A hybrid neuro-fuzzy system for the classification of normal, fusion and PVC cardiac beats in the MIT-BIH database

Hybrid architectures for intelligent systems is as a new direction in the artificial intelligence research which aims at the development of the next generation of intelligent systems. Current research interests in this field focus on integrating neural networks and fuzzy logic to better exploit their strengths as well as expand their application domain. This paper presents a hybrid neuro-fuzzy system (HNFS) for the classification of normal, fusion and PVC (premature ventricular contraction) cardiac beats of patient 208 in the MIT-BIH Arrhythmia Database. The HFNS has a hierarchical topology consisting of three kinds of building blocks-fuzzy neural networks (FNNs), neural networks (NN) and fuzzy systems (FS). The FNNs and NNs are based on the feedforward backpropagation model. The FS is based on classical methods that measure the QRS area, height and RR interval. The first level of the HFNS accomplishes the task of classification of QRS complexes from leads MLII and VI into different classes. In case of the classification output being ambiguous, the QRS pattern is passed to the second HFNS level for final decision-making. A small inference system is developed to support the decision when the classification obtained from MLII and VI differs. Different configurations of FNNs, NNs and FS for both the first and the second level have been examined and tested. Classification results based on sensitivity and predictivity rates are presented and compared to previous approaches.