Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study.

ABSTRACT:

Introduction

The diagnostic performance of automated analysis of electrocardiograms for screening children with pediatric heart diseases at risk of sudden cardiac death is unknown. In this study, we aimed to develop and validate a deep learning-based model for automated analysis of ECGs in children.

Methods

Wave data of 12-lead electrocardiograms were transformed into a tensor sizing 2 × 12 × 400 using signal processing methods. A deep learning-based model to classify abnormal electrocardiograms based on age, sex, and the transformed wave data was developed using electrocardiograms performed in patients at the age of 6-18 years during 2003-2006 at a tertiary referral hospital in Japan. Eighty-three percent of the patients were assigned to a training group, and 17% to a test

SUBMITTER: Toba S 

PROVIDER: S-EPMC11919894 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature