The nature of statistical learning theory
The nature of statistical learning theory
An automatic analysis method for detecting and eliminating ECG artifacts in EEG
Computers in Biology and Medicine
Multiclass cell detection in bright field images of cell mixtures with ECOC probability estimation
Image and Vision Computing
An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
Prediction of aeration efficiency on stepped cascades by using least square support vector machines
Expert Systems with Applications: An International Journal
Identifying core sets of discriminatory features using particle swarm optimization
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.