Year: 2024
Type: conference-paper
Abstract: 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.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 (XG-Boost) based on ECG features and basic demographic features to estimate a wide range of diagnoses, encompassing both cardiac and non-cardiac conditions.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.
Source: Computing in cardiology
Institution: Carl von Ossietzky Universität Oldenburg
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4405435197