Residual-attention deep learning model for atrial fibrillation detection from Holter recordings.

Journal: Journal of electrocardiology
PMID: 39827741

Abstract

BACKGROUND: Detecting subtle patterns of atrial fibrillation (AF) and irregularities in Holter recordings is intricate and unscalable if done manually. Artificial intelligence-based techniques can be beneficial. In fact, with the rapid advancement of AI, deep learning (DL) demonstrated the capability to identify AF from ECGs with significant performance. However, further development and validation on larger cohorts is still needed.

Authors

  • Md Moklesur Rahman
    Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy.
  • Massimo Walter Rivolta
    Dipartimento di Informatica, Università degli Studi di Milano, Crema, Italy.
  • Martino Vaglio
    AMPS-LLC, New York, NY, USA. Electronic address: vaglio@amps-llc.com.
  • Pierre Maison-Blanche
    Department of Cardiology, Hôpital Bichat, Paris, France.
  • Fabio Badilini
    Department of Physiological Nursing, University of California, San Francisco, CA, USA.
  • Roberto Sassi
    Dipartimento di Informatica, Università degli Studi di Milano, Crema, Italy.

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