Comparative study of electrocardiogram QRS complex detection algorithm on Field Programmable Gate Array platform

Nowadays, many people suffer from heart problems and hence the demand of inexpensive and efficient electrocardiogram (ECG) for frequent heart monitoring is becoming crucial. To make the ECG device portable, cost-effective and light-weight, an alternative of deploying an ECG system is on Field Programmable Gate Array (FPGA). It is also important to choose suitable algorithm that optimize in terms of feature extraction accuracy and computation time. This paper implements and compares two methods of ECG QRS detection in terms of Pan and Tompkins algorithm and Derivative-based Method on FPGA platform as embedded software computation. The Derivative-based Method is modified from fixed threshold to adaptive threshold to increase robustness in real time QRS detection. The input are 48 records of 30 minutes, total 24 hours ECG data obtained from MIT-BIH database as standard database for performance benchmarking. Both algorithm yield a different outcome in terms of accuracy and computation speed. Results show that though Pan and Tompkins shows a better accuracy (98.15%) on detecting the QRS complex compared to Derivative-based Method (96.73%), the latter consume less than half of computation time (a total 17.86 minutes to compute 24 hours ECG data). Compared to Pan and Tompkins algorithm (a total 45.2 minutes to compute 24 hours ECG data).