An FPGA based approach for nonlinear characterization of Electrocardiographic data

An FPGA based system has been developed in order to characterize, in real time, Electrocardiographic (ECG) signals. These signals are analyzed by using nonlinear data analysis to extract the d_∞ parameter, characterizing their asymptotic chaotic behaviour. The FPGA implemented algorithm has been applied on a statistically significant number of ECG signals taken from the MIT-BIH database and including normal subjects and subjects affected by arrhythmia and ventricular arrhythmia. The real time feature of the proposed approach allows a clinical applications of such methodology in the early diagnosis of cardiac pathologies.