# Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS
## 4 Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS
On the basis of retaining the original 140-layer structure, this paper improves the last four-layer structure. Using the new dropout layer, the fully-connected layer, the Softmax layer, and the classification layer replace the original layers. This network structure is used to train the PCG and ECG signals, respectively. The optimal network model is obtained by continuously adjusting the network parameters. Then the classification results of the two-channel signals are obtained. Figure 11: GoogLeNet network structure. Figure 10: Time-frequency diagrams of signals: (**a**) the time-frequency diagram of the ECG signal; (**b**) the time-frequency diagram of the PCG signal. Step 6: The three-channel classification results obtained in the above steps are fused at the decision level using the improved D-S theory. According to the obtained classification results, the BPA function under various classification methods is obtained. Then the ECG and PCG signals are fused and determined according to the improved D-S theory. The following is the implementation process. 1. Classification framework establishment  
The above classification results are used as the evidence of ECG and PCG signal classifications. The classification framework is established, as shown in the formula. \[\Theta{=\{\omega_{normal},\omega_{abnormal}\}} \tag{12}\]  
\(\omega_{normal}\) represents the normal signal with a classification result, and \(\omega_{abnormal}\) represents the abnormal signal with a classification result. The subset of the framework \(\Theta\) contains \(\{\{\omega_{normal}\},\{\omega_{abnormal}\},\{\omega_{normal},\omega_{ abnormal}\}\}\). Research studies that deposit large datasets in publicly available databases should specify where the data are deposited and provide the relevant accession numbers. If the accession numbers have not yet been obtained at the time of submission, please state that they will be provided during the review. They must be provided prior to publication. 2. The BPA function establishment  
The normal PCG signal probability value \(p(\omega_{1n})\) and the abnormal PCG signal probability value \(p(\omega_{1p})\) obtained in Step 5 are used as the BPA function \(m_{1}\) of the PCG signal. The normal ECG signal probability value \(p(\omega_{2n})\) and the abnormal ECG signal probability value \(p(\omega_{2p})\) are used as the BPA function \(m_{2}\) of the ECG signal. The normal ECG and PCG signal probability value \(p(\omega_{3n})\) and the abnormal ECG and PCG signal probability value \(p(\omega_{3p})\) obtained in Step 3 are used as the BPA function \(m_{3}\) of the ECG and PCG signals, as shown in the formula. \[\left\{ \begin{array}{l}m_{1}(\omega_{normal})=p(\omega_{1n})\\ m_{1}(\omega_{abnormal})=p(\omega_{1p})\\ m_{2}(\omega_{normal})=p(\omega_{2n})\\ m_{2}(\omega_{abnormal})=p(\omega_{2p})\\ m_{3}(\omega_{normal})=p(\omega_{3n})\\ m_{1}(\{\omega_{normal},\omega_{abnormal}\})=m_{2}(\{\omega_{normal},\omega_{ abnormal}\})=m_{3}(\{\omega_{normal},\omega_{abnormal}\})=0\\ \end{array} \right. \tag{13}\]  
According to the D-S theory, the probability function values \(m(\omega_{normal})\) and \(m(\omega_{abnormal})\) of the evidence are obtained, namely, the basic credibility function, as shown in Formula (14). \[\left\{ \begin{array}{l}m(\omega_{normal})=\frac{m_{1}(\omega_{normal})m_{2 }(\omega_{normal})m_{3}(\omega_{normal})}{K}\\ m(\omega_{abnormal})=\frac{m_{1}(\omega_{abnormal})m_{2}(\omega_{abnormal})m_{ 3}(\omega_{abnormal})}{K}\\ \end{array} \right. \tag{14}\]  
\(K\) is called as the fusion index. The bigger the \(K\) value, the higher the support degree belonging to the same category. \[K=m_{1}(\omega_{normal})m_{2}(\omega_{normal})m_{3}(\omega_{normal})+m_{1}( \omega_{abnormal})m_{2}(\omega_{abnormal})m_{3}(\omega_{abnormal}) \tag{15}\]Implementation of the improved D-S theory algorithm. The basic principle of the algorithm is to use the method based on local credibility to perform the weighted correction. The classification probability is corrected by the confusion matrix generated previously.