Abstract
Decision support systems can seriously help medical doctors in the diagnosis of different diseases, especially in complicated cases. This article is devoted to recognizing and diagnosing heart disease based on automatic computer processing of the electrocardiograms (ECG) of patients. In the general case, the change of the ECG parameters can be presented as a random sequence of the signals under processing. Developing new computational methods for such signal processing is an important research problem in creating efficient medical decision support systems. Authors consider the possibility of increasing the diagnostic accuracy of cardiovascular diseases by implementing of the new proposed computational method of information processing. This method is based on the generalized nonlinear canonical decomposition of a random sequence of the change of cardiogram parameters. The use of a nonlinear canonical model makes it possible to significantly simplify the maximum likelihood criterion for classifying diseases. This simplification is provided by the transition from a multi-dimensional distribution density of cardiogram parameters to a product of one-dimensional distribution densities of independent random coefficients of a nonlinear canonical decomposition. The absence of any restrictions on the class of random sequences under study makes it possible to achieve maximum accuracy in diagnosing cardiovascular diseases. Functional diagrams for implementing the proposed method reflecting the features of its application are presented. The quantitative parameters of the core of the computational diagnostic procedure can be determined in advance based on the preliminary statistical data of the ECGs for different heart diseases. That is why the developed method is quite simple in terms of computation (computing complexity, accuracy, computing time, etc.) and can be implemented in medical computer decision systems for monitoring cardiovascular diseases and for their diagnosis in real time. The results of the numerical experiment confirm the high accuracy of the developed method for classifying cardiovascular diseases.