## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Experiment** & **Exp \#** & **Acc** & **Sens** & **Spec** & **Prec** & **AUC** & \
\begin{tabular}{c} **Training** \\ **Time** \\ \end{tabular} & **Structure/Size** \\ \hline Group I—FFNN on MEG & 1 & 88.1 & 88.1 & 88.0 & 78.0 & 0.9410 & 51 min & Layer Sizes: [64 32 32 32 16] \\ segment values & 2 & 82.9 & 75.7 & 86.6 & 74.5 & 0.9029 & 1 h 50 min & Layer Sizes: [64 32 32 32 16] \\ \hline Group II—FFNN on & 3 & 91.7 & 90.8 & 92.2 & 85.7 & 0.9655 & 112 min & Layer Sizes: [32 16] \\ MEG STFFT values & 4 & 87.9 & 88.4 & 87.6 & 76.9 & 78 min & Layer Sizes: [64 32 32 16] \\ \hline Group III—FFNN on FFT & 5 & 87.6 & 83.1 & 90.3 & 83.1 & 0.9196 & 33 h 55 min & Layer Sizes: [64 32 16] \\ \hline Group III—FFNN on FFT & 6 & **91.9** & 93.1 & 91.2 & 86.4 & **0.9694** & 34 h & Layer Sizes: [64 32 16] \\ and MEG segment value & 7 & **91.9** & 88.4 & 92.5 & 87.1 & & 47 h & Layer Sizes: [64 32 32 16] \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experiment** & **Exp \#** & **Acc** & **Sens** & **Spec** & **Prec** & **AUC** &
\begin{tabular}{c} **Training** \\ **Time** \\ \end{tabular} & **Structure/Size** \\ \hline Group V—Multiple-Output 2 & 13 & 90.99–90.9 & 89.4 & 91.7 & 89.6 & 0.9660 & 4 h & 16 outputs \(\rightarrow\) [32 32 32] \\ Sequential FFNNs & 14 & 90.99–91.1 & 86.8 & 92.9 & 86.8 & 0.9680 & 4 h & 32 outputs \(\rightarrow\) [32 32 32] \\ \hline Group VI—Single-Output 2 & 15 & 91.9–92.5 & 94.1 & 91.4 & 86.8 & 0.9720 & 3 h & [64 32 32 16] \(\rightarrow\) [64 32 16] \\ Sequential FFNNs & 16 & 91.9–92.6 & 93.7 & 92.0 & 87.7 & 0.9636 & 3 h 43 min & [64 32 32 16] \(\rightarrow\) [64 32 16] \\ Sequential FFNNs & 17 & **91.9–92.65** & 93.3 & 92.3 & 88.2 & 0.9688 & 2 h & [32 32 16] \(\rightarrow\) [64 32 16] \\ \hline Group IX—Single Output & 18 & 91.73–91.5 & 93.1 & 90.6 & 85.5 & 0.9627 & 30 h & 5\(\times\) [32 16 8] \(\rightarrow\) 5 Inputs [32 16 8] \\ Parallel Configuration & 19 & 91.73–92.6 & 93.7 & 92.0 & 87.7 & 0.9651 & 30 h & 5\(\times\) [32 16 8] \(\rightarrow\) 521 Inputs [32 16 8] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative results of the experiments using various FFNNs and 1D-CNN. Bold numbers indicate the best results. All the attempts to combine the output of previously trained networks to the ST-FFT spectral values given as input to another FFNN and train the new model on an augmented input set resulted in at least a small increase in prediction accuracy, both with single-output pretrained FFNN (Exp. 13 and 14) as well as multiple-output pretrained FFNN (Exp. 15 and 16). Using a single-output pretrained FFNN with prediction accuracy 91.9% and feeding its output combined with the initial input to an untrained three-hidden-layer FFNN improves the final accuracy to 92.5% (Exp. 15). The accuracy was further improved when we repeated the procedure a second time. Feeding the output of the second three-hidden-layer FFNN to another untrained three-hidden-layer FFNN, again combined with the initial input, resulted in yet another small improvement in prediction accuracy to 92.6% (Exp. 16)  
A special attention should be paid to the last two experiment groups (Exp. 17, 18, and 19--Groups VIII and IX) where we used several pretrained networks to feed their output to a new FFNN. In experiments 18 and 19, we trained five FFNNs and tested them on the validation set, separately. The accuracy of each of these networks were: FFNN1 Acc = 91.18%, FFNN2 Acc = 91.18%, FFNN3 Acc = 90.81%, FFNN4 Acc = 91.73%, and FFNN5 Acc = 89.52%. The best performing model gave us 91.73% accuracy. After obtaining the output of the five pretrained models, we combined them in a single dataset along with the ST-FFT spectral values. This extended dataset was used to train a new three-hidden-layer FFNN. After testing the extended network on the validation set, we obtained a prediction accuracy of 92.6% on the validation set in the second experiment (Exp. 19), which is a 1.00% gain in the model's accuracy. In Exp.