Scientific article
Open access

A Lightweight and Interpretable Model to Classify Bundle Branch Blocks from ECG Signals

Published inStudies in health technology and informatics, vol. 294, no. Challenges of Trustable AI and Added-Value on Health, p. 43-47
Publication date2022-05-25

Automatic classification of ECG signals has been a longtime research area with large progress having been made recently. However these advances have been achieved with increasingly complex models at the expense of model’s interpretability. In this research, a new model based on multivariate autoregressive model (MAR) coefficients combined with a tree-based model to classify bundle branch blocks is proposed. The advantage of the presented approach is to build a lightweight model which combined with post-hoc interpretability can bring new insights into important cross-lead dependencies which are indicative of the diseases of interest.

  • ECG automatic classification
  • Interpretability
  • Lightweight Model
  • Algorithms
  • Bundle-Branch Block / diagnosis
  • Electrocardiography
  • Humans
Citation (ISO format)
TURBÉ, Hugues et al. A Lightweight and Interpretable Model to Classify Bundle Branch Blocks from ECG Signals. In: Studies in health technology and informatics, 2022, vol. 294, p. 43–47. doi: 10.3233/SHTI220393
Main files (1)
Article (Published version)
ISSN of the journal0926-9630

Technical informations

Creation06/07/2022 2:23:00 PM
First validation06/07/2022 2:23:00 PM
Update time03/16/2023 8:43:37 AM
Status update03/16/2023 8:43:36 AM
Last indexation05/06/2024 11:55:04 AM
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