Novel Bayesian classifier discriminant function optimization strategies for arrhythmia classification

Cardiac arrhythmia is considered to be one of the most critical addressed problems leading to death. Thus, Computer Aided Diagnosis (CAD) systems are essential for early arrhythmia detection and diagnosis. In this paper, we propose a classification system for arrhythmia diagnosis based on Bayesian classifier. The system employs one-versus-one approach, used in the classification methodology of several multi-class classifiers such as the support Vector Machine (SVM). The proposed idea is mainly based on introducing new algorithms for optimizing the classifier's parameters in order to improve the overall classification system performance, using the Space Search (SS) and the One-versus-One Error Minimization (OOEM) approaches. The SS approach boosted system accuracy over the conventional Bayes (CB) by 1.14%, 2.5% and 3.33% for 3, 5 and 6-classes arrhythmia problems respectively while OOEM showed less superiority than SS as it boosted accuracy by 0.7% and 2.44% for the 5 and 6-classes problems respectively and attained same accuracy achieved by CB for the 3-class problem. The learning and testing times were calculated for both approaches. The results show that the SS based system offers the best possible accuracy, however it has the longest learning time.