Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/29763
Title: Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study.
Austin Authors: Chen, Zhe Sage;Hsieh, Aaron;Sun, Guanghao;Bergey, Gregory K;Berkovic, Samuel F ;Perucca, Piero ;D'Souza, Wendyl;Elder, Christopher J;Farooque, Pue;Johnson, Emily L;Barnard, Sarah;Nightscales, Russell ;Kwan, Patrick;Moseley, Brian;O'Brien, Terence J;Sivathamboo, Shobi;Laze, Juliana;Friedman, Daniel;Devinsky, Orrin
Affiliation: Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States..
Tandon School of Engineering, New York University, New York, NY, United States..
Johns Hopkins University School of Medicine, Baltimore, MD, United States..
Medicine (University of Melbourne)
Neurology
Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia..
Division of Epilepsy and Sleep, Columbia University, New York, NY, United States..
Yale University School of Medicine, New Haven, CT, United States..
Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia..
Clinical Development Neurocrine Biosciences Inc., San Diego, CA, United States..
Comprehensive Epilepsy Center, New York University Langone Health, New York, NY, United States..
Department of Neurology, New York University Grossman School of Medicine, New York, NY, United States.. Comprehensive Epilepsy Center, New York University Langone Health, New York, NY, United States..
Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States..
Department of Neurology, Alfred Health, Melbourne, VIC, Australia..
Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia..
Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia..
Issue Date: 18-Mar-2022
Date: 2022
Publication information: Frontiers in neurology 2022; 13: 858333
Abstract: Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73-0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.
URI: https://ahro.austin.org.au/austinjspui/handle/1/29763
DOI: 10.3389/fneur.2022.858333
ORCID: 0000-0003-4580-841X
0000-0002-7855-7066
0000-0001-9108-207X
Journal: Frontiers in neurology
PubMed URL: 35370908
PubMed URL: https://pubmed.ncbi.nlm.nih.gov/35370908/
ISSN: 1664-2295
Type: Journal Article
Subjects: ECG
EEG
SUDEP
biomarker
machine learning
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