Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications

Khaled Rjoob, RR Bond, D Finlay, V. E. McGilligan, Stephen J Leslie, Ali Rababah, Aleeha Iftikhar, Daniel Güldenring, Charles Knoery, Anne McShane, Aaron Peace, Peter Macfarlane

Research output: Contribution to journalArticlepeer-review

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Abstract

Background: The application of artificial intelligence to interpret the electrocardiogram (ECG) has predominantly included the use of knowledge engineered rule-based algorithms which have become widely used today in clinical practice. However, over recent decades, there has been a steady increase in the number of research studies that are using machine learning (ML) to read or interrogate ECG data. Objective: The aim of this study is to review the use of ML with ECG data using a time series approach. Methods: Papers that address the subject of ML and the ECG were identified by systematically searching databases that archive papers from January 1995 to October 2019. Time series analysis was used to study the changing popularity of the different types of ML algorithms that have been used with ECG data over the past two decades. Finally, a meta-analysis of how various ML techniques performed for various diagnostic classifications was also undertaken. Results: A total of 757 papers was identified. Based on results, the use of ML with ECG data started to increase sharply (p < 0.001) from 2012. Healthcare applications, especially in heart abnormality classification, were the most common application of ML when using ECG data (p < 0.001). However, many new emerging applications include using ML and the ECG for biometrics and driver drowsiness. The support vector machine was the technique of choice for a decade. However, since 2018, deep learning has been trending upwards and is likely to be the leading technique in the coming few years. Despite the accuracy paradox, accuracy was the most frequently used metric in the studies reviewed, followed by sensitivity, specificity, F1 score and then AUC. Conclusion: Applying ML using ECG data has shown promise. Data scientists and physicians should collaborate to ensure that clinical knowledge is being applied appropriately and is informing the design of ML algorithms. Data scientists also need to consider knowledge guided feature engineering and the explicability of the ML algorithm as well as being transparent in the algorithm's performance to appropriately calibrate human-AI trust. Future work is required to enhance ML performance in ECG classification.

Original languageEnglish
Article number102381
JournalArtificial Intelligence in Medicine
Volume132
Early online date27 Aug 2022
DOIs
Publication statusPublished (in print/issue) - 31 Oct 2022

Bibliographical note

Funding Information:
This work is supported by the European Union's INTERREG VA programme, managed by the Special EU Programmes Body (SEUPB). The work is associated with the project – ‘Centre for Personalised Medicine – Clinical Decision Making and Patient Safety’. The views and opinions expressed in this study do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB). [Formula presented][Formula presented]

Funding Information:
This work is supported by the European Union 's INTERREG VA programme, managed by the Special EU Programmes Body (SEUPB). The work is associated with the project – ‘Centre for Personalised Medicine – Clinical Decision Making and Patient Safety’. The views and opinions expressed in this study do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • AI
  • data science
  • machine learning
  • digital health
  • algorithms
  • electrocardiogram
  • cardiology
  • digital cardiology
  • meta analysis
  • deep learning
  • artificial intelligence
  • Deep learning
  • Electrocardiogram
  • Machine learning
  • Artificial intelligence

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