Classification of ECG patterns for diagnostic purposes by means of Neural Networks and Support Vector Machines
作者： Silvia Conforto , Antonino Laudani , Fabio Oliva , Francesco Riganti Fulginei , Maurizio Schmid
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摘要: This paper presents an application of Neural Networks (NNs) and Support Vector Machines (SVMs) for the detection classification heartbeats in electrocardiogram (ECG) signals. The preprocessing algorithm beats is based on well-known Pan-Tompkins' algorithm. proposed approach robust to different types noise shows good performances both beat analysis QRS morphology extraction. method combination with radial basis function SVM adaptive NNs, brought remarkable results kind cardiac arrhythmia as shown by suitable numerical simulations presented at end paper.
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