# A Novel 1-D CCANet for ECG Classification
## 4 Experiments
### Results and Discussion  
Table 6 shows the results obtained for various classification methods. It includes the previously described model, variations of it (e.g., without adding the time-domain features), the 1-D ResNet and the best dual-leads method in the state of the art [20]. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Output Size** & & **Layers** \\ \hline MIT-BIH & INCART & Initial Layer \\ \hline \multirow{6}{*}{\(184\times 16\)} & & Conv (kernel size = 7, stride = 1, padding = 3) \\  & & BatchNorm \\  & & ReLU \\ \(184\times 16\) & \(132\times 16\) & MaxPool (kernel size = 5, stride = 2, padding = 0) \\ \hline \(184\times 32\) & \(132\times 32\) & ResBlock (kernel size = 5, stride = 1, padding = 2) \\ \hline \(184\times 64\) & \(132\times 64\) & ResBlock (kernel size = 5, stride = 1, padding = 2) \\ \hline \(184\times 128\) & \(132\times 128\) & ResBlock (kernel size = 5, stride = 1, padding = 2) \\ \hline \(184\times 256\) & \(132\times 256\) & ResBlock (kernel size = 5, stride = 1, padding = 2) \\ \hline \(1\times 256\) & \(1\times 256\) & AvgPool \\ \hline \(15\) & \(7\) & Fully Connected Network \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Architecture of 1-D ResNet with the parameters for the initial layer and the convolutions of each residual block. Our method and [20] demonstrated comparable performances on the MIT-BIH database, though our overall accuracy and mean ppv were better by around 0.3%. As for the INCART database, our results proved to be better, especially the overall accuracy (+1.69%), mean sensitivity (+2.89%), and mean ppv (+1.78%). Contrary to [20], our approach is purely one-dimensional, allowing to explore a more complex version of CCANet while maintaining a reasonable computational complexity and providing better results: we opted for 4 layers and stacking features extracted after each convolution gave better results than without doing so (see seventh method of Table 6), especially increasing the sensitivity. Using frequency features with the one-dimensional spectrogram helped obtain a better classification by notably increasing the sensitivity (+1.12% for INCART) and the ppv (+1.3% for INCART). The addition of the AR coefficients and the time-domain features contributed to slightly increase the performance of our model. The performances were significantly better when using our CCA-SVD filter extraction technique instead of the CCA technique described in [20], with a sensitivity gaining more than 3% for INCART (see the third method of Table 6). Finally, our method provided significantly better results than the 1-D ResNet approach (+3.64% for overall ACC for MIT-BIH, +9.45% for INCART). Our analysis of the correlations of the two leads, using SVD, proved to be a good way of recognizing the various types of heartbeats. Tables 7 and 8 show the comparison between the best of our proposals and similar works in the state of the art for MIT-BIH and INCART databases respectively. In the case of MIT-BIH, Table 7 confirms that the use of dual-leads-based approaches brings improvements in performance (more than 1%). Also in the case of INCART we see an improvement with respect to single-lead-based method (more than 3%). Here, the proposed approach is slightly better than a variant of the work by [PERSON] et al. [20] that uses three leads. Although our approach explores more complex structures with respect to [PERSON] et al. [20], it remains comparable, in terms of computational cost, with it. The inference time for each heart beat classification is about 0.05 s while in the case of [PERSON] et al. [20], it is about 0.02 s.