## 3 Results and Discussion
Further, it can be confirmed that the highest recognition rate is shown, when the optimum number of features is found for each piece of data, and the recognition rate is lowered, when the number is smaller or larger than the optimum number. It is difficult to determine the classifier and the optimal number of features for each DB. However, experimental results show that the best results are obtained when the LDA classifier is used on the average in NSR, PTB, and QT DB, and the optimal number of features are 40, 300 and 110, respectively. In particular, when comparing the average result with regards to the window identification rate, the LDA classifier shows about a 0.50% and 1.83% higher recognition rate than the NN and SVM classifier on average. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**DB** & **Subject Identification Rate (\%)** & **Window Identification Rate (\%)** & **Number of Features (\%)** \\ \hline \multirow{3}{*}{NSR} & 100 & 95.05 & 20 \\  & 100 & 96.92 & 40 \\  & 100 & 95.04 & 60 \\ \hline \multirow{3}{*}{PTB} & 99.60 & 95.57 & 200 \\  & 100 & 95.82 & 300 \\  & 98.80 & 94.15 & 400 \\ \hline \multirow{3}{*}{QT} & 100 & 97.57 & 30 \\  & 100 & 98.32 & 50 \\ \cline{1-1}  & 100 & 97.91 & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of results according to the number of features with support vector machine (SVM). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Subject Number**} & \multicolumn{3}{c}{**Subject Identification Rate (\%)**} & \multicolumn{3}{c}{**Window Identification Rate (\%)**} \\ \cline{3-8}  & & **NSR** & **PTB** & **QT** & **NSR** & **PTB** & **QT** \\ \hline \multirow{3}{*}{NN} & 100 & 99.40 & 100 & 99.02 & 97.13 & 98.91 \\  & & 100 & 100 & 100 & 96.92 & 95.82 & 98.32 \\ \cline{1-1}  & & 100 & 100 & 100 & 98.67 & 98.65 & 99.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of proposed method. NN: nearest neighbor; SVM: support vector machine; LDA: linear discriminant analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DB** & **Subject Identification Rate (\%)** & **Window Identification Rate (\%)** & **Number of Features (\%)** \\ \hline \multirow{3}{*}{NSR} & 100 & 98.14 & 30 \\  & 100 & 99.02 & 50 \\  & 99.44 & 96.20 & 70 \\ \hline \multirow{3}{*}{PTB} & 99.20 & 95.57 & 200 \\  & 99.40 & 97.13 & 300 \\  & 99.40 & 96.78 & 400 \\ \hline \multirow{3}{*}{QT} & 100 & 97.56 & 50 \\  & 100 & 98.91 & 70 \\ \cline{1-1}  & 100 & 98.75 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of results according to the number of features with nearest neighbor (NN). In order to demonstrate the efficiency of the proposed window removal method, the experiment results are compared before and after the application. As shown in Table 6, there is no significant difference between the results before and after applying the window removal method, because the subject recognition rate is already high in all of the data and classifiers. However, it confirms that the results of the window recognition rate after applying the window removal method are better than those before applying the window removal method in all data. It also confirms that the results of all classifiers used in the experiment have improved. Therefore, the window removal method proposed in this study can improve the ECG identification rate by efficiently removing unnecessary windows. Lastly, random selection is performed and it is compared to the results of other algorithms in order to evaluate the performance of the proposed method. NSR, PTB, and the QT DB are short data recorded within 60 s. A small number of training and test windows are generated because the window is set at 5 s time intervals. Therefore, it is difficult to apply 10 fold cross-validation. The performance of the proposed algorithm is verified by averaging random selection 10 times. The random selection is performed by selecting the training window at random and selecting the rest as the testing window.