Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning.

Journal: Communications medicine
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Life-threatening arrhythmias (LTAs) are a leading cause of death worldwide. Enhancing LTA detection in wearable monitoring systems is of great importance. One of the main challenges in building robust LTA detection algorithms is the limited availability of labeled LTA data.

Authors

  • Giuliana Monachino
    Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
  • Beatrice Zanchi
    Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano 6900, Switzerland; Department of Quantitative Biomedicine, University of Zurich, Schmelzbergstrasse 26, Zurich 8091, Switzerland.
  • Michael Wand
    Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
  • Giulio Conte
    Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, Lugano 6900, Switzerland; Centre for Computational Medicine in Cardiology, Faculty of Informatics, Università della Svizzera Italiana, Via la Santa 1, Lugano 6900, Switzerland.
  • Athina Tzovara
    Institute of Computer Science, University of Bern, Bern, Switzerland.
  • Francesca Dalia Faraci
    Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.

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