LMCG-Net: Integrating LLMs and ECG for LVEF-Based Classification for Pacing Patients

Wonkyeong Shim, Namjun Park, Donggeun Ko, Hyebin Gwag, Youngjun Park, Seungjung Park, Jaekwang Kim

Abstract: Detecting left ventricular systolic dysfunction (LVSD) traditionally relies on expensive and specialized echocardiography, limiting accessibility for many patients. To address this, researchers explore the potential of electrocardiography (ECG), a more affordable and widely available alternative, despite its historically limited performance in cardiac dysfunction detection. In this study, we present a novel approach that integrates a 1D convolutional neural networks (CNNs) with a large-scale language model (LLM) to simultaneously analyze sequential ECG data and non-sequential clinical metadata. To validate our model’s effectiveness, we conducted rigorous comparative experiments on both specially collected clinical data and public datasets, achieving an impressive AUROC of 0.97 across both. Our findings underscore the capability of ECG-based AI to accurately predict LVSD including pacemaker patients, offering a rapid and cost-effective alternative to traditional echocardiography. This innovative approach could significantly enhance early diagnosis and management of cardiac dysfunction.

External IDs:dblp:conf/bibm/ShimPKGPPK24