## 4 Discussion  
Using deep learning techniques to detect AF with varied length ECG signals, we believe that the analysis of the proposed model is necessary. According to the comparison of results in Table 4, for the same segmentation input scheme, the result of MCNN-ULSTM is slightly lower than the MCNN-BLSTM, which indicates that compared with LSTM, BLSTM can learn more spatial features and improve the classification performance. When compared with 1 BCNN-BLSTM, MCNN-BLSTM had better results, meaning that MCNN-BLSTM could fully use the feature information of short single-lead ECG recordings. The input length of 1 BCNN-BLSTM is fixed. When the signal length is larger than the set length, the model will lose information, mainly when the set input length is less than 30 s. When the signal length is less than the set length, zero padding will perform for the ECG signal until meeting the length of the input. However, too many zero-padding operations on ECG that do not satisfy the input length can increase the noise of the ECG signal and reduce the detection capability of AF, which is more noticeable when the input length of the model is set to 61 s. At this time, although all ECG signals are input into the model, the performance of the model is lower than MCNN-BLSTM, this is because there are too many zeros supplemented in the ECG signals. In MCNN-BLSTM, the MCNN module is first used to extract the feature information of short single-lead ECG recordings fully. Then, the feature information is integrated, and the BLSTM module strengthens the feature \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rhythm** & **PRE (\%)** & **REC (\%)** & **SPE (\%)** & **F1 (\%)** & **ACC (\%)** \\ \hline Normal & 78.15 & 62.42 & 97.22 & 69.41 & - \\ AF & 81.63 & 89.69 & 96.10 & 85.47 & - \\ Other & 90.41 & 91.91 & 77.18 & 91.15 & - \\ Avg. & 83.40 & 81.34 & 90.17 & 82.01 & 87.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification performance using MCNN-BLSTM (\(l=25\) s) on the China Physiological Signal Challenge 2018. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rhythm** & **PRE (\%)** & **REC (\%)** & **SPE (\%)** & **F1 (\%)** & **ACC (\%)** \\ \hline Normal & 88.10 & 94.41 & 81.08 & 91.14 & - \\ AF & 87.30 & 74.83 & 98.78 & 80.59 & - \\ Other & 81.69 & 73.01 & 73.43 & 77.11 & - \\ Noise & 66.67 & 66.67 & 98.97 & 66.67 & - \\ Avg. & 80.94 & 77.23 & 88.07 & 78.88 & 85.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification performance for Normal, AF, Other and Noise rhythm using MCNN-BLSTM (\(l=25\) s) on the PhysioNet/CinC Challenge 2017.  
information. Therefore, the model can achieve better classification performance compared with the other two models. As shown in Table 4, when we set the segment length \(l=25\) s, the accuracy of MCNN-BLSTM, MCNN-ULSTM, and 1 BCNN-BLSTM is 87.57%, 86.17%, and 82.84%, respectively. With the same segmentation, we obtained the time consumption of 6.63 ms, 6.85 ms, and 2.98 ms for the three models in predicting a sample, respectively. We found that the time consumption of MCNN-BLSTM and MCNN-ULSTM is higher than that of 1 BCNN-BLSTM by about 3-4 ms, the accuracy of MCNN-BLSTM achieves a higher accuracy of about 5% over 1 BCNN-BLSTM. Although our method takes longer time to predict, our classification performance is higher. To compare the effects of different input lengths on the performance and time consumption of the model MCNN-BLSTM, we calculated the time consumption required for the model to predict a sample at input lengths of 6 s, 25 s, and 30 s, which are 21.94 ms, 6.63 ms, and 7.18 ms, respectively. The model's accuracy is 82.96%, 87.57%, and 87.51%.