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A novel method is proposed in this paper for the feature extraction of electrocardiogram (ECG). The shape characteristic of the QRS complex has been a diagnostic criterion of cardiac arrhythmia. In other words, geometric property of the QRS complex is a very important kind of feature. Different with other feature extraction algorithms, the proposed method utilizes geometric algebra (GA) to extract...
The aim of this article is to propose an intelligent electrocardiogram classifier. The classifier is similar to probabilistic neural networks. In these networks, a user needs to set some parameters optionally. Improper selections may decrease the performance drastically. The proposed method needs no optional parameter settings and all required parameters are extracted from the statistics of the input...
A novel method is proposed in this paper for the feature extraction of electrocardiogram (ECG). Different with other algorithms, the proposed method utilizes independent component analysis (ICA) and wavelet transform to get an ensemble feature composed of ICA-based features and the QRS complex width feature. The QRS complex is the most characteristic waveform of an ECG signal and its width has been...
We propose a novel approach to the automated discrimination of normal and ventricular arrhythmic beats. The method employs Gaussian Processes, a non-parametric Bayesian technique which is equivalent to a neural network with infinite hidden nodes. The method is shown to perform competitively with other approaches on the MIT-BIH Arrhythmia Database. Furthermore, its probabilistic nature allows to obtain...
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