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Big Data in electrophysiology

Big data in der Elektrophysiologie

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An Erratum to this article was published on 30 March 2022

This article has been updated

Abstract

The quantity of data produced and captured in medicine today is unprecedented. Technological improvements and automation have expanded the traditional statistical methods and enabled the analysis of Big Data. This has permitted the discovery of new associations with a granularity that was previously hidden to human eyes. In the first part of this review, the authors would like to provide an overview of basic Machine Learning (ML) principles and techniques in order to better understand their application in recent publications about cardiac arrhythmias. In the second part, ML-enabled advances in disease detection and diagnosis, outcome prediction, and novel disease characterization in topics like electrocardiography, atrial fibrillation, ventricular arrhythmias, and cardiac devices are presented. Finally, the limitations and challenges of applying ML in clinical practice, such as validation, replication, generalizability, and regulatory issues, are discussed. More carefully designed studies and collaborations are needed for ML to become feasible, trustworthy, accurate, and reproducible and to reach its full potential for patient-oriented precision medicine.

Zusammenfassung

Die Menge generierter und erfasster Daten in der Medizin war noch nie so hoch wie heute. Der technologische Fortschritt und die Automatisierung haben die traditionellen statistischen Methoden erweitert und die Analyse von Big Data ermöglicht. Dies hat die Entdeckung neuer Assoziationen mit einer Granularität ermöglicht, die dem Menschen zuvor verborgen war. Der erste Teil dieser Übersichtsarbeit bietet einen Überblick der grundlegenden Prinzipien und Techniken des maschinellen Lernens (ML), um deren Anwendung in den neuesten Veröffentlichungen über Herzrhythmusstörungen besser zu verstehen. Der zweite Teil schildert ML-gestützte Fortschritte bei der Erkennung und Diagnose von Erkrankungen, bei der Vorhersage von Ergebnissen und bei der neuartigen Charakterisierung von Erkrankungen im Zusammenhang mit Themen wie Elektrokardiographie, Vorhofflimmern, ventrikulären Arrhythmien und kardialen Geräten. Schließlich werden die Grenzen und Herausforderungen der Anwendung von ML in der klinischen Praxis diskutiert, beispielsweise in Bezug auf Validierung, Replikation, Verallgemeinerbarkeit und regulatorische Fragen. Weitere sorgfältig konzipierte Studien und Kooperationen sind erforderlich, damit ML umsetzbar, verlässlich, präzise und reproduzierbar wird und um ihr volles Potenzial für eine patientenorientierte Präzisionsmedizin ausschöpfen zu können.

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Authors and Affiliations

  1. Department of Electrophysiology, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany

    Sotirios Nedios MD, PhD, Christopher Kowalewski, Andreas Bollmann, Gerhard Hindricks & Nikolaos Dagres

  2. Department of Cardiology and Rhythmology, Ev. Krankenhaus Hagen, Hagen, Germany

    Konstantinos Iliodromitis & Harilaos Bogossian

  3. Department of Cardiology, University Witten/Herdecke, Witten, Germany

    Konstantinos Iliodromitis & Harilaos Bogossian

  4. Rhythmologie, Herzzentrum Leipzig, Universität Leipzig, Strümpellstr. 39, 04289, Leipzig, Germany

    Sotirios Nedios MD, PhD

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Correspondence to Sotirios Nedios MD, PhD.

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S. Nedios, K. Iliodromitis, C. Kowalewski, A. Bollmann, G. Hindricks, N. Dagres, and H. Bogossian declare that they have no competing interests.

For this article no studies with human participants or animals were performed by any of the authors. All studies performed were in accordance with the ethical standards indicated in each case.

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Nedios, S., Iliodromitis, K., Kowalewski, C. et al. Big Data in electrophysiology. Herzschr Elektrophys 33, 26–33 (2022). https://doi.org/10.1007/s00399-022-00837-z

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