GA-NN approach for ECG feature selection in rule based arrhythmia classification

Computer-aided ECG analysis is very important for early diagnosis of heart diseases. Automated ECG analysis integrated with experts' opinions may provide more accurate and reliable results for detection of arrhythmia. In this study, a novel genetic algorithm-neural network (GA-NN) approach is proposed as a classifier, and compared with other classification methods. The GA-NN approach was shown to perform better than alternative approaches (e.g. k-nn, SVM, naïve Bayes, Bayesian networks) on the UCI Arrythmia and the novel TEPAS ECG datasets, where the GA resulted in a feature reduction of 95%. Based on the selected features, several rule extraction algorithms are applied to allow the interpretation of the classification results by the experts. In this application, the accuracy and interpretability of results are more important than processing speed. The results show that neural network based approaches benefit greatly from dimensionality reduction, and by employing GA, we can train the NN reliably.