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Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study.

Zhe Sage Chen^1,2, Aaron Hsieh³, Guanghao Sun¹

¹Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States.

Frontiers in Neurology

|April 4, 2022

Summary

Machine learning models can predict sudden unexpected death in epilepsy (SUDEP) risk using noninvasive EEG and ECG data. This approach may help identify high-risk epilepsy patients for preventive strategies.

Keywords:

ECG EEG SUDEP biomarker machine learning

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Area of Science:

Neurology
Biomedical Engineering
Data Science

Background:

Sudden unexpected death in epilepsy (SUDEP) is the primary cause of mortality in epilepsy patients.
Current methods for predicting SUDEP risk are limited, lacking validated individual risk prediction tools.
Prolonged postictal EEG suppression (PGES) shows potential as a SUDEP biomarker but is infrequently observed and requires specialized monitoring.

Purpose of the Study:

To develop and validate machine learning models for predicting SUDEP risk using readily available interictal EEG and ECG recordings.
To explore the utility of interictal EEG and ECG features as noninvasive biomarkers for SUDEP risk stratification.
To compare the performance of different machine learning algorithms in predicting SUDEP risk.

Main Methods:

A multicenter, retrospective cohort study involving 30 SUDEP cases and 58 matched living epilepsy controls.
Extraction of interictal EEG features (alpha and low gamma power ratio) and ECG features (heart rate variability).
Training and cross-validation of machine learning models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and convolutional neural network (CNN).

Main Results:

The logistic regression (LR) model demonstrated the best performance among the evaluated classifiers.
The LR model achieved a median AUC of 0.77 in cross-validation and a mean AUC of 0.79 in leave-one-center-out prediction.
Key predictors included interictal EEG alpha/low gamma power ratio and heart rate variability from ECG.

Conclusions:

Machine learning models utilizing interictal EEG and ECG data can effectively quantify SUDEP risk in epilepsy patients.
These noninvasive, low-cost biomarkers offer a promising avenue for identifying individuals at high risk of SUDEP.
Future model refinements could enable individualized SUDEP risk prediction, aiding clinicians in implementing timely preventive measures.