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Antidictionary-Based Cardiac Arrhythmia Classification

Authors
  • Duforest, Julien
  • Larras, Benoit
  • John, Deepu
  • Märtens, Olev
  • Frappé, Antoine
Publication Date
Jun 27, 2022
Source
HAL-Descartes
Keywords
Language
English
License
Unknown
External links

Abstract

Cardiovascular diseases can be detected early by analyzing the electrocardiogram of a patient using wearable systems. In the context of smart sensors, detecting arrhythmias with good accuracy and ultra-low power consumption is required for long-term monitoring. This paper presents a novel cardiac arrhythmia classification method based on antidictionaries. The features are sequences of consecutive slopes generated from the input signal's event-driven processing. The proposed system shows an average detection accuracy of 98% while offering an ultra-low complexity. This antidictionary-based method is also particularly suited to imbalanced datasets since the antidictionaries are created exclusively from heartbeats classified as normal beats.

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