## 3 Method
### Sinc-LSTM Network Structure  
The structure of Sinc-LSTM network is shown in Figure 2. Among them, the first layer of the hidden layer is composed of \(16\times 6=96\) Sinc function filters, and the LSTM network is composed of three LSTM one-way layers with 288 units. Layer normalization (LN) is applied to the Sinc filter layer and each LSTM layer to normalize their inputs and improve learning efficiency. The output of the LSTM flows through the dropout layer and the dense layer to prevent overfitting and is then output by the fully connected layer to complete the reconstruction of the sequence. The various layers in the network are closely coordinated with each other and have their own functions. The SincNet layer is responsible for extracting local features from the input signal by convolving it with a bank of Sinc filters. The resulting feature sequence is then passed on to the LSTM layer, which is able to capture the temporal dependencies in the input sequence and learn long-term dependencies between different features. The output of the SincNet layer is fed into the LSTM layer as a sequence of feature vectors, which are processed in a recurrent manner. Overall, the SincNet layer and LSTM layer work together to extract and model both local and global features from the input sequence, allowing for more accurate and robust predictions. In addition, LN normalizes the inputs to each LSTM layer independently, reducing the internal covariate shift and improving the stability and efficiency of the learning process. Then before output, the dropout layer randomly removes a fraction of the neurons during training, which reduces overfitting by encouraging the network to learn more robust features. In the following paragraphs of this subsection, a detailed description of the structure and principles of each module will be provided. **Sinc function filter layer. ** Since function filters are obtained from SincNet networks. The structure of the Sinc function filter is shown in Figure 3. SincNet was originally used to  
Figure 2: The internal structure of Sinc-LSTM. process speech signals. The shape of Sinc function filters is similar to the form of alternating peaks and troughs in 1D temporal data, thus providing a better fitting ability to 1D temporal data of speech signals. For time series, the standard CNN convolution formula is as in Equation (4), where \(x[n]\) is the speech signal, \(h[n]\) is the filter of length \(L\), and \(y[n]\) is the output of the filter. In the standard CNN structure, the \(L\) elements of each filter are learned from the data. The Sinc function filter is defined as a filter \(g[n,\theta]\), whose frequency domain characteristics are shown in Equation (5), where \(f_{L}\) and \(f_{H}\) are the low and high cutoff frequencies, respectively, which are learnable parameters in the filter. \[y[n]=x[n]*h[n]=\sum_{l=0}^{L-1}x[l]\cdot h[n-l] \tag{4}\]  
\[G[f,f_{L},f_{H}]=rect\left(\frac{f}{2f_{H}}\right)-rect\left(\frac{f}{2f_{L}}\right) \tag{5}\]  
The neural network regulates \(f_{L}\) and \(f_{H}\) by loss function so that the model can automatically adjust the band selected with the bandpass filter. Therefore, the model can extract more meaningful features and enhance the interpret ability of the network. As shown in Figure 3, in the Sinc function filter layer of the hidden layer, we set six Sinc function filters with different initial cutoff frequencies for the 16 channel data of the CMG to extract the feature information of different frequency bands on each channel. **LSTM Network. ** The training and weight update visualization of the LSTM network is shown in Figure 2, with each unit in each layer computing in parallel for each channel of the input. One update is performed at each time step until the individual channel data traversal is complete. The unit structure of the LSTM neural network is shown in Figure 4. The LSTM network consists of multiple memory cells that allow the network to capture long-term dependencies in the input sequence.