Arrhythmia disease classification using a higher-order neural unit

This paper presents a quadratic neural unit with error backpropagation learning algorithm to classify electrocardiogram arrhythmia disease. The electrocardiogram arrhythmia classification scheme consists of data acquisition, feature extraction, feature reduction, and a quadratic neural unit classifier to discriminate three different types of arrhythmia. A total of 44 records were obtained from MIT-BIH arrhythmia database to test the efficiency of arrhythmia disease classification method, the obtained results were a specificity of 97.60 % and a sensitivity of 97.05 %. The best accuracy classification rate obtained using the presented approach has been of 98.16 %.