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Review
. 2020 Nov;9(3):146-154.
doi: 10.15420/aer.2020.26.

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

Rutger R van de Leur  1 , Machteld J Boonstra  1 , Ayoub Bagheri  1   2 , Rob W Roudijk  1   3 , Arjan Sammani  1 , Karim Taha  1   3 , Pieter Afm Doevendans  1   3   4 , Pim van der Harst  1 , Peter M van Dam  1 , Rutger J Hassink  1 , René van Es  1 , Folkert W Asselbergs  1   5   6
Affiliations
Review

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

Rutger R van de Leur et al. Arrhythm Electrophysiol Rev. 2020 Nov.

Abstract

The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.

Keywords: Artificial intelligence; ECG; big data; cardiology; deep learning; electrophysiology; neural networks.

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Conflict of interest statement

Disclosure: The authors have no conflicts of interest to declare. Funding: This study was partly supported by The Netherlands Organisation for Health Research and Development (ZonMw, grant number 104021004) and partly supported by the Netherlands Cardiovascular Research Initiative, an initiative with support of the Dutch Heart Foundation (grant numbers CVON2015-12 eDETECT and QRS-VISION 2018B007). FWA is supported by UCL Hospitals NIHR Biomedical Research Center. AS is supported by the UMC Utrecht Alexandre Suerman MD/PhD programme.

Figures

Figure 1:
Figure 1:. Traditional Machine Learning and Deep Learning with a Schematic Representation of Fitting a Model to a Data Set
Figure 2:
Figure 2:. The Effect of Shifting Precordial Electrodes Upward or Downward
Figure 3:
Figure 3:. Standardised Clinical Visualised Signals
Figure 4:
Figure 4:. Important Regions for the Deep Neural Network to Predict Whether an ECG is Normal, Abnormal or Acute
See this image and copyright information in PMC

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