A New Methodology for Classifying QRS Morphology in ECG Signals

The electrocardiogram (ECG) is a non-invasive method to detect cardiovascular diseases (CVD), the most common cause of death in the world. The recognition of heartbeat morphologies present in the ECG signal is an effective way to detect CVDs prematurely. Many approaches were developed for this purpose, such as the use of Wavelets, High Order Statistics (HOS), Local Binary Patterns (LBP), Random Projection, Fiducial points, and Hermite Polynomials. Unfortunately, most parts of these approaches suffer from the high variability of ECG signal features and conditions. Also, it is common to use more than one of them simultaneously, which makes it hard to infer the contributions of each one. This work presents a new robust methodology to extract features for heartbeat morphology classification. Moreover, we introduce new labels for a small set of morphologies present in MIT-BIH Arrhythmia database, taking into account only the QRS complex (the 3 more representative waves of a heartbeat) instead of the whole heartbeat. We evaluate each approach in isolation and the results show that our method outperforms other well-known strategies.