본문 바로가기 주메뉴 바로가기

논문발간

Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocar diogram Signals Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study
Background:When using a smartwatch to obtain electrocardiogram (ECG) signals from multiple leads, the device has to be placed on different parts of the body sequentially.
The ECG signals measured from different leads are asynchronous. Artificial intelligence (AI) models for asynchronous ECG signals have barely been explored.

Objective:We aimed to develop an AI model for detecting acute myocardial infarction using asynchronous ECGs and compare its performance with that of the automatic ECG interpretations provided by a commercial ECG analysis software. We sought to evaluate the feasibility of implementing multiple lead–based AI-enabled ECG algorithms on smart watches. Moreover, we aimed to determine the optimal number of leads for sufficient diagnostic power.

Methods:We extracted ECGs recorded within 24 hours from each visit to the emergency room of Ajou University Medical Center between June 1994 and January 2018 from patients aged 20 years or older. The ECGs were labeled on the basis of whether a diagnostic code corresponding to acute myocardial infarction was entered. We derived asynchronous ECG lead sets from standard 12-lead ECG reports and simulated a situation similar to the sequential recording of ECG leads via smartwatches. We constructed an AI model based on residual networks and self-attention mechanisms by randomly masking each lead channel during the training phase and then testing the model using various targeting lead sets with the remaining lead channels masked.
날짜 2023-12-12
링크 http://urgent.dev.website.ne.kr/sian/sub3_list.php
저자 changho Han, Jong-Hwan Jang, changho Han, Jong-Hwan Jang, changho Han, Jong-Hwan Jang, changho Han, Jong-Hwan Jang,
태그
Automated
Automated Automated
tag3
LIST
TOP