Morphological Arrhythmia Classification Based on Inter-Patient and Two Leads ECG Using Machine Learning

Abstract

Arrhythmia is a heart disorder in which the heart beats irregularly. Electrocardiogram (ECG) has been widely used as a tool for detecting arrhythmias. However, the interpretation of ECG recordings is still tedious, time-consuming, and a difficult task since it needs beat-by-beat manual examination. In arrhythmia classification studies, the inter-patient method is required to obtain unbiased results and has been used in most studies to detect arrhythmias specifically in large classes, such as normal, ventricular, atrial, and fusion beats. However, this method is still limited in detecting more specific arrhythmias, particularly morphological arrhythmias, such as bundle branch block and premature beats. In addition, the methods usually only use one ECG lead. To overcome this limitation, in this work, we propose inter-patient-based arrhythmias classification using combined two ECG leads automatically by employing machine learning methods, specifically ensemble learning. The classification is intended to detect 5 classes of morphological arrhythmia which are Left Bundle Branch Block (LBBB), Left Right Branch Block (RBBB), Premature Ventricular Contractions (PVC), Premature Atrial Contractions (PAC), and Normal. This work also details the method covering data set preparation, algorithm and design parameters exploration for pre-processing ECG signals, features extraction and selection, and hyper parameter tuning for employed machine learning methods. Evaluation results show that the proposed machine learning method, which is Ensemble Learning achieves performance improvements, compared to similar other works with average results of accuracy 87%, recall 87.4%, precision 88.4%, and F1-score 87%. These results correspond to performance improvements compare to state of the art methods for about 12% of accuracy, 28% of recall and 24% of precision, respectively. Furthermore, the proposed work potentially can be employed in real-world clinical practice, specifically as fast yet accurate aided tool for cardiac events diagnostic.

Keywords

• ECG
• morphological arrhythmia
• arrhythmia classification
• inter-patient
• machine learning