## 3 Results and Discussion
Table 7 shows the accuracy for each trial after performing it 10 times, as well as the number of correctly recognized windows and the total number of windows. For each trial, it can be shown that the number of test windows is slightly different due to the application of the window removal method. As shown in Figure 7 and Table 7, LDA, SVM, and NN classifiers show high results, but LDA shows higher overall results than NN and SVM, except for the NSR DB. In Table 8, when compared to the results of the other studies, the proposed method shows high results in both the subject and window identification rates, except for the study by [PERSON] et al. (2012) and [PERSON] et al. (2011). However, in the study by [PERSON] et al. (2012) and [PERSON] et al. (2011), experiments were formed with a small sample size. On the other hand, this study is performed with a larger sample size when compared to the two studies, and shows significantly higher results. In addition, the PTB DB includes abnormal signals, such as myocardial infarction and cardiomyopathy. Such abnormal signals may cause a reduction in recognition rate. When compared to the results containing abnormal signals by [PERSON] et al. (2012), [PERSON] et al. (2017), and [PERSON] et al. (2018) using PTB in Table 8, the proposed method shows relatively high results in not only the subject identification rate, but also in the window identification rate with regards to the various DBs. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Without Window Removal Method**} & \multicolumn{6}{c}{**With Window Removal Method**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Subject Identification Rate (\%)**} & \multicolumn{3}{c}{**Window Identification Rate (\%)**} & \multicolumn{3}{c}{**Subject Identification Rate (\%)**} & \multicolumn{3}{c}{**Window Identification Rate (\%)**} \\ \cline{2-13}  & **NSR** & **PTB** & **QT** & **NSR** & **PTB** & **QT** & **NSR** & **PTB** & **QT** & **NSR** & **PTB** & **QT** \\ \hline NN & 100 & 99.40 & 100 & 95.85 & 96.80 & 97.18 & 100 & 99.40 & 100 & 99.02 & 97.13 & 98.91 \\ SVM & 100 & 100 & 100 & 95.11 & 95.76 & 96.32 & 100 & 100 & 100 & 96.92 & 95.82 & 98.32 \\ LDA & 100 & 100 & 100 & 97.13 & 98.40 & 97.87 & 100 & 100 & 100 & 98.67 & 98.65 & 99.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the results with and without the window removal method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**DB** & **Subject Identification Rate (\%)** & **Window Identification Rate (\%)** & **Number of Features (\%)** \\ \hline \multirow{3}{*}{NSR} & 97.22 & 85.17 & 20 \\  & 100 & 98.67 & 40 \\  & 94.44 & 97.41 & 60 \\ \hline \multirow{3}{*}{PTB} & 99.80 & 98.44 & 200 \\  & 100 & 98.65 & 300 \\  & 99.80 & 98.40 & 400 \\ \hline \multirow{3}{*}{QT} & 100 & 98.89 & 90 \\  & 100 & 99.23 & 110 \\  & 100 & 98.54 & 130 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of results according to the number of features with linear discriminant analysis (LDA). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**DB**} & \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**1**} & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \multicolumn{11}{c}{**Accuracy (5x) Number of Correctly Recognized Windows/Total Number of Windows)**} \\ \hline \multirow{11}{*}{NSR} & \multirow{3}{*}{NN} & 97.80 & 97.83 & 100 & 100 & 100 & 100 & 97.83 & 98.88 & 97.83 \\  & & (89/91) & (69/92) & (89  
The results of this study and the other studies may be difficult to objectively evaluate due to the various differences in the data measurement environment, method, and DBs. However, this study is able to enhance the reliability of the algorithm by considering the data that contain normal, as well as abnormal, signals, and using multiple DBs instead of a single DB.