# The Multi-Branch Deep-Learning-Based Approach to Heart Dysfunction Classification
## 3 Experiments
### Results  
The experiments were performed on computational servers with a 24-core processor clocked at 3.2 GHz, 256 GB RAM clocked at 3.2 GHz, and four NVIDIA Quadro RTX 5000 ADA 32 GB GPUs. To thoroughly test the proposed approaches, we performed experiments in seven scenarios, which are described below. * Scenario A--a test of single-branch architecture with LSTM as the core network. Table 6 shows results for six different branches (different data inputs) tested separately. The core network was configured with two hidden layers, with seven neurons each. As can be seen, branches \(\gamma,\delta,\zeta\) perform worse than the baseline raw signal (\(\alpha\) branch), while branches \(\beta\) and \(e\) obtain better results. Figure 4: Diagnosis similarity matrix used by challenge score, proposed in [50]. * Scenario B--tests possible differences between core network architectures working on different branches. Single branch tests were performed for branches \(\alpha,\beta,\delta,\epsilon\), comparing LSTM, GRU, and N-BEATS architectures, and the results are provided in Table 7. Omitted in the experiment were the branches \(\gamma\), as in Scenario A, where domain knowledge alone proved to be inefficient, and \(\zeta\), because the \(\epsilon\) branch baseline drift removal worked better. Each of the core networks was configured with two hidden layers, with seven neurons each. An enhanced input signal window (3 s of signal) was used, and dropout layers were added. Dropout is a regularization technique for neural networks that prevents overfitting by randomly setting a subset of activations to zero during training, aiming to reduce co-adaptation of neurons and improve generalization [51]. Similarly to the experiment from scenario A, also in this scenario, the best results were achieved for the \(\beta\) and \(\epsilon\) branches. The obtained results were relatively consistent across all core networks, but it is worth noting that GRU tended to have the highest variance for branches, which gave the best maximal results. Also, the addition of a dropout layer (not used in Scenario A) and a larger signal window improved the LSTM network's results of the \(\alpha\) branch so that they were almost the same as for the \(\epsilon\), and for this network, only \(\beta\) gave much better results. * Scenario C--test comparing the multi-branch architectures of core networks. LSTM, N-BEATS, and GRU networks were used with a full six-branch architecture with a dropout layer and 5% pruning each epoch. Each of the core networks was configured with two hidden layers, with seven neurons each. The experiment shows that a full multi-branch architecture obtains better results for the 12-lead ECG signal (Table 8) than an architecture based on any of the single branches (Table 7), for each of the core networks. As can be seen, both GRU and LSTM achieved the same results in this scenario, with GRU being slightly faster and having more consistent results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Branch** & **Time [ms]** & **F1-Measure** & **AVG** & **STD** \\ \hline \(\alpha\) & 0.075 & 0.196 & 0.319 & 0.015 \\ \(\beta\) & 0.070 & **0.221** & **0.337** & 0.020 \\ \(\gamma\) & 0.073 & 0.156 & 0.269 & 0.005 \\ \(\delta\) & 0.092 & 0.174 & 0.285 & 0.032 \\ \(\epsilon\) & 0.074 & **0.204** & **0.327** & 0.005 \\ \(\zeta\) & 0.082 & 0.183 & 0.296 & 0.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Scenario A—Comparison of LSTM single-branch architecture for different inputs. 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.