# The Multi-Branch Deep-Learning-Based Approach to Heart Dysfunction Classification
## 3 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Branch**} & \multicolumn{4}{c}{**LSTM**} & \multicolumn{4}{c}{**N-BEATS**} & \multicolumn{4}{c}{**GRU**} \\ \cline{2-13}  & **Time** & **F1** & **AVG** & **STD** & **Time** & **F1** & **AVG** & **STD** & **Time** & **F1** & **AVG** & **STD** \\ \hline \(\alpha\) & 0.10 & 0.252 & 0.415 & 0.010 & 0.12 & 0.100 & 0.282 & 0.004 & 0.11 & 0.161 & 0.328 & 0.028 \\ \(\beta\) & 0.09 & 0.268 & **0.441** & 0.009 & 0.12 & 0.128 & **0.299** & 0.015 & 0.10 & 0.230 & **0.400** & 0.057 \\ \(\delta\) & 0.23 & 0.101 & 0.288 & 0.004 & 0.11 & 0.097 & 0.281 & 0.006 & 0.25 & 0.099 & 0.274 & 0.005 \\ \(\epsilon\) & 0.10 & 0.247 & **0.416** & 0.021 & 0.11 & 0.134 & **0.292** & 0.016 & 0.10 & 0.198 & **0.364** & 0.023 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Scenario B—tests possible differences between core network architectures working on different branches. The core network was configured with two hidden layers, with seven neurons each, and a dropout layer. Results are averages from tests on five folds, time for single window classification is in milliseconds, avg is the average challenge score, and std is the standard deviation for the challenge score. Best results are bolded in the table. * Scenario D--tests how core networks performed after size increase. The network from Scenario C is modified, and two hidden layers of size 7 were changed into two hidden layers of size 11. The same dropout layer and 5% pruning each epoch are used. As can be seen in Table 9, despite an increase in network size, the time needed for classification was almost the same, while both the F-measure and challenge score for the two networks (N-BEATS and LSTM) significantly improved. An interesting observation is the worsening of the results of the GRU network, which obtained slightly worse results. The same result was obtained when the experiment was repeated. A careful inspection of the network weights showed that the weights for two branches (\(\delta\) and \(\zeta\)) were practically zeroed within this architecture. * Scenario E--test prune and dropout strategies on the LSTM core network. The test is conducted on the multi-branch network with all six branches, and the results are shown in Table 10. The core network was configured with two hidden layers, with seven neurons each. The best results were achieved for two configurations, both with an added dropout layer, in one case without pruning, and in the other with 20% L1 regularization performed once. Other solutions, with the exception of one-time random 20% pruning, which considerably worsened the results, obtained the same or only slightly better results than the lack of pruning and dropout. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dropout** & **Prune** & **Time [ms]** & **F1-Measure** & **AVG** & **STD** \\ \hline NO & NO & 0.38 & 0.295 & 0.448 & 0.011 \\ YES & NO & 0.40 & 0.302 & **0.465** & 0.011 \\ YES & 5\% L1 each epoch & 0.41 & 0.294 & 0.456 & 0.017 \\ YES & 5\% Random each epoch & 0.41 & 0.288 & 0.440 & 0.023 \\ YES & 20\% L1 once & 0.40 & 0.301 & **0.465** & 0.011 \\ YES & 20\% Random once 1\% & 0.34 & 0.211 & 0.283 & 0.039 \\ YES & Decreasing each 2 epoch from 5\% to 1\% L1 & 0.43 & 0.296 & 0.459 & 0.009 \\ YES & Decreasing each 3 epoch from 5\% to 1\% L1 & 0.42 & 0.295 & 0.460 & 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Scenario E—Results of dropout and prune strategies testing on the multi-branch LSTM core network. Results are averages from tests on five folds, time for single window classification is in milliseconds, avg is the average challenge score, and std is the standard deviation for the challenge score. Best results are bolded in the table. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Time [ms]** & **F1-Measure** & **AVG** & **STD** \\ \hline LSTM & 0.40 & 0.294 & 0.456 & 0.017 \\ N-BEATS & 0.53 & 0.178 & 0.335 & 0.039 \\ GRU & 0.37 & 0.287 & 0.456 & 0.005 \\ \hline \hline \end{tabular}
\end{table}