## 4 Results
### CNN-Based Feature Extraction Using Transfer Learning
#### 4.3.5 Results for Proposed Models  
The proposed model incorporates DWT and 1D-CRNN features. The best features selected from DWT based on their results, Coif5 and Haar wavelet give the best results on the random forest classifier (RFC). The model was trained using LSTM with 1D-CNN, the features extracted from the dense layer before the output layer, 128 features extracted from the dense layer and 256 features extracted from the DWT wavelet are combined to form hybrid features. The hybrid features are classified using the Random Forest classifier and the proposed method is shown in Table 10.  
The hybrid features' complexity analysis was performed on four different databases: ECG-ID, MIT-BIH, NSRDB and a combined dataset. The total parameters for ECG-ID, MIT-BIH, NSRDB and the combined dataset were 587,650, 573,232, 569,362 and 587,164, respectively. The training times for ECG-ID, MIT-BIH, NSRDB and the combined dataset were 1 min, 9 min, 4 min and 13 min and 12 s, respectively. The testing times for ECG-ID, MIT-BIH, NSRDB and the combined dataset are 0.77 ms, 0.53 ms, 1.01 ms and 0.60 ms, respectively. For a detailed analysis of the results, the barplot is shown in the figures for all datasets. The results contain the mean value of the F1 score, precision, recall and specificity and accuracy. These are plotted using bar plots. In Figure 16, the blue bar represents the mean specificity, the red bar represents the mean recall, the gray bar represents the mean precision, the yellow bar represents the mean f1-score and the green bar represents the accuracy. The models are represented on the x axis, and the percentage values are \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Database Name**} & \multirow{2}{*}{**1D-CRNN**} & \multirow{2}{*}{\begin{tabular}{c} **DWT +** \\ **Random** \\ **Forest** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **DWT +** \\ **1D-CRNN** \\ **+ Random** \\ **Forest** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Training** \\ **Samples** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Testing** \\ **Samples** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Number** \\ **of Classes** \\ \end{tabular} } \\ \hline ECGID & 73.55\% & 73.04\% & 80.64\% & 2480 & 620 & 90 \\ MIT-BIH & 97.50\% & 97.60\% & 98.81\% & 34,464 & 8544 & 48 \\ NSRDB & 99.06\% & 99.15\% & 99.62\% & 12,924 & 3204 & 18 \\ Combined & 96.91\% & 96.28\% & 98.24\% & 49,868 & 12,368 & 156 \\ dataset & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy of the models 1D-CRNN, DWT+ random forest and DWT + 1D-CRNN + random forest on ECG segmented samples. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Database Name** & **VGG-19** & **ResNet-152** & **Inception Version 3** \\ \hline ECGID & 70.96\% & 70.96\% & 70.96\% \\ MITD & 94.99\% & 93.56\% & 93.16\% \\ NSRDB & 98.50\% & 98.50\% & 98.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy of pretrained models Vgg19, ResNet-152 and Inception version 3 on the ECG Scalogram images. databases are 99.97%, 99.62%, 99.62% and 99.62%, respectively. Figures 16-18 represent the bar plots for the three databases. The comparison of different models such as CNN, VGG19, ResNet-152, Inception version 3 and the proposed hybrid features base model was analyzed on the MIT-BIH, ECG-ID and NSR-DB datasets. Thus, the proposed model gives a comparatively better result instead of the other transfer learning and CNN model base approach. The ECG-ID dataset is collected from 90 subjects, which contains 310 recordings for the 90 subjects. Some of the subjects have ten samples, some have five samples, and some have two samples, so these are imbalanced data which can affect the performance of the training model. The performance decrease is because of those subjects having a small number of samples compared to the subjects with a large number of samples. Because of this non-scalability issue of the ECG-ID dataset, it is unlikely that this could be addressed using the current ECG-based techniques.