Fractal QRS-complexes pattern recognition for imperative cardiac arrhythmias

Abstract

This paper proposes using fractal QRS-complexes pattern recognition for imperative cardiac arrhythmias. A typical electrocardiogram (ECG) signal is comprised of P-wave, QRS-complex, and T-wave. Fractal dimension transformation (FDT) is employed to adjoin the QRS-complex from time-domain ECG signals, including the fractal features of supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. FDT with fractal dimension (FD) is addressed for constructing various symptomatic features, and can produce family functions and enhance features, making the difference between healthy and unhealthy subjects more significant. The probabilistic neural network (PNN) is proposed for recognizing the states of cardiac physiologic function. The proposed method is tested using the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database. Compared with other methods, the numerical experiments demonstrate greater efficiency and higher accuracy in recognizing ECG signals.