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Updated: Aug 5, 2025
Author Spotlight: Assessing the Feasibility of Using Amplitude-Integrated EEG During Neonatal Transport
Published on: June 21, 2024
Leveraging electronic medical record-embedded standardised electroencephalogram reporting to develop neonatal seizure
Jillian L McKee1, Michael C Kaufman2, Alexander K Gonzalez3
1Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Insights
Accurate prediction of neonatal seizures is possible using data from standardized electroencephalogram (EEG) reports. Machine learning models can identify high-risk infants, optimizing continuous EEG monitoring resources.
Area of Science:
- Neonatal neurology
- Medical informatics
- Machine learning in healthcare
Background:
- Continuous electroencephalogram (CEEG) monitoring is resource-intensive and typically used for neonates at high risk of seizures.
- Accurate seizure prediction models are needed to optimize the allocation of CEEG resources.
- Standardized EEG reporting can facilitate the development of such predictive models.
Purpose of the Study:
- To develop and evaluate seizure prediction models for vulnerable neonates using data from standardized EEG reports.
- To assess the feasibility of using machine learning models to predict neonatal seizures based on early EEG features.
- To improve the efficiency of CEEG monitoring by identifying neonates who require extended monitoring.
Main Methods:
- Retrospective cohort study of neonates undergoing CEEG.
- Implementation of a novel, standardized CEEG reporting system within the electronic medical record (EMR).
- Development of logistic regression, decision tree, and random forest models using Day 1 EEG features to predict future seizures.
Main Results:
- The study included 1117 neonates, with a subgroup of 150 neonates with hypoxic ischemic encephalopathy.
- Standardized EEG reporting achieved over 95% completion of key EEG features.
- Random forest models achieved classification accuracies up to 90% in the overall cohort and 97% in the hypoxic ischemic encephalopathy subgroup, with high recall and precision.
Conclusions:
- Standardized EEG data can be used to predict neonatal seizures with high accuracy (>90%) using machine learning models.
- These models can guide decisions on the necessity of continuing CEEG monitoring beyond the first day.
- Standardized data collection supports learning health systems and personalized CEEG utilization.
Background:
Accurate prediction of seizures can help to direct resource-intense continuous electroencephalogram (CEEG) monitoring to neonates at high risk of seizures. We aimed to use data from standardised EEG reports to generate seizure prediction models for vulnerable neonates.
Methods:
In this retrospective cohort study, we included neonates who underwent CEEG during the first 30 days of life at the Children's Hospital of Philadelphia (Philadelphia, PA, USA). The hypoxic ischaemic encephalopathy subgroup included only patients with CEEG data during the first 5 days of life, International Classification of Diseases, revision 10, codes for hypoxic ischaemic encephalopathy, and documented therapeutic hypothermia. In January, 2018, we implemented a novel CEEG reporting system within the electronic medical record (EMR) using common data elements that incorporated standardised terminology. All neonatal CEEG data from Jan 10, 2018, to Feb 15, 2022, were extracted from the EMR using age at the time of CEEG. We developed logistic regression, decision tree, and random forest models of neonatal seizure prediction using EEG features on day 1 to predict seizures on future days.
Findings:
We evaluated 1117 neonates, including 150 neonates with hypoxic ischaemic encephalopathy, with CEEG data reported using standardised templates between Jan 10, 2018, and Feb 15, 2022. Implementation of a consistent EEG reporting system that documents discrete and standardised EEG variables resulted in more than 95% reporting of key EEG features. Several EEG features were highly correlated, and patients could be clustered on the basis of specific features. However, no simple combination of features adequately predicted seizure risk. We therefore applied computational models to complement clinical identification of neonates at high risk of seizures. Random forest models incorporating background features performed with classification accuracies of up to 90% (95% CI 83-94) for all neonates and 97% (88-99) for neonates with hypoxic ischaemic encephalopathy; recall (sensitivity) of up to 97% (91-100) for all neonates and 100% (100-100) for neonates with hypoxic ischaemic encephalopathy; and precision (positive predictive value) of up to 92% (84-96) in the overall cohort and 97% (80-99) in neonates with hypoxic ischaemic encephalopathy.
Interpretation:
Using data extracted from the standardised EEG report on the first day of CEEG, we predict the presence or absence of neonatal seizures on subsequent days with classification performances of more than 90%. This information, incorporated into routine care, could guide decisions about the necessity of continuing EEG monitoring beyond the first day, thereby improving the allocation of limited CEEG resources. Additionally, this analysis shows the benefits of standardised clinical data collection, which can drive learning health system approaches to personalised CEEG use.
Funding:
Children's Hospital of Philadelphia, the Hartwell Foundation, the National Institute of Neurological Disorders and Stroke, and the Wolfson Foundation.
