# The Multi-Branch Deep-Learning-Based Approach to Heart Dysfunction Classification
## Appendix C Detailed Classification Metrics
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Class** & **Branch \(\mathbf{\alpha}\)** & **Branch \(\mathbf{\beta}\)** & **Branch \(\mathbf{\gamma}\)** & **Branch \(\mathbf{\delta}\)** & **Branch \(\mathbf{\epsilon}\)** & **Branch \(\mathbf{\zeta}\)** \\ \hline BBB & **0.597253** & 0.566419 & 0.563013 & 0.532307 & 0.553680 & 0.586041 \\ PR & 0.636354 & 0.578343 & **0.639206** & 0.550651 & 0.574360 & 0.598207 \\ PVC \(\mid\)VPB & 0.654703 & 0.700841 & 0.683498 & 0.672783 & **0.708444** & 0.643936 \\ LAD & 0.561885 & 0.516847 & **0.563233** & 0.536503 & 0.518118 & 0.526578 \\ RAD & 0.524549 & 0.485925 & **0.672749** & 0.637727 & 0.511582 & 0.500972 \\ RBBB \(\mid\)CRBBB & **0.912768** & 0.905233 & 0.668891 & 0.760903 & 0.911796 & 0.901102 \\ TInv & **0.582459** & 0.572995 & 0.501062 & 0.517745 & 0.564880 & 0.575673 \\ LQT & 0.527403 & 0.571438 & 0.563697 & 0.531343 & **0.590151** & 0.508965 \\ AF & 0.552105 & 0.535774 & 0.516796 & 0.538986 & 0.540597 & **0.58035** \\ AFL & 0.546212 & 0.541007 & 0.517290 & 0.526367 & 0.553671 & **0.584327** \\ CLBBB \(\mid\)LBBB \(\mid\)LBBB \(\mid\)O61665 & 0.584795 & **0.607222** & 0.554284 & 0.580440 & 0.583780 \\ QAb & **0.695842** & 0.637586 & 0.541663 & 0.577689 & 0.636144 & 0.680083 \\ TAb & 0.624878 & **0.643519** & 0.533651 & 0.557697 & 0.623960 & 0.570308 \\ LPR & 0.533071 & 0.518645 & 0.477432 & 0.501859 & 0.525226 & **0.533177** \\ LQRSV & 0.449438 & 0.462724 & 0.454474 & **0.467843** & 0.449351 & 0.443257 \\ IAVB & **0.527106** & 0.523633 & 0.477752 & 0.478633 & 0.523875 & 0.515661 \\ PAC & 0.542571 & 0.493563 & 0.504964 & 0.504925 & **0.604947** & 0.540788 \\ PRWP & 0.833240 & **0.841728** & 0.754121 & 0.766087 & 0.805014 & 0.840615 \\ SB & 0.402757 & 0.408576 & **0.442977** & 0.431250 & 0.399976 & 0.405371 \\ Brady & **0.549197** & 0.523406 & 0.488573 & 0.492513 & 0.543767 & 0.543974 \\ NSR & 0.305179 & 0.297668 & **0.385371** & 0.372845 & 0.240797 & 0.260344 \\ STach & 0.487214 & 0.469564 & **0.525710** & 0.435486 & 0.460380 & 0.488562 \\ SA & 0.691453 & **0.716436** & 0.588086 & 0.650014 & 0.709198 & 0.657213 \\ LAnFB & 0.478703 & 0.492784 & 0.457145 & 0.476290 & **0.482760** & 0.474585 \\ NSIVCB & **0.622468** & 0.610410 & 0.549982 & 0.540467 & 0.602240 & 0.579858 \\ IRBBB & 0.507146 & 0.503786 & 0.480981 & 0.480648 & **0.509905** & 0.506149 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **F2-Score** & **ROC AUC** & **Support** \\ \hline BBB & 0.101532 & 0.215214 & 0.136205 & 0.173574 & 0.543275 & 942.8 \\ PR & **0.386206** & 0.243268 & **0.295677** & 0.261496 & 0.597415 & 1683.4 \\ PVC1 VPB & **0.018211** & 0.741141 & **0.035541** & **0.082847** & **0.732057** & 102.6 \\ LAD & 0.004746 & 0.280139 & 0.099331 & 0.022194 & 0.529168 & 57.4 \\ RAD & 0.011326 & 0.063903 & 0.011978 & 0.032938 & 0.483921 & 251.2 \\ RBBB1 CRBBB & **0.424107** & 0.949389 & **0.601828** & **0.770135** & **0.934670** & 921.4 \\ TInv & **0.056053** & 0.419457 & 0.098779 & 0.182190 & 0.547519 & 661.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance metrics for architecture using LSTM 11x2 combining six branches. Average of five folds. Bolded results for metrics show when the larger six-branch architecture obtained a better result for the given metric than all of the one-branch architectures.