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In this paper, a new method of arrhythmia classification is proposed. At first we extract twenty two features from electrocardiogram signal. We propose a novel classification system based on genetic algorithm to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design by searching for the best value of the parameters that tune its...
This paper aims at developing a wearable system able to recognize the most significant cardiac arrhythmias through an efficient algorithm, in terms of low computational cost and memory usage, implementable in a portable, real-rime hardware. In addition, it must respect the specifications of good specificity and sensitivity, in order to permit a positive clinical validation. The hardware is constituted...
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