The electrocardiogram endeavour: from the Holter single-lead recordings to multilead wearable devices supported by computational machine learning algorithms.

Clicks: 412
ID: 48163
2019
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Ranked #3 of 28 articles by views in Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology

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Abstract
This review aims to provide a comprehensive recapitulation of the evolution in the field of cardiac rhythm monitoring, shedding light in recent progress made in multilead ECG systems and wearable devices, with emphasis on the promising role of the artificial intelligence and computational techniques in the detection of cardiac abnormalities.
Reference Key
vardas2019theeuropace Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Vardas, Panos;Cowie, Martin;Dagres, Nikolaos;Asvestas, Dimitrios;Tzeis, Stylianos;Vardas, Emmanuel P;Hindricks, Gerhard;Camm, John;
Journal Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
Year 2019
DOI
euz249
URL
Keywords
electrocardiogram machine learning algorithms electrocardiography multilead wearable devices

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