Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features

• URL: http://arxiv.org/abs/2408.17329v1
• Date: Fri, 30 Aug 2024 14:42:03 GMT
• Title: Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features
• Authors: Juan Miguel Lopez Alcaraz, Nils Strodthoff,
• Abstract summary: We use publicly available datasets to investigate the feasibility of inferring general diagnostic conditions from ECG features. We train a tree-based model (XGBoost) based on ECG features and basic demographic features to estimate a wide range of diagnoses.
• Score: 1.068128849363198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Introduction: Ensuring timely and accurate diagnosis of medical conditions is paramount for effective patient care. Electrocardiogram (ECG) signals are fundamental for evaluating a patient's cardiac health and are readily available. Despite this, little attention has been given to the remarkable potential of ECG data in detecting non-cardiac conditions. Methods: In our study, we used publicly available datasets (MIMIC-IV-ECG-ICD and ECG-VIEW II) to investigate the feasibility of inferring general diagnostic conditions from ECG features. To this end, we trained a tree-based model (XGBoost) based on ECG features and basic demographic features to estimate a wide range of diagnoses, encompassing both cardiac and non-cardiac conditions. Results: Our results demonstrate the reliability of estimating 23 cardiac as well as 21 non-cardiac conditions above 0.7 AUROC in a statistically significant manner across a wide range of physiological categories. Our findings underscore the predictive potential of ECG data in identifying well-known cardiac conditions. However, even more striking, this research represents a pioneering effort in systematically expanding the scope of ECG-based diagnosis to conditions not traditionally associated with the cardiac system.

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