Automated interpretation of the 12-lead ECG has remained an underpinning interest in decades of research that has seen a diversity of computing applications in cardiology. The application of computers in cardiology began in the 1960s with early research focusing on the conversion of analogue ECG signals (voltages) to digital samples. Alongside this, software techniques that automated the extraction of wave measurements and provided basic diagnostic statements, began to emerge. In the years since then there have been many significant milestones which include the widespread commercialisation of 12-lead ECG interpretation software, associated clinical utility and the development of the related regulatory frameworks to promote standardised development. In the past few years, the research community has seen a significant rejuvenation in the development of ECG interpretation programs. This is evident in the research literature where a large number of studies have emerged tackling a variety of automated ECG interpretation problems. This is largely due to two factors. Specifically, the technical advances, both software and hardware, that have facilitated the broad adoption of modern artificial intelligence (AI) techniques, and, the increasing availability of large datasets that support modern AI approaches. In this article we provide a very high-level overview of the operation of and approach to the development of early 12-lead ECG interpretation programs and we contrast this to the approaches that are now seen in emerging AI approaches. Our overview is mainly focused on highlighting differences in how input data are handled prior to generation of the diagnostic statement. [Abstract copyright: Copyright © 2021 Elsevier Inc. All rights reserved.]
|Number of pages||5|
|Journal||Journal of Electrocardiology|
|Early online date||17 Aug 2021|
|Publication status||E-pub ahead of print - 17 Aug 2021|
Bibliographical noteFunding Information:
This work has been conducted as part of the Eastern Corridor Medical Engineering centre (ECME). It is supported by the European Union's INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB).
This work has been conducted as part of the Eastern Corridor Medical Engineering centre (ECME). It is supported by the European Union's INTERREG VA Programme , managed by the Special EU Programmes Body (SEUPB).
© 2021 Elsevier Inc.
- Artificial intelligence
- Automated electrocardiogram interpretation
- Deep learning