FPGA implementation of fractal patterns classifier for multiple cardiac arrhythmias detection

Abstract

This paper proposes the fractal patterns classifier for multiple cardiac arrhythmias on field-programmable gate array (FPGA) device. Fractal dimension transfor- mation (FDT) is employed to adjoin the fractal fea- tures of QRS-complex, including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricu- lar ectopic beat. FDT with fractal dimension (FD) is addressed for constructing various symptomatic pat- terns, which can produce family functions and enhance features, making clear differences between normal and unhealthy subjects. The probabilistic neural network (PNN) is proposed for recognizing multiple cardiac arrhythmias. Numerical experiments verify the effi- ciency and higher accuracy with the software simula- tion in order to formulate the mathematical model logical circuits. FDT results in data self-similarity for the same arrhythmia category, the number of dataset requirement and PNN architecture can be reduced. Its simplified model can be easily embedded in the FPGA chip. The prototype classifier is tested using the MIT-BIH arrhythmia database, and the tests reveal its practicality for monitoring ECG signals.