DeepCardio—Cardiac Arrhythmia Detection with a Neural Network

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dc.contributor.authorVan Zaen, Jérôme
dc.contributor.authorChételat, Olivier
dc.contributor.authorLemay, Mathieu
dc.contributor.authorMuntané Calvo, Enric
dc.contributor.authorDelgado-Gonzalo, Ricard
dc.date.accessioned2025-11-11T15:37:39Z
dc.date.available2025-11-11T15:37:39Z
dc.date.issued2019
dc.description.abstractMonitoring cardiac arrhythmias over long periods of time is a resource-intensive task as a specialist needs to review ECG signals. Methods for automatic detection can help to reduce the time needed to review the data by selecting interesting segments. However, these methods need to be accurate to avoid erroneous detections. We trained a neural network model to detect arrhythmias from a single-lead ECG signal and applied it to data collected with a smart vest previously developed at CSEM. The results are promising for screening cardiac arrhythmias in large populations.
dc.identifier.citationCSEM Scientific and Technical Report 2019, p. 97
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1785
dc.titleDeepCardio—Cardiac Arrhythmia Detection with a Neural Network
dc.typeCSEM Report
dc.type.csemdivisionsBU-D
dc.type.csemresearchareasDigital Health
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