The role of automated 12-lead ECG interpretation in the diagnosis and risk stratification of cardiovascular disease

Salah Al-Zaiti; Ziad Faramand; Khaled Rjoob; D Finlay; RR Bond

doi:10.1016/B978-0-12-822706-0.00005-6

The role of automated 12-lead ECG interpretation in the diagnosis and risk stratification of cardiovascular disease

Salah Al-Zaiti, Ziad Faramand, Khaled Rjoob, D Finlay, RR Bond

Research output: Chapter in Book/Report/Conference proceeding › Chapter

3 Citations (Scopus)

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	https://doi.org/10.1016/B978-0-12-822706-0.00005-6
Publication status	Published (in print/issue) - 26 Nov 2021

Keywords

automated interpretation
12-lead ECG
AI
machine learning
cardiovascular disease

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/B978-0-12-822706-0.00005-6

Cite this

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AU - Bond, RR

PY - 2021/11/26

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AB - 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.

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ER -

The role of automated 12-lead ECG interpretation in the diagnosis and risk stratification of cardiovascular disease

Abstract

Keywords

UN SDGs

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