Cited by 147ECG Monitoring and AnalysisMachine Learning in HealthcareArtificial Intelligence in Healthcare

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Machine learning in the electrocardiogram

Mincholé, Ana, Camps, Julià, Lyon, Aurore, Rodríguez, Blanca·2019·Journal of Electrocardiology

DOI 10.1016/j.jelectrocard.2019.08.008

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Abstract

The electrocardiogram is the most widely used diagnostic tool that records the electrical activity of the heart and, therefore, its use for identifying markers for early diagnosis and detection is of paramount importance. In the last years, the huge increase of electronic health records containing a systematised collection of different type of digitalised medical data, together with new tools to analyse this large amount of data in an efficient way have re-emerged the field of machine learning in healthcare innovation. This review describes the most recent machine learning-based systems applied to the electrocardiogram as well as pros and cons in the use of these techniques. Machine learning, including deep learning, have shown to be powerful tools for aiding clinicians in patient screening and risk stratification tasks. However, they do not provide the physiological basis of classification outcomes. Computational modelling and simulation can help in the interpretation and understanding of key physiologically meaningful ECG biomarkers extracted from machine learning techniques.

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Authors: Mincholé, A., Camps, J., Lyon, A., & Rodríguez, B.
Affiliations: University of Oxford (GB)
Maastricht University (NL)
Corresponding author: Mincholé, Ana
ORCID: Mincholé, Ana
Camps, Julià
Lyon, Aurore
Rodríguez, Blanca
Venue: Journal of Electrocardiology
Publisher: Elsevier BV
Published: 2019-11
Volume: 57
Pages: S61-S64
ISSN: 0022-0736 (ISSN-L)
DOI: 10.1016/j.jelectrocard.2019.08.008
OpenAlex: W2968959539
Cited by: 147
References: 22
Citations by year: 2026: 17 · 2025: 19 · 2024: 24 · 2023: 33 · 2022: 20 · 2021: 17 · 2020: 15 · 2019: 2
FWCI: 9.75
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Journal of Electrocardiology (publishedVersion)
PubMed (publishedVersion)
Research Publications (Maastricht University) (publishedVersion)
Research Publications (Maastricht University) (submittedVersion)
Funders: Engineering and Physical Sciences Research Council
Horizon 2020 Framework Programme
Wellcome Trust
Topics: Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: electrocardiogram, machine learning, deep learning, ECG biomarkers, risk stratification, patient screening, early diagnosis, electronic health records, computational modelling, clinical decision support
MeSH terms: Machine Learning, Computer Simulation, Electrocardiography, Humans, Electronic Health Records

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Machine learning in the electrocardiogram

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