An Optimal Set of Features for Multi-Class Heart Beat Abnormality Classification

The analysis of the ECG signal provides important information useful for heart disease diagnosis. Moreover, it can also help in identifying other problems such as neonatal seizures. Considering the nature of ECG patterns, several features can be extracted and used for the purpose of heartbeat and rhythm classification. In this paper, we discuss a set of 13 ECG geometric features for the purpose of identifying five abnormal types of heart beats. The proposed algorithm for feature extraction is based on the Pan-Tompkins QRS model. The MIT-BIH arrhythmia database is used in this study to test the performance of the proposed algorithm. The results show that different types of ECG based features are optimal for different types of heartbeat abnormalities. More importantly, we show that using the developed 13 features, we can identify at least 5 types of abnormalities with an accuracy of more than 92%.