Machine learning in the electrocardiogram
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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Wearable ECG sensors continuously monitor heart activity and facilitate early detection of arrhythmias and cardiovascular abnormalities.
“In this regard, wearable ECG sensors continuously monitor heart activity and facilitate early detection of arrhythmias and cardiovascular abnormalities [181].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [181]
Multimodal sensing, combining ECG data with other physiological and environmental parameters, such as EOG, EMG, temperature, strain, humidity, and bacterial infection, facilitated a comprehensive health profile.
“Additionally, multimodal sensing, combining ECG data with other physiological and environmental parameters, such as EOG, EMG, temperature, strain, humidity, and bacterial infection, facilitated a comprehensive health profile [8,181,237] (Figure 7c).”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [181]
Integrating AI and ML algorithms into wearable ECG sensors has substantially enhanced the accuracy of ECG analysis.
“Integrating AI and ML algorithms into wearable ECG sensors has substantially enhanced the accuracy of ECG analysis [181].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [181]
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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
- Venue
- Journal of Electrocardiology
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 57
- Pages
- S61-S64
- ISSN
- 0022-0736 (ISSN-L)
- 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
- 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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