Table 3 Comparison of performance of several QRS detection algorithms cited in the literature
From: Real time QRS complex detection using DFA and regular grammar
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 |