Design of hardware-efficient PVC recognition and classification system for early detection of sudden cardiac arrests

Abstract

Sudden cardiac arrest (SCA) is a critical cardiovascular condition that needs greater emphasis on its early detection because it occurs unexpectedly and is fatal within minutes. Nowadays, wearable healthcare technologies are preferred for continuous monitoring of a wide range of cardiovascular diseases. Additionally, the recognition of cardiac arrhythmias such as premature ventricular contraction (PVC) and non-sustained ventricular tachycardia (NSVT) by wearable healthcare devices can help in the early detection of SCAs. The biggest challenge in this regard for compactly sized wearable healthcare devices is to design a hardware-efficient yet highly accurate PVC detection and classification system. In this work, a novel and hardware-efficient PVC recognition and classification system capable of categorizing PVCs into five types of ventricular arrhythmias, including NSVT, for the early detection of SCAs is proposed. For PVC detection, lifting-based discrete wavelet transform (LDWT) is used to perform pre-processing and extract morphological electrocardiogram (ECG) features and the Teager energy operator. When compared to the state-of-the-art ventricular arrhythmias and PVC detection systems, the proposed system performs PVC beat recognition and classification with an improved accuracy and sensitivity of 98.29% and 98.64%, respectively, while utilizing only 4.36% of the total resources when implemented on the Nexys 4 DDR FPGA board.