Latent representation of intracardiac electrograms using an AI-based autoencoder for rhythm classification

30 August 2025 (09:15 - 10:00)

Organised by:

Video

Sign in for access options

Abstract

Slides

About the speaker

Mr Long Lin

University Carlos III of Madrid, Madrid (Spain)

6 More presentations in this session

Presentation thumbnail

Artificial intelligence for prediction of major arrhythmic events in dilated cardiomyopathy

Doctor M. Coriano' (London, GB)

Abstract Slides Video

Presentation thumbnail

Impact of arrhythmias on the performance of a deep learning model for predicting echocardiographic abnormalities from electrocardiograms

Doctor G. Fujiki (Tokyo, JP)

Abstract Slides Video

Presentation thumbnail

Development of deep learning models for predicting atrial fibrillation occurrence using real world hand-held mobile electrocardiograms

Mr Y. Na (Seoul, KR)

Abstract Slides Video

Presentation thumbnail

Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using artificial intelligence-enhanced electrocardiogram

Doctor Z. Xiao (Shanghai, CN)

Abstract Slides Video

Presentation thumbnail

Identifying optimal electrocardiogram lead combinations for artificial intelligence

Mr M. Nakayama (Isehara, JP)

Abstract Slides Video

Access the full session

Session thumbnail

Data meets diagnostics: use of artificial intelligence in arrhythmias

The Event

Event poster

ESC Congress 2025

30 August 2025 09:15 CET

You may be interested in

Congress Session

Revolutionising cardiac diagnosis: exploring deep learning in electrocardiography and beyond

Congress Presentation

Rhythm classifier: artificial intelligence for rhythm classification in insertable cardiac monitor

Congress Session

eCardiology

ESC 365 is supported by

novartis

Explore sponsored resources

ESC 365 is supported by

novartis

Explore sponsored resources