## 4 Results and Discussion
### Comparison with Other Algorithms  
Table 6 shows the benchmarking results with the other 11 algorithms. Of the various algorithms, we highlighted the top three highest accuracies tested with both databases, i.e., [PERSON] & [PERSON] (2018), [PERSON] et al. (2015), and [PERSON] et al. (2019). Note that, for a fair comparison, we used accuracy results from manual ECG beat detection and segmentation, as used by the other top three algorithms. Nevertheless, for real-time implementation, we could use [PERSON]'s method for automatic beat detection and segmentation, as it only provides a slight decrease in accuracy performance. In [PERSON] & [PERSON] (2018), ECG beat detection and segmentation was conducted using [PERSON]'s algorithm ([PERSON] & [PERSON], 2018), in which they manually selected the best-quality eighteen or nine consecutive ECG beats to be fed to the Long Short Term Memory (LSTM) classifier. From each patient of MIT-BIH, they selected 18 ECG beats for training and testing. Each beat has 250 sample points, which is 125 points before and after \(R_{\text{peak}}\). For each subject of ECG-ID, they selected nine ECG beats for training and testing. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multicolumn{2}{c}{**Author and**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Database**} & \multirow{2}{*}{**Training**} & \multirow{2}{*}{**Testing**} & \multicolumn{1}{c}{**Accuracy**} \\  & & **Year** & & & & & **(\%)** \\ \hline \multirow{2}{*}{1} & [PERSON], 2005 & PCA, LDA, & ECG-ID & 6 beats & 6 beats & 96.00 \\  & [10] & MVC & MIT-BIH & - & - & - \\ \multirow{2}{*}{2} & [PERSON] et al., 2016 & Wavelet, & ECG-ID & 9 fiducial & 9 fiducial & 98.79 \\  & [26] & Random Forest & & & points & points & 98.79 \\  & & & MIT-BIH & 9 fiducial & 9 fiducial & 9 fiducial \\  & & & & points & points & 99.43 \\ \multirow{2}{*}{3} & [PERSON], 2017 [11] & Wavelet, CNN & ECG-ID & - & - & - \\  & & & MIT-BIH & single random & single random & 93.50 \\  & & & segment & segment & segment & 97.78 \\ \multirow{2}{*}{4} & [PERSON], 2017 & DCT, feature & ECG-ID & - & - & - \\  & [27] & correlation & MIT-BIH & - & single RR & 97.78 \\  & & & & segment & segment & \\ \multirow{2}{*}{5} & [PERSON] \& [PERSON], & ECG-ID & 9 beats & 9 beats & 100 \\  & 2017 [24] & LSTM & MIT-BIH & 18 beats & 18 beats & 100 \\  & & & & 3 sets of & 3 sets of & 100 \\ \multirow{2}{*}{6} & [PERSON] et al., & ANN (MIT-BIH) & ECG-ID & features & features & \\  & 2018 [25] & SVM (ECG-ID) & & 3 sets of & 3 sets of & \\  & & & MIT-BIH & features & features & 99.00 \\ \multirow{2}{*}{7} & [PERSON] et al., 2019 & & DWT, S-AE & ECG-ID & QRST features & QRST features & 98.87 \\  & [6] & & MIT-BIH & QRST features & QRST features & 96.82 \\ \multirow{2}{*}{8} & [PERSON] et al., 2019 & PCA-Net, SVM & ECG-ID & QRST features & QRST features & 97.75 \\  & [9] & & MIT-BIH & QRST features & QRST features & 100 \\ \multirow{2}{*}{9} & [PERSON] et. al., 2019 & & ECG-ID & 80\% & 9 beats & 98.60 \\  & [8] & BGRU & MIT-BIH & 80\% & 9 beats & 98.40 \\ \multirow{2}{*}{10} & Altan, 2019 [28] & LGA, SODP, & ECG-ID & 5s ECG & 5s ECG & 91.96 \\  & & kNN & segment & segment & segment & \\  & & & MIT-BIH & 5s ECG & 5s ECG & 95.12 \\ \multirow{2}{*}{11} & [PERSON], 2019 & Multiscale & ECG-ID & 500 beats & 2 beats & 91.96 \\  & [7] & 1D-residual & MIT-BIH & 500 beats & 2 beats & 95.12 \\ \multirow{2}{*}{12} & Proposed & CNN & ECG-ID & 10 beats & 8 beats & 100 \\  & & MDS-CNN & MIT-BIH & 100 beats & 6 beats & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Benchmarking of the proposed algorithm with other algorithms using ECG-ID and MIT-BIH databases. Each beat has 300 sample points, which is 150 before and after \(R_{peak}\). Our proposed algorithm used a lower number of consecutive ECG beats, i.e., eight for ECG-ID and six for MIT-BIH, and no selection of the quality of ECG signal.