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

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

Abstract 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 EE...

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Bibliographic Details
Main Authors: Yanxiang Niu, Xin Chen, Jianqi Fan, Chunli Liu, Menghao Fang, Ziquan Liu, Xiangyan Meng, Yanqing Liu, Lu Lu, Haojun Fan
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Cardiac arrest
Machine learning
Quantitative EEG
Heart rate variability
Multi-modal evaluation
SHAP analysis
Online Access:https://doi.org/10.1038/s41598-025-93579-0
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https://doi.org/10.1038/s41598-025-93579-0

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