Saeed Karimifard

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This paper presents the results of morphological heart arrhythmia detection based on features of electrocardiography, ECG, signal. These signals are obtained from MIT/BIH arrhythmia database. The ECG beats were first modeled using Hermitian basis functions, (HBF). In this step, the width parameter, sigma, of HBF was optimized to minimize the model error.(More)
This paper presents the results of morphological heart arrhythmia detection based on parameters which are obtained from modeling of the cumulants of the electrocardiography, ECG, signals. Cumulants possess many properties that make them effective tools to describe morphological variations of non-stationary signals. Among these properties, the two most(More)
In this paper, a new piecewise modeling for approximation of ECG signal is presented. Most of the modeling methods are focused to obtain the best approximation of the entire ECG signal. The proposed method exploits the importance of different intervals of ECG signals, in particular QRS complex, by performing a segmented based modeling using Hermitian basis(More)
BACKGROUND Electrocardiography (ECG) signal is a primary criterion for medical practitioners to diagnose heart diseases. The development of a reliable, accurate, non-invasive and robust method for arrhythmia detection could assists cardiologists in the study of patients with heart diseases. This paper provides a method for morphological heart arrhythmia(More)
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