SpEC: A system for patient specific ECG beat classification using deep residual network

Abstract

Electrocardiogram (ECG) is a non-invasive technique used to detect various cardiac disorders. One of the major causes of cardiac arrest is an arrhythmia. Furthermore, ECG beat classification is essential to detect life-threatening cardiac arrhythmias. The major limitations of the traditional ECG beat classification systems are the requirement of an extensive training dataset to train the model and inconsistent performance for the detection of ventricular and supraventricular ectopic (V and S) beats. To overcome these limitations, a system denoted as SpEC is proposed in this work based on Stockwell transform (ST) and two-dimensional residual network (2D-ResNet) for improvement of ECG beat classification technique with a limited amount of training data. ST, which is used to represent the ECG signal into a time-frequency domain, provides frequency invariant amplitude response and dynamic resolution. The resultant ST images are applied as input to the proposed 2D-ResNet to classify five different types of ECG beats in a patient-specific way as recommended by the Association for the Advancement of Medical Instrumentation (AAMI). The proposed SpEC system achieved an overall accuracy (Acc) of 99.73%, sensitivity (Sen)=98.84%, Specificity (Spe)=99.50%, Positive predictivity (Ppr)=98.20% on MIT-BIH arrhythmia database, and shows an overall Acc of 89.87% on real-time acquired ECG dataset with classification time of single ECG beat image=0.2365 (s) in detecting of five arrhythmia classes. The proposed method shows better performance on both the database compared to the earlier reported state-of-art techniques.