Table 3.

Comparison of performance of several QRS detection algorithms cited in the literature

Method	Method description	Sensitivity (%)
Pan et al. [30]	Derivative approach based on filtering and analyzing the slope	99.30
Szu et al. [37]	Neural network based on adaptive filtering	99.50
Sai et al. [38]	Using the Euclidean distance metric with KNN algorithm (K-Nearest Neighbor)	99.81
Ben et al. [44]	Approach based on discrete wavelet decomposition and calculation of energy	99.39
Ham et al. [56]	Derivative approach based on filtering using an optimized process of rule decision	99.46
Cho et al. [57]	A multi wavelet packet decomposition	99.14
Had et al. [58]	Empirical modal decomposition (EMD)	99.92
Chr et al. [59]	Use of adaptive thresholding	99.65
Gha et al. [60]	Mathematical model based on the continuous wavelet transform (CWT)	99.91
Kry et al. [61]	Technique based on the recursive temporal prediction	99.00
Meh et al. [62]	Approach based on SVM (Support Vector Machine)	99.75
Gri et al. [63]	A transformation based on the duration and the energy	99.26
Tra et al. [64]	Approach based on mathematical morphology	99.38
The suggested method	Approach based on regular grammar and calculation of the standard deviation	99.74