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Andoni Elola

Researcher at University of the Basque Country

Publications -  29
Citations -  530

Andoni Elola is an academic researcher from University of the Basque Country. The author has contributed to research in topics: Computer science & Cardiopulmonary resuscitation. The author has an hindex of 9, co-authored 22 publications receiving 265 citations. Previous affiliations of Andoni Elola include Emory University.

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Journal ArticleDOI

Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020.

TL;DR: This work addresses issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020, setting a new bar in reproducibility for public data science competitions.
Journal ArticleDOI

Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest.

TL;DR: Two deep neural network architectures are proposed to detect pulse using short ECG segments to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR) and improved the performance of state of the art methods by more than 1.5 points in BAC.
Posted ContentDOI

Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022

TL;DR: A cost-based evaluation metric is devised that captures the costs of screening, treatment, and diagnostic errors, allowing us to investigate the benefits of algorithmic pre-screening and facilitate the development of more clinically relevant algorithms.
Journal ArticleDOI

Feasibility of the capnogram to monitor ventilation rate during cardiopulmonary resuscitation.

TL;DR: An automatic algorithm based on the capnogram to detect ventilations and provide feedback on ventilation rate during CPR was evaluated, and its accuracy was proven even in intervals where canpography signal was severely corrupted by CCs.
Journal ArticleDOI

ECG-based pulse detection during cardiac arrest using random forest classifier

TL;DR: The first method based exclusively on the ECG for the automatic detection of pulse during cardiopulmonary resuscitation is introduced, which permits to detect the presence of pulse accurately, minimizing interruptions in cardiopula resuscitation therapy, and could contribute to improve survival from cardiac arrest.