# Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS
## 5 Experiments and Results
### Comparative Experimental Setup  
In the experiment, the following classification models are set to evaluate the model in this paper, and the classification performance of the BiLSTM-GoogLeNet-DS model is verified by comparing the models:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Definition** & **Optimal Value** \\ \hline sequenceInputLayer & Input vector dimension & 284 \\ Number of hidden units & Number of hidden nodes & 50 \\ Adam & Optimizer & Adam \\ MaxEpochs & Training epochs & 10 \\ MiniBatchSize & Number of samples used per batch & 150 \\ InitialLearnRate & Learning rate & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: BiLSTM network model parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Definition** & **Optimal Value** \\ \hline imgSTrain & Input vector dimension & [224 224 3] \\ dropoutLayer & Random drop rate & 0.6 \\ sgdm & Optimizer & sgdm \\ MaxEpochs & Training epochs & 20 \\ MiniBatchSize & Number of samples used per batch & 15 \\ InitialLearnRate & Learning rate & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: GoogLeNet network model parameters.  
BiLSTM model.  
The BiLSTM model is composed of two LSTMs in different directions, which can simultaneously capture the front and backward timing information and solve the long-distance dependency problem in the classification process. After the PCG and ECG signals are filtered separately, the signal features are extracted by BiLSTM, and then input into the fully-connected layer and the Softmax layer to realize the classification of the signal. In order to verify the superiority of ECG and PCG signal classification, in addition to inputting PCG and ECG signals into the BiLSTM network model to obtain their respective classification results, the paper also splices and fuses the PCG and ECG signals into the BiLSTM network model for classification. Since the classification effects of ECG and PCG signals are better than that of single-modal cardiac function signals using the PCG or ECG signal, this paper mainly focuses on the classification results of ECG and PCG signals using the BiLSTM model for further processing.  
GoogleNet model.  
The GoogLeNet model consists of several inception modules. The GoogLeNet model structure selected in this paper consists of nine layers of inception modules, namely inception-3a, inception-3b, inception-4a, inception-4b, inception-4c, inception-4d, inception-4e, inception-5a and inception-5b. Each inception module includes three convolutional kernels of 1 \(\times\) 1, 3 \(\times\) 3, and 5 \(\times\) 5. When the image data are input into the network, 7 \(\times\) 7 convolution is performed first, then the ReLU layer, pooling layer, regularization, and 3 \(\times\) 3 convolution. After the ReLU layer, pooling layer, and regularization, the data are input into each inception module in turn. The filtered PCG and ECG signals are subjected to continuous wavelet transform to obtain their time-frequency diagrams. The sizes of the diagrams are transformed to be suitable for the input into the GoogLeNet network. The network is used to classify the PCG and ECG signals, respectively.  
BiLSTM-GoogLeNet-DS model.  
The BiLSTM-GoogLeNet-DS model first splices and fuses ECG and PCG signals into the BiLSTM network to obtain the fused classification results. Then the ECG and PCG signals are, respectively, input into the GoogLeNet network for classification. Based on the above classification results, a total of three channel classification results are obtained. The improved D-S theory is used to fuse the above classification results. Then the classification results are finally determined according to the discriminant criterion.