Primer on Machine Learning in Electrophysiology - Overview of supervised and unsupervised models

Aug 2, 2026

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Machine learning, a core branch of artificial intelligence, is positioned as a driver of rapid technological change in medicine. In cardiac arrhythmia and electrophysiology, its clinical adoption depends on broad understanding of both the rationale and real-world successes of specific methods. This primer reviews common supervised models—least squares, support vector machines, neural networks, and random forests—alongside key unsupervised approaches including k-means and principal component analysis, explaining how and why they are applied in electrophysiology studies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Abstract

Machine Learning Fundamentals

Types of Learning

Supervised and Unsupervised Models in Electrophysiology

Primer on Machine Learning in Electrophysiology Shane E Loeffler 1 and Natalia Trayanova 1,2 1. Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, US; 2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, US 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. Keywords Machine learning, artificial intelligence, cardiac, electrophysiology, primer Disclosure: The authors have no conflicts of interest to declare. Funding: This project was supported by National Institutes of Health Grant No. R01HL142496 . Received: 23 November 2022 Accepted: 10 January 2023 Citation: Arrhythmia & Electrophysiology Review 2023;12:e06 . DOI: [https://doi.org/10.15420/aer.2022.43](https://doi.org/10.15420/aer.2022.43)[ ](https://doi.org/10.15420/aer.2022.43)[Correspondence:](Correspondence: Shane E Loeffler)[ Shane E Loeffler](Correspondence: Shane E Loeffler), [Alliance for Cardiovascular Diagnostic and Treatment Innovation](Alliance for Cardiovascular Diagnostic and Treatment Innovation) ([ADVANCE](ADVANCE)), Johns Hopkins University, 3400 North Charles St, Hackerman Hall Room 216, Baltimore, MD 21218, US. E: [sloeffl2@jhu.edu](sloeffl2@jhu.edu) Open Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly. Artificial intelligence (AI) has recently gained popularity, becoming a buzzword in the fields of science, technology, engineering and mathematics. AI aims to teach a computer how to replicate human intelligence to perform human tasks. Machine learning (ML) is a subset of AI that uses data to teach a machine how to imitate human behaviour. The overall goal of ML is to have a computer perform a task that a human performs, based on prior collected data, with high accuracy, quickly and automatically. The‘holy grail’ for ML is to have a computer outperform the prediction ability of humans. While the purpose of ML is clear, ML has no standard model and is almost exclusively task-specific. ML performs statistical operations on data sets to learn underlying patterns. The three most common ways a machine can learn are through supervised, unsupervised and reinforcement learning. Each way of learning is specific to the available data and/or the operators’ goals. Supervised learning uses labelled data to understand the correlation between inputs and outputs. Unsupervised learning finds hidden patterns within data sets. Reinforcement learning determines optimal decisions based on feedback from the interaction between an agent and an environment. In 2021, there were a total of 13,646, 7,774 and 11,567 publications specifically under the labels ‘supervised’,‘unsupervised’ and‘reinforcement’ learning, respectively, according to the Scopus database ([https://www.scopus.com](https

FAQs

What is the role of machine learning in electrophysiology?

Machine learning helps analyze diverse medical data to learn underlying patterns and supports improved decision-making in cardiac arrhythmia and electrophysiology research and clinical practice.

Which supervised machine learning models are discussed?

The primer covers least squares, support vector machines, neural networks, and random forest as common supervised learning approaches.

Which unsupervised methods are included?

Unsupervised learning methods highlighted include k-means and principal component analysis to discover hidden patterns within data.

Aug 2, 2026

Upload by Logic