## 3 Results and Discussion  
NSR, PTB, and the QT DB are used to perform the method proposed in this study. 18 subjects in the NSR DB, 50 subjects in the PTB DB, and 36 subjects in the QT DB are used. The total number of windows in each DB is 184 (Unmeasured portions were excluded), 600 and 432. At random for each subject, half of the window is set to training data, and the rest is set to testing data to perform the window recognition. Thereafter, the subject class is determined using a majority voting. The experimental setting is shown in Table 1.  
Figure 4 demonstrates the results of pre-processing. Figure 4a shows the original signal and Figure 4b shows the result of the noise removal by applying Daubechies 4. Figure 5 shows the result of applying autocorrelation and scaling in the pre-processed signal, and Figure 6 shows the result of the DCT application. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**DB** & \multicolumn{2}{c}{**Record**} & \
\begin{tabular}{c} **Number of** \\ **Subjects** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Windows** \\ \end{tabular} \\ \hline NSR & 16265, 16272, 16273, 16420, 16483, 16539, 16773, 16786, 16795, 17052, 17453, 18177, & 18 & 184 \\  & 18184, 19088, 19090, 19093, 19140, 19830 & & \\ \hline PTB & 15, 17, 20, 21, 22, 26, 28, 36, 39, 43, 45, 46, 47, 52, 53, 54, 58, 62, 65, 66, 80, 83, 87, 88, 89, & \\  & 90, 92, 99, 100, 102, 105, 109, 110, 111, 112, 125, 129, 132, 135, 141, 142, 147, 420 A25, & 50 & 600 \\  & 441, 488, 489, 546, 548, 549 & & \\ \hline QT & sel34, sel100, sel103, sel104, sel116, sel123, sel213, sel230, sel231, sel301, sel302, sel306, sel307, & \\  & sel301, sel803, sel808, sel811, sel820, sel840, sel847, sel853, sel872, sel873, sel883, & 432 \\  & sel891, sel14157, sel1625, sel1627, sel16273, sel16420, sel16483, sel16539, sel1673, & \\  & sel16786, 16795, sel17152, sel17453 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental setting. DB: database; NSR: normal sinus rhythm; PTB: pth diagnostic; QT: qt database. Figure 3: Hierarchical one vs. one (OAO). The result of this study's experiment is indicated by the window and subject identification rate. The window identification rate refers to the ratio of windows with correct recognition out of all the windows of the respective subject. The subject identification rate refers to the ratio of the number of correctly identified subjects among the total subjects after the subjects are determined using a majority vote by examining the generated windows. Table 2 shows the test results of the method proposed in this study. As shown in Table 2, when the NN classifier is used in the NSR, PTB and QT DB, the subject identification rates are 100%, 99.40% and 100%, while the window identification rates are 99.02%, 97.13% and 98.91%. When the SVM classifier is used, the subject identification rates are 100% in all of the DBs, while the window identification rates are 96.92%, 95.82% and 98.32%, respectively. When the LDA classifier is used,  
Figure 4: Pre-processing result: **(a)** Original signal; **(b)** Signal with daubechies used. Figure 5: Signal with autocorrelation and scaling used. Figure 6: Signal with discrete cosine transform (DCT) used. the subject identification rates are 100% in all of the DBs, while the window identification rates are 98.67%, 98.65% and 99.23%, respectively. Each NSR, PTB, and QT DB are derived from a different acquisition environment and sampling frequency; thus, the results would vary depending on the number of features. Additionally, it is possible to derive various identification results that are based on the classifier selection in the personal identification, such as signal processing and pattern recognition. Therefore, this study also compares the results by applying various feature numbers according to the classifiers in the NSR, PTB, and QT DB. As shown in Tables 3-5, the window recognition rate slightly varies according to the classifiers of NN, SVM, and LDA, as well as the number of extracted features.