Development and validation of Auto-Neo-electroencephalography (EEG) to estimate brain age and predict report conclusion for electroencephalography monitoring data in neonatal intensive care units

Xinran Dong; Yanting Kong; Yan Xu; Yuanfeng Zhou; Xinhua Wang; Tiantian Xiao; Bin Chen; Yulan Lu; Guoqiang Cheng; Wenhao Zhou

doi:10.21037/atm-21-1564

Development and validation of Auto-Neo-electroencephalography (EEG) to estimate brain age and predict report conclusion for electroencephalography monitoring data in neonatal intensive care units

Ann Transl Med. 2021 Aug;9(16):1290. doi: 10.21037/atm-21-1564.

Authors

Xinran Dong¹, Yanting Kong², Yan Xu¹, Yuanfeng Zhou³, Xinhua Wang³, Tiantian Xiao^{2

4}, Bin Chen², Yulan Lu¹, Guoqiang Cheng², Wenhao Zhou^{1

2}

Affiliations

¹ Center for Molecular Medicine, Pediatric Research Institute, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
² Division of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
³ Division of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
⁴ Department of Neonatology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

Abstract

Background: Electroencephalography (EEG) monitoring is widely used in neonatal intensive care units (NICUs). However, conventional EEG report generation processes are time-consuming and labor-intensive. Therefore, an automatic, objective, and comprehensive pipeline for brain age estimation and EEG report conclusion prediction is urgently needed to assist clinician's decision-making.

Methods: We recruited patients who underwent EEG monitoring from the NICU at Children's Hospital of Fudan University from Jan. 2016 to Mar. 2018. A total of 1,851 subjects were enrolled, including the patient's conceptional age (CA) and the clinical EEG report conclusion (normal, slightly abnormal, moderately abnormal, or severely abnormal). A total of 1,591 subjects were used to generate predictive models and 260 were used as the validation dataset. We developed Auto-Neo-EEG (an automatic prediction system to assist clinical neonatal EEG report generation), including signal feature extraction, supervised machine learning realized by gradient boosted models, to estimate brain age and predict EEG report conclusion.

Results: The predicted results from the validation dataset were compared with the clinical observations to assess the performance. In the independent validation dataset, the model could achieve accordance 0.904 on estimating brain age for neonates with normal clinical EEG report conclusion, and differences between the predicted and observed brain age were strongly related with EEG report conclusion abnormality. Further, as for the EEG report conclusion prediction, the model could achieve area under the curve (AUC) of 0.984 for severely abnormal situations, and 0.857 for moderately abnormal ones.

Conclusions: The Auto-Neo-EEG has the high accuracy of estimating brain age and EEG report conclusion, which can potentially greatly accelerate the EEG report generation processes assist in clinical decision making.

Keywords: Neonates; brain age estimation; electroencephalography monitor; machine learning model; neural signal processing.