COMPUTER-AIDED DIAGNOSIS OF THE PAROXYSMAL ATRIAL FIBRILLATION: A FUZZY-EVOLUTIONARY APPROACH

Abstract

This paper presents an integrated fuzzy-evolutionary methodology to address computer-aided diagnosis in medical applications by using features extracted from biosignal processing. In the proposed methodology, a deterministic crowding genetic optimizer designed to provide high-diversity solutions is used for weighted feature selection, and the diagnostic decision is made by a binary k-nearest neighbor classifier. Weight vector solutions resulting from the optimization stage are processed by a fuzzy rules generator to retrieve a fuzzy model giving friendly information to the medical specialist about the role of the different features in the diagnosis. This allows the design of efficient diagnosis protocols. The overall diagnostic methodology is applied to Paroxysmal Atrial Fibrillation detection based on analysis of nonfibrillation ECGs, obtaining a fuzzy model consistent with previous work in this field.