@inproceedings{aa791f0bccf445d99f02f8c001c59a10,
title = "PacECG-Net: A Multi-modal Approach Integrating LLMs and ECG for LVSD Classification in Pacemaker Patients",
abstract = "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.",
keywords = "classification, CNN, ECG, echocardiography, imbalanced data, LLMs, LVEF, LVSD, multi-modal, pacemaker, PPM",
author = "Wonkyeong Shim and Namjun Park and Donggeun Ko and San Kim and Gwag, \{Hye Bin\} and Park, \{Young Jun\} and Seung-Jung Park and Jaekwang Kim",
note = "Publisher Copyright: Copyright {\textcopyright} 2025 held by the owner/author(s).; 40th Annual ACM Symposium on Applied Computing, SAC 2025 ; Conference date: 31-03-2025 Through 04-04-2025",
year = "2025",
month = may,
day = "14",
doi = "10.1145/3672608.3707983",
language = "English",
series = "Proceedings of the ACM Symposium on Applied Computing",
publisher = "Association for Computing Machinery",
pages = "1303--1305",
booktitle = "40th Annual ACM Symposium on Applied Computing, SAC 2025",
}