Mr Samuel Ruiperez-Campillo

Mr Samuel Ruiperez-Campillo

Stanford University School of Medicine, Palo Alto (United States of America)

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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, and Palo Alto, CA, USA.

Presentations

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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Study of the omnipolar EGM reconstruction for robustness against wavefront propagation in epicardial signals

Event: EHRA 2023

Topic: Invasive Diagnostic Methods

Session: ePoster session 29

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Novel reconstruction technique of omnipolar signals in high density electrode arrays

Event: EHRA 2023

Topic: Arrhythmias

Session: Basic Science

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Reduction of artifacts and noise in small electrogram datasets without manual annotation using transfer machine learning

Event: ESC Congress 2022

Topic: Arrhythmias

Session: Mechanisms of atrial and ventricular arrhythmias

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Artificial intelligence to reduce artifact in cardiac electrophysiological signals

Event: ESC Congress 2022

Topic: Noninvasive Diagnostic Methods

Session: Non-invasive diagnostic methods for various arrhythmias

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Defining refractoriness in single atrial beats using autoencoder neural networks

Event: EHRA 2022

Topic: Arrhythmias

Session: Genetics and bascis in atrial fibrillation and other arrhythmias: is there news?

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Machine-learned physiological signatures from the ECG predict sudden death in ischemic cardiomyopathy

Event: EHRA 2022

Topic: Electrocardiography (ECG)

Session: Moderated ePosters - Diagnostic methods to improve arrhythmia therapy

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Mr Samuel Ruiperez-Campillo

Biography

Contributor content

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

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

Improving omnipolar electrogram reconstruction: an animal model study

Quantification of local heterogeneity from activation maps of omnipolar multielectrode recordings

Study of the omnipolar EGM reconstruction for robustness against wavefront propagation in epicardial signals

Novel reconstruction technique of omnipolar signals in high density electrode arrays

Reduction of artifacts and noise in small electrogram datasets without manual annotation using transfer machine learning

Artificial intelligence to reduce artifact in cardiac electrophysiological signals

Defining refractoriness in single atrial beats using autoencoder neural networks

Machine-learned physiological signatures from the ECG predict sudden death in ischemic cardiomyopathy

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