Computationally efficient QRS detection analysis based on dual-slope method

This paper presents a computationally efficient QRS detection algorithm for wearable electrocardiogram (ECG) applications based on dual-slope analysis. In general, ECG signals of arrhythmias are pseudo-periodic and contaminated with noises like the patient's contraction muscles, respiration, 60 Hz interference and other types which impede correct QRS detection. To resolve this problem, in this paper, a technique is presented which is based on two slopes on both sides of a peak in ECG signal. Based on these slopes, a variable measuring steepness is developed and R peaks are detected. The algorithm was evaluated against MIT/BIH arrhythmia database and achieved 99.38% detection rate. This method was compared with one of the recently developed dual-slope based QRS detection methods. The results showed that the proposed method has 12.48 times faster runtime than the old method.