# Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS
## 5 Experiments and Results
### Experimental Results and Analysis  
In order to verify the superiority of the proposed algorithm, the above network models are compared with the BiLSTM-GoogLeNet-DS model, mainly from the evaluation indicators. This paper improves the last four layers of the GoogLeNet network, and mainly updates the random dropout layer, fully-connected layer, Softmax layer, and classification layer. Using the improved D-S theory, the BPA function is weighted by the method based on confusion matrix. The following are some experimental results. Figure 12 shows the confusion matrix obtained by different models. The confusion matrix is mainly used to weight the BPA function. It solves the problem that the BPA function of the D-S theory is difficult to determine in decision fusion. Interventionary studies involving animals or humans, and other studies that require ethical approval, must list the authority that provides approval and the corresponding ethical approval code. In order to show more intuitively and clearly the superiority of the proposed algorithm, the BiLSTM-GoogLeNet-DS model is compared with the BiLSTM model and the GoogLeNet model, respectively. The comparison results are shown in Table 3. It can be seen from Table 3 that the classification method proposed in this paper, based on BiLSTM-GoogLeNet-DS, has the best classification effect. Although the classification method based on PCG-BiLSTM obtained 100% sensitivity, 100% precision, and a 100% F1 score, the specificity is only 0%. This means that the classification method cannot identify healthy samples. Although the classification method based on PCG-GoogLeNet obtained 100% sensitivity, the specificity is only 18.29%. This means that the classification effect of this method for health samples is also very unsatisfactory. In order to express the comparison results in the table more clearly, this paper uses the following histograms for graphical displays. It can be clearly seen from Figure 13 that the classification accuracy of the algorithm proposed in this paper (based on the BiLSTM-GoogLeNet-DS model) is higher than that of other classification models. In Figure 14, the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification Mode** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F1 Score** \\ \hline PCG-BiLSTM & 71.13\% & 100\% & 0\% & 100\% & 100\% \\ ECC-BiLSTM & 75\% & 91.58\% & 34.15\% & 77.41\% & 83.9\% \\ PCG-BC-BiLSTM & 78.17\% & 95.05\% & 36.59\% & 78.69\% & 86.1\% \\ PCG-GoogLeNet & 76.41\% & 100\% & 18.29\% & 75.09\% & 85.78\% \\ ECG-GoogLeNet & 85.92\% & 99.51\% & 52.44\% & 83.75\% & 90.95\% \\ BiLSTM-GoogLeNet-DS & 96.13\% & 98.48\% & 90.8\% & 96.04\% & 97.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of several algorithm models. Figure 12: Confusion matrix obtained by different models: (**a**) ECG-PCG-BiLSTM; (**b**) ECG-GoogLeNet; (**c**) PCG-GoogLeNet; (**d**) BiLSTM-GoogLeNet-DS. sensitivity, specificity, precision, and F1 score are compared. The best classification results are obtained by using the BiLSTM-GoogLeNet-DS model. The classification accuracy, sensitivity, specificity, precision, and F1 score are 96.13%, 98.48%, 90.8%, 96.04%, and 97.24%, respectively. From a comprehensive point of view, the algorithm proposed is better than other algorithm models. Figure 14: The comparison of sensitivity, specificity, precision, F1 score: (**a**) the comparison of sensitivity; (**b**) the comparison of specificity; (**c**) the comparison of precision; (**d**) the comparison of F1 score. Figure 13: The comparison of accuracy. This paper also compares the methods from other literature studies. The comparison mainly focuses on the methods based on deep learning and the classification signals. Moreover, the current research studies on ECG and PCG signals mainly focus on the database provided in the PhysioNet/CINC challenge 2016 heart sound database, so the comparative classification effect is more convincing.