862-2. View

10.

Gonzalez-Moreira E, Harmony T, Hinojosa-Rodriguez M, Carrillo-Prado C, Juarez-Colin M, Gutierrez-Hernandez C . Prevention of Neurological Sequelae in Preterm Infants. Brain Sci. 2023; 13(5). PMC: 10216630. DOI: 10.3390/brainsci13050753. View

11.

Salih A, Nichols T, Szabo L, Petersen S, Raisi-Estabragh Z . Conceptual Overview of Biological Age Estimation. Aging Dis. 2023; 14(3):583-588. PMC: 10187689. DOI: 10.14336/AD.2022.1107. View

12.

Qi F, Wu W, Yu Z, Gu Z, Wen Z, Yu T . Spatiotemporal-Filtering-Based Channel Selection for Single-Trial EEG Classification. IEEE Trans Cybern. 2020; 51(2):558-567. DOI: 10.1109/TCYB.2019.2963709. View

13.

Bhattacharyya A, Pachori R . A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform. IEEE Trans Biomed Eng. 2017; 64(9):2003-2015. DOI: 10.1109/TBME.2017.2650259. View

14.

OToole J, Boylan G, Vanhatalo S, Stevenson N . Estimating functional brain maturity in very and extremely preterm neonates using automated analysis of the electroencephalogram. Clin Neurophysiol. 2016; 127(8):2910-2918. DOI: 10.1016/j.clinph.2016.02.024. View

15.

OToole J, Boylan G, Lloyd R, Goulding R, Vanhatalo S, Stevenson N . Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach. Med Eng Phys. 2017; 45:42-50. PMC: 5461890. DOI: 10.1016/j.medengphy.2017.04.003. View

16.

Zhang Y, Wang K, Wei Y, Guo X, Wen J, Luo Y . Minimal EEG channel selection for depression detection with connectivity features during sleep. Comput Biol Med. 2022; 147:105690. DOI: 10.1016/j.compbiomed.2022.105690. View

17.

Li J, Barma S, Un Mak P, Chen F, Li C, Li M . Single-Channel Selection for EEG-Based Emotion Recognition Using Brain Rhythm Sequencing. IEEE J Biomed Health Inform. 2022; 26(6):2493-2503. DOI: 10.1109/JBHI.2022.3148109. View

18.

Stevenson N, Oberdorfer L, Tataranno M, Breakspear M, Colditz P, de Vries L . Automated cot-side tracking of functional brain age in preterm infants. Ann Clin Transl Neurol. 2020; 7(6):891-902. PMC: 7318094. DOI: 10.1002/acn3.51043. View

19.

Guarini A, Sansavini A, Fabbri C, Savini S, Alessandroni R, Faldella G . Long-term effects of preterm birth on language and literacy at eight years. J Child Lang. 2009; 37(4):865-85. DOI: 10.1017/S0305000909990109. View

20.

Mathieson S, Stevenson N, Low E, Marnane W, Rennie J, Temko A . Validation of an automated seizure detection algorithm for term neonates. Clin Neurophysiol. 2015; 127(1):156-168. PMC: 4727504. DOI: 10.1016/j.clinph.2015.04.075. View