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Computer-aided diagnosis of Electrocardiographic (ECG) signal consists of processing the signal to extract parameters in time, frequency, scale, etc. Then distinct parameters that provide significant differences between normal and arrhythmic ECG beats are selected as a feature vector. This feature vector is fed to either a supervised or an unsupervised classifier for detection. Seeking significant...
In this paper, a method for the detection of premature ventricular contraction (PVC) beat in electrocardiogram (ECG) signal is presented. The method adopts the minimum distance (MD) and the linear discriminant analysis (LDA) in a proposed feature space for classification. The features are extracted by continuous wavelet-transform of the ECG beat signal followed by the Teager-Kiaser Energy (TKE) operator...
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