Long-term time-to-ventricular arrhythmic event prediction using a deep learning based model on the electrocardiogram signal

Back

ESC Professional Premium Access

Long-term time-to-ventricular arrhythmic event prediction using a deep learning based model on the electrocardiogram signal

Session: Revolutionising cardiac diagnosis: exploring deep learning in electrocardiography and beyond

Topic: Electrocardiogram (ECG) and Arrhythmia Analysis

Speaker: Doctor J. Ramirez (Zaragoza, ES)

Event: ESC Congress 2024

Congress Presentation

Add to my library

About the speaker

Doctor Julia Ramirez

Aragon Institute of Engineering Research, Zaragoza (Spain)

1 presentation

0 follower

5 more presentations in this session

Unsupervised electrocardiogram feature extraction using deep learning empowers discovery of genetic and phenotypic determinants of cardiac electrophysiology

Speaker: Doctor E. Sieliwonczyk (Antwerpen, BE)

Artificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis

Speaker: Mr V. Sangha (New Haven, US)

Interethnic validation of an artificial intelligence algorithm for prediction of atrial fibrillation using sinus rhythm electrocardiogram

Speaker: Associate Professor J. Lee (Seongnam, KR)

A novel 12-lead standard ECG derived scoring system for risk stratification in the emergency department

Speaker: Doctor F. Theurl (Innsbruck, AT)

121 year after Einthoven: show me the normal ECG?

Speaker: Doctor P. Van Dam (Cracow, PL)

Access the full session

Revolutionising cardiac diagnosis: exploring deep learning in electrocardiography and beyond

Speakers: Doctor J. Ramirez, Doctor E. Sieliwonczyk, Mr V. Sangha, Associate Professor J. Lee, Doctor F. Theurl...

Access

Video

About the event

ESC Congress 2024

30 August - 2 September 2024

Sessions Presentations

Congress

ESC 365 is supported by

Explore sponsored resources