High-resolution detection of sustained ventricular and supraventricular tachycardia through FPGA-based fuzzy processing of ECG signal.

The paper presents a field-programmable gate array (FPGA)-based fast processing system with 12-channel high-resolution (24 bits) front-end for ECG signal processing. The implemented high-resolution data conversion makes the system suitable for recording of late potentials of the QRS complex in patients prone to sustained ventricular tachycardia. The system accepts ECG signals through 12 channels and then filtered to minimize baseline wander and power-line interference. The filter outputs are connected to 12 delta-sigma ADCs. The whole ADCs work synchronously at 8 kHz sampling frequency, and their output data are transferred to an FPGA that computes online on the digitized sample values in real time and ascertains whether the patient under study suffers from ventricular tachycardia or not. In order to ascertain the QRS complex accurately in the noisy ECG signal, fuzzy entropy of the sample values has been computed and provided as an input to inverse multiquadratic radial basis function neural network. Using the standard CSE ECG database, the algorithm performed highly effectively. The performance of the algorithm in respect of QRS detection with sensitivity of 99.83 % and accuracy of 99.7 % is achieved when tested using single-channel ECG with entropy criteria. The performance of the QRS detection system has been compared and found to be better than most of the QRS detection systems available in the literature. Using the system, 200 patients have been diagnosed with an accuracy of 99 %.