Doctor Julia Ramirez

Doctor Julia Ramirez

Aragon Institute of Engineering Research, Zaragoza (Spain)

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Julia Ramírez is a María Zambrano Research Fellow at the University of Zaragoza, Spain and holds an honorary lectureship in Cardiovascular Data Science at Queen Mary University of London, United Kingdom. Julia received a Ph.D. in Biomedical Engineering from the University of Zaragoza in 2017. The main objective of her PhD was to extract information from the electrocardiogram (ECG) to non-invasively quantify cardiovascular risk. She, then, moved to Queen Mary University of London to investigate the genetics of ECG markers and their role in assessing cardiovascular risk. In January 2022, she returned to the University of Zaragoza thanks to the María Zambrano Scheme for Attracting International Talent. Julia has 56 publications, and her research has been awarded with four postdoctoral fellowships and several awards, including four Young Investigator Awards. In addition to science, Julia enjoys hiking, and spending time with her family.

Presentations

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

Event: ESC Congress 2024

Topic: Electrocardiogram (ECG) and Arrhythmia Analysis

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

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Combining hypothesis-driven ECG indices with machine learning improves ventricular arrhythmic risk prediction in a low-risk population

Event: ESC Congress 2023

Topic: Electrocardiogram (ECG) and Arrhythmia Analysis

Session: Allow artificial intelligence to assist with diagnostic heavy lifting

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Ventricular Arrhythmic Risk Prediction in Low- and Moderate-risk Populations Using a Single-Lead ECG Heartbeat.

Event: ESC Congress 2022

Topic: Electrocardiogram (ECG) and Arrhythmia Analysis

Session: Basic and Translational Late-Breaking Science 2

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Doctor Julia Ramirez

Biography

Contributor content

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

Combining hypothesis-driven ECG indices with machine learning improves ventricular arrhythmic risk prediction in a low-risk population

Ventricular Arrhythmic Risk Prediction in Low- and Moderate-risk Populations Using a Single-Lead ECG Heartbeat.

ESC 365 is supported by

Doctor Julia Ramirez Follow

Biography

Contributor content

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

Combining hypothesis-driven ECG indices with machine learning improves ventricular arrhythmic risk prediction in a low-risk population

Ventricular Arrhythmic Risk Prediction in Low- and Moderate-risk Populations Using a Single-Lead ECG Heartbeat.

ESC 365 is supported by

Doctor Julia Ramirez