Erik Alonso

Associate Professor

University of the Basque Country

Spain

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Erik Alonso is an Associate Professor at the University of the Basque Country (UPV/EHU) in Spain. His research primarily focuses on artificial intelligence applications in medical emergencies, particularly in cardiac arrest scenarios. Recent publications highlight his work on multiclass rhythm analysis, weight estimation in pediatric emergency care, and innovations in cardiac output estimation. He also investigates methodologies for optimizing ventilation and airway management during resuscitation efforts.

Articles (14)

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2025

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2025

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2024

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2024

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2024

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2024

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2023

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2022

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2021

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2021

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2021

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2021

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2021

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2020

Collaborators (3)

Xabier Jaureguibeitia

University of the Basque Country

SPAIN

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Ancor Sanz-Garcia

Universidad de Castilla-La Mancha - Centro de Estudios Universitarios Talavera de la Reina

SPAIN

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Masashi Okubo

Assistant Professor

University of Pittsburgh

UNITED STATES

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Erik Alonso

Research Interests

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Articles (14)

Artificial Intelligence for Multiclass Rhythm Analysis for Out-of-Hospital Cardiac Arrest During Mechanical Cardiopulmonary Resuscitation

Artificial intelligence for weight estimation in paediatric emergency care

Spanish vs USA cohort comparison of prehospital trauma scores to predict short-term mortality

Cardiac output estimation using ballistocardiography: a feasibility study in healthy subjects

Vasopressor or advanced airway first in cardiac arrest?

Cardiac output estimation using ballistocardiography: a feasibility study in healthy subjects

Methods for calculating ventilation rates during resuscitation from out-of-hospital cardiac arrest

Airway strategy and ventilation rates in the pragmatic airway resuscitation trial

Airway strategy and chest compression quality in the Pragmatic Airway Resuscitation Trial

Airway strategy and chest compression quality in the Pragmatic Airway Resuscitation Trial

Methodology and framework for the analysis of cardiopulmonary resuscitation quality in large and heterogeneous cardiac arrest datasets

Methodology and framework for the analysis of cardiopulmonary resuscitation quality in large and heterogeneous cardiac arrest datasets

A Machine Learning-Based Pulse Detection Algorithm for Use During Cardiopulmonary Resuscitation

A Machine Learning Framework for Pulse Detection During Out-of-Hospital Cardiac Arrest

Collaborators (3)

Xabier Jaureguibeitia

Ancor Sanz-Garcia

Masashi Okubo

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