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

Panos Vardas | Zendy; Martín Cowie | Zendy; Nikolaos Dagres | Zendy; Dimitrios Asvestas | Zendy; Stylianos Tzeis | Zendy; Emmanouil P. Vardas | Zendy; Gerhard Hindricks | Zendy; A. John Camm | Zendy

Open Access

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

Author(s) -

Panos Vardas,

Martín Cowie,

Nikolaos Dagres,

Dimitrios Asvestas,

Stylianos Tzeis,

Emmanouil P. Vardas,

Gerhard Hindricks,

A. John Camm

Publication year - 2019

Publication title -

ep europace

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.119

H-Index - 102

eISSN - 1532-2092

pISSN - 1099-5129

DOI - 10.1093/europace/euz249

Subject(s) - wearable computer , medicine , wearable technology , artificial intelligence , rhythm , field (mathematics) , machine learning , computer science , embedded system , mathematics , pure mathematics

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.

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