This paper presents QRS complex detection algorithm based on dual slope technique, which is suitable for wearable electrocardiogram (ECG) applications. For cardiac patients of different arrhythmias, ECG signals are needed to be monitored over an extensive period of time. Thus, the wearable heart monitoring system needs computationally efficient QRS detection technique with good accuracy. In this paper, a method of QRS detection based on two slopes on both sides of an R peak is presented which is computationally efficient. Based on the slopes, first, a variable measuring steepness is developed, then by introducing an adjustable R-R interval based window and adaptive thresholding techniques, depending on the number of peaks detected in such window, R peaks are detected. The algorithm was evaluated against MIT/BIH arrhythmia database and achieved 99.16% detection rate with sensitivity of 0.9935 and positive predictivity of 0.9981. The method was compared with two widely used R peaks detection algorithms.

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.

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