# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
## 4 Results and Discussion
### Illustrations of the Identifications of QRS Peaks
Instead of removing the high-frequency components in the preprocessing stage, this study selects a lower-noise wavelet detail signal according to the results of the noise amount evaluation to reduce the interference of noise. For low-noise ECG signals, the higher-frequency d3' is usually selected for the detection of QRS peaks because the higher frequency wavelet detail signal has smaller energy to the lower frequency signal. Furthermore, d3' has higher detection accuracy than d4' for low-noise signals. If ECG signals suffer interference from a large amount of high-frequency noise, usually the higher frequency d3' would be contaminated by noise more than the lower frequency d4'. The detection of QRS peaks based on the lower-noise d4' can reduce the noise interference and improve the detection accuracy of QRS peaks for high-noise ECG signals. Comparing the proposed algorithm with non-wavelet-based methods using 30-minute ECG signals, the minimum FN was 79 in Manikandan and Soman [3], followed by 137 in Yazdani and Vesin [17], and 180 in the proposed method. The minimum FP is 91 in the proposed method, followed by 108 in Yazdani and Vesin [17], and 140 in Manikandan and Soman [3]. The DER of the proposed method is 0.25%, which is slightly higher than the 0.20% of Manikandan and Soman [3] and the 0.22% of Yazdani and Vesin [17]. The Se of the proposed method is 99.84%, which is slightly lower than the 99.93% of Manikandan and Soman [3] and the 99.87% of Yazdani and Vesin [17]. The maximum +P is 99.92% in the proposed method, followed by 99.90% in Yazdani and Vesin [17]. The results show that \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **TP** & **FN** & **FP** & **FP+FN** & **DER\%** & **Se\%** & **+P\%** \\ \hline
**Wavelet-Based Methods** & & & & & & & \\ Proposed & 109,308 & 180 & 91 & 271 & 0.25 & 99.84 & 99.92 \\ [PERSON] et al., 2012 [18] & 109,101 & 393 & 193 & 586 & 0.54 & 99.64 & 99.82 \\ [PERSON] et al., 2014 [19] & 109,354 & 140 & 232 & 372 & 0.34 & 99.87 & 99.79 \\ [PERSON] et al., 2015 [12] & 109,316 & 178 & 126 & 304 & 0.28 & 99.84 & 99.88 \\ [PERSON] et al., 2018 [14] & 3186 & 22 & 26 & 48 & 1.49 & 99.31 & 99.19 \\
**Non-Wavelet-Based Methods** & & & & & & & \\ [PERSON], 2004 [1] & 109,615 & 240 & 239 & 479 & 0.44 & 99.78 & 99.78 \\ [PERSON] and [PERSON], 2012 [3] & 109,417 & 79 & 140 & 219 & 0.20 & 99.93 & 99.87 \\ [PERSON] and [PERSON], & & & & & & & \\
2014 [11] & 115,945 & 192 & 308 & 500 & 0.43 & 99.81 & 99.70 \\ [PERSON] and [PERSON], & & & & & & & \\
2015 [6] & 108,880 & 614 & 353 & 967 & 0.88 & 99.43 & 99.67 \\ [PERSON], 2015 [7] & 109,281 & 202 & 210 & 412 & 0.38 & 99.82 & 99.81 \\ [PERSON], 2016 [13] & 109,692 & 273 & 163 & 436 & 0.40 & 99.75 & 99.85 \\ [PERSON] and [PERSON], 2016 [8] & 108,979 & 509 & 428 & 937 & 0.93 & 99.50 & 99.56 \\ [PERSON] and [PERSON], 2016 [17] & 109,357 & 137 & 108 & 245 & 0.22 & 99.87 & 99.90 \\ [PERSON] and [PERSON], 2017, [5] & 109,250 & 193 & 203 & 396 & 0.36 & 99.82 & 99.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of the proposed QRS peak detection system with the previous studies. the proposed algorithm also improves the detection accuracy of QRS peaks in comparison with the non-wavelet-based methods.