Integration of multiple neural classifiers for heart beat recognition

Purpose – This paper presents new approach to the integration of neural classifiers. Typically only the best trained network is chosen, while the rest is discarded. However, combining the trained networks helps to integrate the knowledge acquired by the component classifiers and in this way improves the accuracy of the final classification. The aim of the research is to develop and compare the methods of combining neural classifiers of the heart beat recognition.Design/methodology/approach – Two methods of integration of the results of individual classifiers are proposed. One is based on the statistical reliability of post‐processing performance on the trained data and the second uses the least mean square method in adjusting the weights of the weighted voting integrating network.Findings – The experimental results of the recognition of six types of arrhythmias and normal sinus rhythm have shown that the performance of individual classifiers could be improved significantly by the integration proposed in t...