A mixture model classifier and its application on the biomedical time series

This article presents a methodology based on the mixture model to classify the real biomedical time series. The mixture model is shown to be an efficient probabilistic density estimation scheme aimed at approximating the posterior probability distribution of a certain class of data. The approximation is conducted by employing a weighted mixture of a finite number of Gaussian kernels whose parameters and mixing coefficients are estimated iteratively through a maximum likelihood method. A database of the real electrocardiogram (ECG) time series of out-of-hospital cardiac arrest patients suffering ventricular fibrillation (VF) with known defibrillation outcomes was adopted to evaluate the performance of this model and confirm its efficiency compared with other classification methods.