Generalized Feedforward Neural Network based cardiac arrhythmia classification from ECG signal data

In this paper we proposed a classification system for cardiac arrhythmia from standard 12 lead ECG recordings data, using a Generalized Feedforward Neural Network (GFNN) classifier. The GFNN classifier is trained using static backpropagation algorithm to classify arrhythmia cases into normal and abnormal classes. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Therefore we have replaced these missing attributes by closest column value of the concern class. Networks models are trained and tested for UCI ECG arrhythmia data set. This data set is a good environment to test classifiers as it is incomplete and ambiguous bio-signal data collected from total 452 patient cases. The classification performance is evaluated using six measures; sensitivity, specificity, classification accuracy, mean squared error (MSE), receiver operating characteristics (ROC) and area under curve (AUC). The experimental results presented in this paper show that up to 82.35% testing classification accuracy can be obtained.