## 4 Discussion  
An ECG-SPP-net was developed for the heartbeat classification in this work. Heartbeats were filtered between 0.1 to 100 Hz and digitized at 360 Hz. Then, the ECG signals were filtered with wavelet \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ground Truth**} & \multicolumn{6}{c}{**Classification Result**} \\ \cline{2-7}  & **N** & \(\backslash\) & **A** & **V** & **L** & **R** & **Accuracy** \\ \hline N & 1794 & 1 & 1 & 0 & 0 & 4 & 99.7\% \\ \(\backslash\) & 0 & 1077 & 0 & 0 & 7 & 1 & 99.26\% \\ A & 43 & 2 & 530 & 63 & 37 & 69 & 71.24\% \\ V & 0 & 6 & 13 & 1921 & 31 & 32 & 95.9\% \\ L & 0 & 1 & 1 & 5 & 2402 & 12 & 99.2\% \\ R & 0 & 0 & 4 & 1 & 6 & 1764 & 99.38\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of the heartbeat classification results for the testing beats for the proposed method. Figure 6: The comparison of accuracies among the three network structures. For each method, we repeated the simulation for each network structure 10 times. The bar is the mean of the accuracy of each method for 10 times. The error is the standard deviation of the accuracy of each method for the experiments repeated 10 times. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Against to** & **Probability of Accept** \\ \hline Method 1 & Proposed method & \(1.15\times 10^{-5}\) \\ Method 2 & Proposed method & \(1.72\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: [PERSON] rank sum test results of each method against the proposed method. denoising and segmented into heartbeats with the proposed segmentation method. Each heartbeat contained 200 to 400 sample points. After that, the heartbeat was normalized between 0 to 1 before entering the ECG-SPP-net. Such a preprocessing method could reserve all the information of heartbeat without any distortion, and this was beneficial for the classification accuracy [30; 31; 32]. In addition, the influence of the number of the feature image of each convolutional layer to the classification was also considered. The numbers of the feature image for the first convolution layer and the second convolution layer were set to be 6 and 12, respectively. Assuming that a heartbeat contained 300 sample points, and then a three-level pyramid pooling (1 \(\times\) 1, 1 \(\times\) 2, and 1 \(\times\) 4) was adopted. Before sending the input to the fully-connected layer, 84 (12 \(\times\) 7 \(\times\) 1) feature values were acquired, which accounted for 28% of the input data (300 sample points). In Ref. [33], 2400 (150 \(\times\) 4 \(\times\) 4) feature values were achieved, accounting for 45% of the input data (73 \(\times\) 73 sample points) before entering the fully-connected layer. Such a setting could achieve a high accuracy of classification of the heartbeats in a relatively short running time of training. The level of pyramid pooling was also considered in this work. For example, the four-level pyramid pooling (1 \(\times\) 1, 1 \(\times\) 2, 1 \(\times\) 4, and 1 \(\times\) 8) have been tried in this study. Such a setting increased the training time dramatically with less improvement of classification accuracy. There are two merits for the proposed design. First, the network allows the entry of different-sized data to the CNN based neural network. Such different-sized data can be trained over a single network to enable weight sharing and avoid the complex operations of multiple network switching [18]. Second, the proposed method is designed on the basis of the CNN, which acts as a feature extractor for simplifying the feature extraction procedure. For some traditional classification methods, the feature extraction of the effective signal should consider many factors including the training and testing performance [12]. In this work, the solely concerned work is the structure of the CNN based network. Although ECG-SPP-net possesses an advantage of extracting quality features automatically, future research is necessary to address some shortcomings.