ECG Signal Diagnoses Using Intelligent Systems Based on FPGA

Abstract

This paper presents the use of Particle Swarm Optimization (PSO), neural networks with the most promising supervised learning algorithms for automatic detection of cardiac arrhythmias based on analysis of the Electrocardiogram (ECG). Artificial Neural Network (ANN) has three layers with ten nodes in the input layer, five nodes in the hidden layer and five nodes in the output layer, which is trained using the PSO algorithm. The trained network was able to classify the ECG signal in normal signal, atrial flutter, ventricular tachycardia, sever conducting tissue and wandering a trial pacemaker. Field Programmable Gate Arrays (FPGAs) have been used to implement ANN trained by the supervised learning algorithms and PSO, because of their speed benefits, as well as the re-programmability of the FPGAs which can support the reconfiguration necessary to program a neural network. A VHDL Design of ANN platform is proposed to evolve the architecture ANN circuits using FPGA-Spartan 6 Evaluation board. The VHDL design platform creates ANN design files using WebPACKTM ISE 13.3 program. All the algorithms used to train the ANN showed high effectiveness with 100% classification.