An Adaptive Threshold Algorithm Combining Shifting Window Difference and Forward-Backward Difference in Real-Time R-Wave Detection

Most ECG diagnosis techniques require an accurate detection of the R-wave, so R-wave detection is important in ECG signal analysis. This paper presents a new real-time R-wave detection algorithm combining adaptive Shifting Window Difference Threshold (SWDT) and Forward-Backward Difference Threshold (FBDT). The algorithm can eliminate or weaken the impact of the high P-wave, high T-wave and other high-frequency interference signals onto the detection of R-wave. It can solve the problem of heavily loaded computation caused by the complicated algorithm of the traditional theory, and has been implemented on a Portable Single-lead ECG Monitor (PSEM) developed by authors. Finally, the algorithm was simulated by the American MIT-BIH Arrhythmia Database with an average detection error rate (DER) 0.2%. Some real data was also collected by our PSEM from several patients. Experimental results indicated that the proposed algorithm was simple, effective, robust, accurate and suitable for application in the embedded system.