Neuro-Fuzzy Model for Arrhythmia Diagnostic System

Abstract

Сardiovascular diseases are one of the leading causes of death worldwide. People suffering from or at high risk of such diseases need constant supervision, early diagnosis and timely assistance. It is shown that the achievement of high accuracy performance in real-time arrhythmias recognition is associated with significant hardware costs. Detection accuracy of arrhythmias recognition does not exceed 80%. An approach, which is offered to solve the problem of high-precision arrhythmia diagnosis on the basis of electrocardiosignal is based on the data mining methods. Application of such methods is necessary for processing large amounts of data with complex structure of the features. Determination of the arrhythmia type with the use of fuzzy inference tools needs to specify the technique of the original data preprocessing. Feature selection, formalization and coding is considered in this paper. The issue of the knowledge base constructioncoding, generation and selection of the features (database) as well as the construction of the rules base ¬ as the part of the neuro-fuzzy diagnostic system is also considered. The research goal is to improve the intelligent systems of arrhythmia diagnostics on the basis of neural network classifiers by developing a solution explanation subsystem based on neuro-fuzzy models.