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

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 (pConclusion: 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 statusE-pub ahead of print - 27 Aug 2022

Keywords

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

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