Detection of PVC in ECG signals using fractional linear prediction

Abstract

Abstract In this paper, we propose a modeling technique for the QRS complex based on the fractional linear prediction (FLP). As a result of FLP modeling, each QRS complex is represented by a vector of three coefficients. The FLP modeling evaluation is achieved in two steps. In the first step, the ability of the FLP coefficients to efficiently model QRS complex waves is assessed by comparison with the Linear Prediction (LP) coefficients through the signal-to-error (SER) values evaluated between the original waves and predicted ones. In the second step, the performance of several classifiers is used to evaluate the effectiveness and robustness of FLP modeling over LP modeling. Classifiers are fed by the three estimated coefficients in order to discriminate premature ventricular contraction (PVC) arrhythmia from normal beats. The study has successfully demonstrated that FLP modeling can be an alternative to the LP modeling in the field of QRS complex modeling.