ABSTRACT: Irregular heartbeats due to abnormal electrical heart activity are symptom of Cardiovascular disease (CVD), it is a source of stroke, blood clots, heart failure and other heart-related complications. Most of the developed Electrocardiogram (ECG) based automatic cardiac arrhythmia detection systems require the availability of a large data with all arrhythmias for the training process, and cannot be updated without adequate data and cost. Therefore, this paper aims to develop a continual learning method by introducing incrementally new arrhythmias to a deep learning CVD detection system already trained with old ones. However, due to the catastrophic forgetting phenomenon, the pre-trained model loses its pre-acquired knowledge and performs poorly, if it is subject to a new training process.....
Keywords: Electrocardiogram classification; continual learning; catastrophic forgetting; generative model, contrastive learning.
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