# Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS
## 6 Conclusions  
In this paper, an ECG-PCG signal classification method based on BiLSTM-GoogLeNet-DS is proposed. The method filters the obtained ECG and PCG signals, and uses the BiLSTM network to fuse and classify the two cardiac function signals. Then, the time-frequency processing of the filtered signals is performed. The time-frequency diagrams of signals are input into the GoogLeNet network for classification. Combining the above classification results provides a discriminative decision using the improved D-S theory to obtain the final classification results. The following conclusions can be drawn from the experiments:  
1. The proposed ECG and PCG signal classification method based on the BiLSTM-GoogLeNet-DS model is superior to the classification methods of the BiLSTM model and the GoogLeNet model. The classification method of ECG and PCG signals is better than the classification methods of single-modal signals. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Classification Method** & **Feature Form** & **Results** \\ \hline \multirow{3}{*}{[PERSON] [29]} & \multirow{3}{*}{1-D CNN} & Time-frequency domain features & Acc = 87.0\% \\  & & PCA domain features & Sen = 90.8\% \\  & & PCG signal & Spe = 83.2\% \\ \hline \multirow{3}{*}{[PERSON] [30]} & \multirow{3}{*}{2-D CNN} & MFCC features & Acc = 84.8\% \\  & & PCG signal & Sen = 76.5\% \\  & & Spe = 93.1\% \\ \hline \multirow{3}{*}{[PERSON] [31]} & \multirow{3}{*}{2-D CNN} & MFCC features & Acc = 88.8\% \\  & & PCG signal & Sen = 86.1\% \\  & & PCG signal & Spe = 91.6\% \\ \hline \multirow{3}{*}{[PERSON] [7]} & \multirow{3}{*}{Support vector machine} & PCG signal & Acc = 92.5\% \\  & & ECG signal & Sen = 92.31\% \\  & & Spe = 92.86\% \\ \hline \multirow{3}{*}{[PERSON] [5]} & \multirow{3}{*}{Dual-Input Neural Network} & PCG signal & Acc = 95.6\% \\  & & ECG signal & Sen = 98.5\% \\  & & Spe = 89.2\% \\ \hline \multirow{3}{*}{This paper} & \multirow{3}{*}{BiLSTM-GoogLeNet-DS} & PCG signal & Acc = 96.13\% \\  & & ECG signal & Sen = 98.48\% \\ \cline{1-1}  & & Spe = 90.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between this algorithm and other algorithms in the literature. 2. This paper uses the deep learning-oriented hybrid fusion method. After the feature layer fusion of signals, the improved D-S theory is used to fuse the decision layer. The better classification results are obtained. 3. The BiLSTM model is used for feature layer fusion, and the obtained classification results are better than those of single-modal signals. This method can extract the features of long-distance-dependent information according to the BiLSTM network, which can improve the training time and learn the features better. It can produce higher classification accuracy for long-span time series data. 4. The classification effect obtained by using the GoogLeNet model to classify cardiac function signals is better than the classification method based on the BiLSTM model, which highlights the advantages of GoogLeNet in classification and recognition. 5. The classification results of GoogLeNet and BiLSTM are fused by the improved D-S theory for the decision-making layer, which mainly calculates the BPA function for the classification result of each channel. Using the local recognition credibility information generated by the confusion matrix to weigh the BPA function can improve the final classification result. 6. The best classification results are obtained by applying the ECG and PCG signal classification method based on BiLSTM-GoogLeNet-DS with 96.13% classification accuracy, 98.48% sensitivity, 90.8% specificity, 96.04% precision, and a 97.24% F1 score. It can be concluded from the experimental results that the information fusion of ECG and PCG signals improves the performance indicators of classification and improves the classification effect. How to fuse more modal cardiac function signals to be used for disease diagnosis and treatment will be the next research direction. Conceptualization, J.L. and L.K. ; methodology, J.L. ; software, J.L.