Primer on Machine Learning in Electrophysiology

Shane E. Loeffler, Natalia Trayanova

Research output: Contribution to journalReview articlepeer-review

Abstract

Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies.

Original languageEnglish (US)
Article numbere06
JournalArrhythmia and Electrophysiology Review
Volume12
DOIs
StatePublished - 2023
Externally publishedYes

Keywords

  • Machine learning
  • artificial intelligence
  • cardiac
  • electrophysiology
  • primer

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Physiology (medical)

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