Abstract
The electrocardiogram (ECG) is the most widely used diagnostic test in clinical settings. The ECG signal is well-understood and its use in clinical applications is regulated by widely adopted clinical practice guidelines. Computer-aided automated ECG interpretations, hence, play an important role in cardiovascular disease diagnostics and risk stratification. This chapter will provide a comprehensive overview of the cellular and electrical basis of cardiac electrophysiology, technical specifications that govern electrocardiography, the current standards of automated ECG interpretation algorithms, machine learning applications in ECG interpretation, and the role of interpretation automation in diagnostic and risk stratification. Challenges, limitations, and future opportunities of computer-aided ECG diagnostics are also summarized.
| Original language | English |
|---|---|
| Title of host publication | Cardiovascular and Coronary Artery Imaging |
| Editors | Ayman S. El-Baz, Jasjit Suri |
| Publisher | Elsevier |
| Chapter | 3 |
| Pages | 45-87 |
| Volume | 1 |
| ISBN (Electronic) | 9780128227077 |
| ISBN (Print) | 9780128227060 |
| DOIs | |
| Publication status | Published (in print/issue) - 26 Nov 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- automated interpretation
- 12-lead ECG
- AI
- machine learning
- cardiovascular disease
Fingerprint
Dive into the research topics of 'The role of automated 12-lead ECG interpretation in the diagnosis and risk stratification of cardiovascular disease'. Together they form a unique fingerprint.Student theses
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Data analytics to augment decision making in cardiac care
Rjoob, K. (Author), Bond, R. (Supervisor), Mc Gilligan, V. (Supervisor) & Peace, A. (Supervisor), Jun 2022Student thesis: Doctoral Thesis
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