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Communication Dans Un Congrès Année : 2022

Antidictionary-Based Cardiac Arrhythmia Classification for Smart ECG Sensors

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 that are generated from event-driven processing of the input signal. 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-03701059 , version 1 (26-08-2022)

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Citer

Julien Duforest, Benoit Larras, Antoine Frappé, Chacko John Deepu, Olev Märtens. Antidictionary-Based Cardiac Arrhythmia Classification for Smart ECG Sensors. 2022 IEEE International Symposium on Circuits and Systems (ISCAS), May 2022, Austin, TX, United States. pp.414-418, ⟨10.1109/ISCAS48785.2022.9937853⟩. ⟨hal-03701059⟩
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