## 3 Results
### R Point Detection Performance  
Figure 14 shows the R point detection result for the Record 108, 111, 118, 217, 214 sections in MIT-BIH DB based on the QRS interval detected in Figure 12. Although the Record 108 section includes bigger P-waves and inverse QRS intervals, the positions of the inverse QRS and PVC were well detected. In the figure, the x in Record 108 means a non-conducted P-wave (blocked APB). This shows that there will not be a QRS complex following. The Record 111 section includes notched QRS intervals and relatively large T waves; however, periodic R points were well detected. The R detection of the Record 118 section is very easy compared to other signal sections. The Record 217 section includes deep S waves and large T waves; however, positive R points were well detected. We can also see that the positive R points and the PVC beats in the Record 214 section including large T waves are well detected.  
The CYBHi DB (1000 samples/s) [31; 32]--which comprises off-the-person data--is also used to verify the R point detection performance of the proposed method. The signals of the DB contain more noise compared to the MIT-BIH DB. The sliding window length of 3000 samples and the step size of 1500 samples are used for the experiment. The ECG signals of the CYBHi DB used in the experiments were filtered by a low-pass filter with a 40 Hz cut-off frequency. Figure 15 shows the R point detection result for the Record 20110719-RMAF-CI-8B, 20110718-ARS-CI-8B, and 20110718-ARS-CI-8S sections in the CYBHi DB. While the 20110719-RMAF-CI-8B section contains bigger T waves, the 20110718-ARS-CI-8B section and 20110718-ARS-CI-8S section contain DC wandering and bigger P waves. Nevertheless, it can be seen that the proposed method detects the R point relatively accurately.  
To evaluate the R peak performance of the proposed method, commonly used detector performance indexes are applied, including false negative (FN), which means failing to detect a true beat (actual QRS); and false positive (FP), which represents a false beat detection. By using FN and FP, the sensitivity (Se), positive prediction (+P), and detection error rate (DER) are calculated by using Se [%] = TP/(TP+FN), +P [%] = TP/(TP+FP), and Der [%] = (FP + FN)/(TP + FN), respectively. The true positive (TP) is the total number of the true positive beats correctly detected by the algorithm. Additionally, the denominator (TP + FN) represents the number of total QRS [34]. Table 1 shows the detection results of the proposed method for all 48 recordings in the MIT-BIH DB. As shown in Table 1, the proposed method achieves Se = 99.80%, +P = 99.80%, and DER = 0.36% against MIT-BIH DB.  
Figure 13: Exclusion of unreliable RR intervals.  
Figure 14: R point detection result for various Record sections in MIT-BIH DB. A: 108; B: 111; C: 118; D: 217; E: 214.  
Figure 15: R point detection result for various Record sections of CYBHi DB. A: 20110719-RMAF-CI-8B; B: 20110718-ARS-CI-8B; C: 20110718-ARS-CI-85.  
Table 2 shows the supplementary function and detection performance comparison of the proposed method with the existing methods. The criterion of real-time feasibility means that the average calculation time for the 1 s ECG signal of Record 111 is less than 10 ms. The proposed method has a high Se and +P but a low DER, similar to that of other methods. R points contaminated by noise affect the RR interval and furthermore the HRV. Therefore, the elimination of erroneously calculated RR intervals due to contaminated R values is very important in the analysis of patient condition. The proposed method not only detects the R point (or QRS) but also detects noises and the erroneously calculated RR interval in order to compute a reliable HRV.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Type** & **Total** & **FN** & **FP** & **Se [\%]** & **+P [\%]** & **DER** & **Tape** & **Total** &