Frontiers in digital health · 2026
Exploratory deep-learning-driven early risk stratification of significant neurological injury in pediatric extracorporeal membrane oxygenation.
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Tang Haiyang, McGetrick Molly, Hwang James, Sohrabi Salman, Raman Lakshmi, Liu Hanli
Аннотация
OBJECTIVE: To develop and examine a multimodal deep-learning framework utilizing clinical variables with and without time-dependent electroencephalogram (EEG) spectral features for predicting significant neurological injury (SNI) in pediatric patients with extracorporeal membrane oxygenation (ECMO).
METHODS: Data were collected from 73 pediatric ECMO patients. After excluding patients who had missing or discontinuous temporal data within 72 h after EEG initiation, 43 patients with usable EEG time-series data were analyzed. Neurological injury severity was quantified using a neuroimaging score (NIS) derived from post-cannulation imaging, with SNI defined as NIS ≥ 8. Model inputs included six clinical variables and six EEG features representing relative delta (1-4 Hz) and theta (4-8 Hz) power from the left, right, and bilateral hemispheres. Down-sampled EEG power signals were segmented into multiple 30-minute windows. A deep-learning fusion framework with modality-specific encoders was developed for SNI prediction, followed by SHapley Additive exPlanations (SHAP)-based feature attribution analysis.
RESULTS: At the patient level, the proposed multilayer perceptron model used only for clinical variables achieved the highest overall performance with an area under the curve (AUC) of 74.29%. The Wilcoxon signed-rank test was performed and showed no statistical significance in model performance between the clinical-only and fusion model. SHAP analysis indicated that clinical variables were the primary contributors, not the EEG inputs, to SNI prediction.
CONCLUSION: A multilayer perceptron neural network used with six clinical inputs demonstrated moderate performance in identifying SNI among pediatric ECMO patients; integration with EEG-derived spectral features did not significantly enhance the performance. However, this conclusion is highly exploratory and dependent on selected EEG inputs, thus needing to be valid with a larger patient cohort, appropriate EEG feature selections, and assess cross-site generalizability.
METHODS: Data were collected from 73 pediatric ECMO patients. After excluding patients who had missing or discontinuous temporal data within 72 h after EEG initiation, 43 patients with usable EEG time-series data were analyzed. Neurological injury severity was quantified using a neuroimaging score (NIS) derived from post-cannulation imaging, with SNI defined as NIS ≥ 8. Model inputs included six clinical variables and six EEG features representing relative delta (1-4 Hz) and theta (4-8 Hz) power from the left, right, and bilateral hemispheres. Down-sampled EEG power signals were segmented into multiple 30-minute windows. A deep-learning fusion framework with modality-specific encoders was developed for SNI prediction, followed by SHapley Additive exPlanations (SHAP)-based feature attribution analysis.
RESULTS: At the patient level, the proposed multilayer perceptron model used only for clinical variables achieved the highest overall performance with an area under the curve (AUC) of 74.29%. The Wilcoxon signed-rank test was performed and showed no statistical significance in model performance between the clinical-only and fusion model. SHAP analysis indicated that clinical variables were the primary contributors, not the EEG inputs, to SNI prediction.
CONCLUSION: A multilayer perceptron neural network used with six clinical inputs demonstrated moderate performance in identifying SNI among pediatric ECMO patients; integration with EEG-derived spectral features did not significantly enhance the performance. However, this conclusion is highly exploratory and dependent on selected EEG inputs, thus needing to be valid with a larger patient cohort, appropriate EEG feature selections, and assess cross-site generalizability.