PacECG-Net: A Multi-modal Approach Integrating LLMs and ECG for LVSD Classification in Pacemaker Patients

Shim, Wonkyeong; Park, Namjun; Ko, Donggeun; Kim, San; Gwag, Hye Bin; Park, Young Jun; Park, Seung-Jung; Kim, Jaekwang

doi:10.1145/3672608.3707983

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PacECG-Net: A Multi-modal Approach Integrating LLMs and ECG for LVSD Classification in Pacemaker Patients

Shim, Wonkyeong;
Park, Namjun;
Ko, Donggeun;
Kim, San;
Gwag, Hye Bin;
... Park, Seung-Jung;
... Kim, Jaekwang;
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초록

This study presents an AI-based model using ECG signals to predict left ventricular systolic dysfunction (LVSD) in pacemaker patients. A 1D convolutional neural network (CNN) combined with large language models processed both sequential ECG data and nonsequential clinical metadata. The model achieved an AUROC of 0.97 on both general and pacemaker-specific datasets, demonstrating its high accuracy. This approach offers a fast, cost-effective alternative to traditional echocardiography, improving LVSD detection in patients with pacemakers. Copyright © 2025 held by the owner/author(s).

키워드

classification; CNN; ECG; echocardiography; imbalanced data; LLMs; LVEF; LVSD; multi-modal; pacemaker; PPM

제목: PacECG-Net: A Multi-modal Approach Integrating LLMs and ECG for LVSD Classification in Pacemaker Patients

저자: Shim, Wonkyeong; Park, Namjun; Ko, Donggeun; Kim, San; Gwag, Hye Bin; Park, Young Jun; Park, Seung-Jung; Kim, Jaekwang

DOI: 10.1145/3672608.3707983

발행일: 2025-05

유형: Proceedings Paper

저널명: Proceedings of the ACM Symposium on Applied Computing

페이지: 1303 ~ 1305