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

被引：16

作者：

Vardas, Panos ^{[1
]}

Cowie, Martin ^{[2
]}

Dagres, Nikolaos ^{[3
]}

Asvestas, Dimitrios ^{[4
]}

Tzeis, Stylianos ^{[4
]}

Vardas, Emmanuel P. ^{[5
]}

Hindricks, Gerhard ^{[3
]}

Camm, John ^{[6
]}

机构：

[1] Hygeia Hosp Grp, Heart Sect, Athens, Greece

[2] Imperial Coll London, London, England

[3] Univ Leipzig, Heart Ctr Leipzig, Dept Electrophys, Leipzig, Germany

[4] Mitera Gen Hosp, Hygeia Grp, Cardiol Dept, Athens, Greece

[5] 251 Airforce Gen Hosp, Athens, Greece

[6] St Georges Univ London, London, England

来源：

EUROPACE | 2020年 / 22卷 / 01期

关键词：

Electrocardiogram; Electrocardiography; Machine learning algorithms; Multilead wearable devices; 12-LEAD ELECTROCARDIOGRAM; ECG; CLASSIFICATION; ARTIFACTS; POSTERIOR; DISEASE;

D O I：

10.1093/europace/euz249

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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.

引用

页码：19 / 23

页数：5

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