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Cardiac Arrhythmia Classification using Antidictionaries
  • +2
  • Julien Duforest,
  • Benoît Larras,
  • Olev Martens,
  • Deepu John,
  • Antoine Frappé
Julien Duforest

Corresponding Author:[email protected]

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Benoît Larras
Olev Martens
Deepu John
Antoine Frappé


Cardiovascular diseases are the primary cause of death worldwide. At-home monitoring systems can be used to prevent them. For long-term monitoring, these systems require high detection accuracy and low power consumption. This paper introduces a new cardiac arrhythmia classification scheme that employs antidictionaries for identifying abnormal patterns in electrocardiograms. This system enables the training to be performed without data augmentation and a smaller dataset for training compared to existing literature. The proposed method is also compatible with an eventdriven implementation, that offers great potential for ultra-low power devices. The reported average detection accuracy reaches 97.97%. The system is demonstrated through simulations and implementation on an FPGA platform.
23 May 2024Submitted to TechRxiv
30 May 2024Published in TechRxiv