A comparison of Estimation Techniques for electrocardiogram classification

Electrocardiograms are one of the most important bio-signals used to diagnose heart conditions such as arrhythmia. In this paper we interpret the rhythm and identify the potential disruptions using computer assisted methods of interpretation. Moreover, Hannan-Rissanen algorithm (HRA) and Maximum Likelihood (MLE) are used to estimate the coefficients of the Generalized Autoregressive Moving Average (GARMA). We classified ECG signals into 6 classes (normal and 5 types of arrhythmia) using neural networks approach. We also compared the results of HRA and MLE estimations methods and found that HRA performs better than MLE. The overall detection accuracy using HRA is 81.7% and 67.3% for MLE.