Speaker illustration

Mr Samuel Ruiperez-Campillo

Swiss Federal Institute of Technology Zurich (ETH Zurich), Zurich (Switzerland)

Member of:

European Society of Cardiology
European Heart Rythm Association

Samuel Ruipérez Campillo is a Biomedical Engineer. He received the 'Rafael del Pino' Excellence Fellowship to study a MSc in Biomedical Devices and Artificial Intelligence at the Swiss Federal Institute of Technology (ETH Zürich). Later he was granted ‘Caixa’ Excellence Fellow and the Fung Institute Fellowhip at UC Berkeley, to study an MEng in Computational Bioengineering with a focus on Data Science and Machine Learning at UC Berkeley. Samuel works at the School of Medicine at Stanford University since 2022, at the computational arrhythmia laboratory guided by Prof. Sanjiv M. Narayan, MD, and collaborates with Universitat Politecnica de Valencia, Hospital la Paz, guided by Prof. José Luis Merino, MD and the Institute of Machine learning, at the ETH Zurich. Samuel is based in Zurich, Switzerland.

Biophysics-inspired deep learning for improved denoising in ventricular signals in ischemic cardiomyopathy

Event: ESC Congress 2025

Topic: Cardiovascular Signal Processing

Session: Precision cardiology: the role of signal processing and artificial intelligence

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Large language models can estimate atrial fibrillation burden and infer progression without ecgs from the electronic healthcare record

Event: ESC Congress 2025

Topic: Large Language Model

Session: Large language models for cardiovascular disease management

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Stratifying pulmonary hypertension severity in newborns from multi-view echocardiograms using variational autoencoders

Event: ESC Congress 2025

Topic: Ultrasound

Session: Using big data for risk stratification

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Comparing artificial-intelligence based tracking of atrial fibrillation waves with clinical phenotypes in patients undergoing ablation

Event: EHRA 2025

Topic: Defining Types of Atrial Fibrillation

Session: Poster session 3: clinical management of arrhythmias

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Designing artificial-intelligence based tracking of atrial fibrillation waves to identify ablation response

Event: EHRA 2025

Topic: Diagnostic Methods

Session: Poster session 3: clinical management of arrhythmias

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Artificial Intelligence Beat-Tracking Improves Regional Rate

Event: EHRA 2025

Topic: Invasive Diagnostic Methods

Session: Poster session 3: clinical management of arrhythmias

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Quantifying local cardiac substrate heterogeneity from high density recordings: an experimental study

Event: EHRA 2024

Topic: Diagnostic Methods

Session: Arrhythmias 4

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Predicting success of atrial fibrillation ablation: comparing machine learning approaches of intracardiac electrograms

Event: ESC Congress 2023

Topic: Rhythm Control, Catheter Ablation

Session: Catheter ablation of atrial fibrillation: efficacy and complications

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Improving omnipolar electrogram reconstruction: an animal model study

Event: ESC Congress 2023

Topic: Diagnostic Methods

Session: Understanding electrograms

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Quantification of local heterogeneity from activation maps of omnipolar multielectrode recordings

Event: ESC Congress 2023

Topic: Invasive Diagnostic Methods

Session: Understanding electrograms

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