Published January 10, 2022 | Version v1
Conference paper Open

Benchmark of deep learning algorithms for the automatic screening in electrocardiograms transmitted by implantable cardiac devices

  • 1. Electrophysiology and Heart Modeling Institute (IHU-Lyric), Pessac, France, ; Institute of Mathematics, University of Bordeaux, Talence, France; INRIA Bordeaux Sud-ouest, CARMEN Team, Talence, France
  • 2. Electrophysiology and Heart Modeling Institute (IHU-Lyric), Pessac, France
  • 3. Universitat Pompeu Fabra, Barcelona, Spain

Description

The objective of this work was to benchmark different
deep learning architectures for noise detection against
cardiac arrhythmia episodes recorded by pacemakers and
implantable cardioverter-defibrillators (PM/ICDs) and
transmitted for remote monitoring. Up to now, most signal
processing from ICD data has been based on classical
hand-crafted algorithms, not AI or DL-based ones.
The database consist of PM/ICD data from 805 patients
representing a total of 10471 recordings from three different
channels: the right ventricular (RV), the right atria
(RA), and the shock channel.
Four deep learning approaches were trained and optimized
to classify PM/ICDs’ records as actual ventricular
signal vs noise episodes. We evaluated the performance of
the different models using the F2 score.
Results show that the use of 2D representations of 1D
signals led to better performances than the direct use of
1D signals, suggesting that the detection of noise takes
advantage of a spectral decomposition of the signal, which
remains to be confirmed in other contexts.
This study proposes deep learning approaches for the
analysis of remote monitoring recordings from PM/ICDs.
The detection of noise allows efficient management of this
large daily flow of data.

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Additional details

Funding

European Commission
PersonalizeAF – Personalized Therapies for Atrial Fibrillation. A Translational Approach 860974