Diagnostic study on arrhythmia cordis based on particle swarm optimization-based support vector machine

Diagnosis of arrhythmia cordis is very significant to ensure human health and save human lives. Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which can solve the classification problem with small sampling, non-linear and high dimension. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behavior of bird flocking or fish schooling. The optimization method not only has strong global search capability, but also is very easy to implement. Thus, in the study, the proposed PSO-SVM model is applied to diagnosis of arrhythmia cordis, in which PSO is used to determine free parameters of support vector machine. The experimental data from MIT-BIH ECG database are used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve higher diagnostic accuracy than artificial neural network in diagnosis of arrhythmia cordis.