Use of cluster analysis technique for computerized recognition and prognosis of myocardial vulnerability

Variability of factors exerting influence on arrhythmia origination leads to the appearance of polymodal distribution in sample space. Therefore, for the approximation of feature distribution, distribution mixture is used. To estimate the number of mixture components and to determine its parameters, cluster algorithm is used. The basic task of the algorithm is to identify the accumulation of vectors in the parallelepiped of their distribution. The accumulations of points are determined by testing statistical hypothesis of uniformity. On the basis of accumulations, clusters are formed and the parameters of normal mixtures of classes are estimated. Analysis of error matrix for recognition of mixture, enable to establish the parameters of the decision rule. The above algorithm was applied for recognition and prognosis of vulnerability of reentry and focal source in experiments on the right rabbit's atrium by using the electrophysiological parameters. We studied 30 cases of reentry, 36 cases of focal sources and 165-arrhythmia-free cases. As a result, we established the 7-class normal mixture which enabled a more effective (96.4%) recognition of the vulnerability types and 89.9% prognosis by features: increase in latency (theta), width of the interval of latency distribution, ratio theta/R, where R-refractory period.