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Communication Dans Un Congrès Année : 2022

Antidictionary-Based Cardiac Arrhythmia Classification

Résumé

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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Dates et versions

hal-03760895 , version 1 (25-08-2022)

Identifiants

  • HAL Id : hal-03760895 , version 1

Citer

Julien Duforest, Benoit Larras, Deepu John, Olev Märtens, Antoine Frappé. Antidictionary-Based Cardiac Arrhythmia Classification. 16ème Colloque National du GDR SOC2, Jun 2022, Strasbourg, France. ⟨hal-03760895⟩
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