Arrhythmia Supraventricular Premature Beat Detection in Electrocardiography Signal using Deep Gated Recurrent Model

Heart diseases are mainly diagnosed by the electrocardiogram (ECG) or (EKG), a mechanism of recording the heart's electrical signals. Correct detection of ECG signals behavior helps diagnose heart diseases and assist the cardiologists in determining the correct treatment for the patient. This paper proposed a novel enhanced Deep Gated Recurrent (eDGRU) based model for heart supraventricular premature beat detection in a multivariate ECG signal. The proposed model achieved a high detection accuracy using a small implementation budget. Furthermore, it could be easily implemented on portable hardware devices.