Skip to Main Content
In general, the detection of cardiovascular disease is performed by ECG, Electrocardiogram, to dynamically monitor and analyze the disease status. Additionally, ECG is also used to diagnose the latent disease to proceed with a further treatment. Therefore, it is very important to give a reasonable judgement from the ECG diagnosis information. In this paper, a linearly modeling system is presented to characterize both the measured ECG data and Blood Pressure Wave (BPW) information. After that, the PSO algorithm, Particle Swarm Optimization, is proposed to classify the frequency responses which are derived from the linear modeling system. From the simulation result, the successful hit rate for identifying the cardiovascular samples can reach to 80%. Meanwhile, the PSO training iterations can converge under an acceptable requirement.