Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making. Existing studies have predominantly focused on EEG for assessing brain injury, with some exploring ECG data. However, the integration of EEG, ECG, a...

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Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 11498 - 11
Main Authors:	Niu, Yanxiang, Chen, Xin, Fan, Jianqi, Liu, Chunli, Fang, Menghao, Liu, Ziquan, Meng, Xiangyan, Liu, Yanqing, Lu, Lu, Fan, Haojun
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 03.04.2025 Nature Publishing Group Nature Portfolio
Subjects:	631/378 692/4019 Accuracy Aged Brain injury Cardiac arrest Coma Coma - diagnosis Coma - etiology Decision making EEG Electrocardiography - methods Electroencephalography - methods Female Heart Heart Arrest - complications Heart Arrest - diagnosis Heart Arrest - physiopathology Heart rate variability Humanities and Social Sciences Humans Learning algorithms Machine Learning Male Middle Aged Multi-modal evaluation multidisciplinary Prognosis Quantitative EEG Regression analysis ROC Curve Science Science (multidisciplinary) SHAP analysis Quantitative EEG Multi-modal evaluation SHAP analysis Heart rate variability Cardiac arrest Machine learning
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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