Generalization of Rule-Based Decision Tree to Fuzzy Intervals for ECG-Beat Clustering

In this paper, we compare two approaches to clustering and diagnosis of the ECG heart beats. In the first approach, the rule-based decision tree method is presented; in this method the decision rules are based on classical intervals. The second approach is based on fuzzification of the intervals; this accords with the situation when the knowledge described by books and cardiologists is vague or unclear. We discuss the way how the results of the fuzzy and the classical approaches can be compared. We choose the sensitivity and specificity as they are a well established measures in the field of the clinical medicine. We define a generalization of the sensitivity and specificity for fuzzy clusters in order to prove correctness of our presented fuzzy approach.