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

Vardas, P; Cowie, M; Dagres, N; Asvestas, D; Tzeis, S; Vardas, EP; Hindricks, G; Camm, J (2020) The electrocardiogram endeavour: from the Holter single-lead recordings to multilead wearable devices supported by computational machine learning algorithms. Europace, 22 (1). pp. 19-23. ISSN 1532-2092 https://doi.org/10.1093/europace/euz249
SGUL Authors: Camm, Alan John

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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.

Item Type: Article
Additional Information: This is a pre-copyedited, author-produced version of an article accepted for publication in EP Europace following peer review. The version of record Panos Vardas, Martin Cowie, Nikolaos Dagres, Dimitrios Asvestas, Stylianos Tzeis, Emmanuel P Vardas, Gerhard Hindricks, John Camm, The electrocardiogram endeavour: from the Holter single-lead recordings to multilead wearable devices supported by computational machine learning algorithms, EP Europace, Volume 22, Issue 1, January 2020, Pages 19–23 is available online at: https://doi.org/10.1093/europace/euz249
Keywords: Electrocardiogram, Electrocardiography, Machine learning algorithms, Multilead wearable devices, Electrocardiogram, Electrocardiography, Machine learning algorithms, Multilead wearable devices, 1103 Clinical Sciences, Cardiovascular System & Hematology
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Journal or Publication Title: Europace
ISSN: 1532-2092
Language: eng
Dates:
DateEvent
January 2020Published
18 September 2019Published Online
12 September 2019Accepted
Publisher License: Publisher's own licence
PubMed ID: 31535151
Go to PubMed abstract
URI: http://openaccess.sgul.ac.uk/id/eprint/111249
Publisher's version: https://doi.org/10.1093/europace/euz249

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