Antidictionary-Based Cardiac Arrhythmia Classification | Duforest, Julien
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Antidictionary-Based Cardiac Arrhythmia Classification

Archive ouverte : Communication dans un congrès

Duforest, Julien | Larras, Benoit | John, Deepu | Märtens, Olev | Frappé, Antoine

Edité par HAL CCSD

International audience. 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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