## 8 Experimental Results
### Performance Evaluation of Electrode Positions
#### 8.1.1 Classification Accuracy  
Precision, recall, F1-score, and support describe the performance of the model in each class label [22]. Precision indicates the number of true positives and the number of false negatives in each class label. However, precision is typically used along with recall metrics to define the ratio of positive instances correctly classified by the model. F1-score indicates the combination of both precision and recall, and support defines the event of each class that lies in a targeted class. For binary and multiclass classifications, Tables 3-5 provide precision, recall, F1-scores, and support metrics. The model's precision is lowest for the REM stage, and this, together with the similar recall score for REM, indicates that the classifier is unable to learn to recognize the REM stage. In its predictions, it is unable to precisely categorize every actual occurrence of REM. The NREM and Wake stages were predicted more accurately than the REM state. This may be due to the fact that they have a greater number count in the training data.  
It is apparent from the table that the accuracy of binary classification was 92% when NREM and REM stages were combined. It is surprising that, when a class with a large number of occurrences is combined with others, the classifier accuracy increased to 92%. Interestingly, the class imbalance affects the classification performance. It can apply a key effect on significance and accuracy, and on other performance metrics [23].  
Multiclass classification accuracy is lower than that of binary classification as shown in Table 3. The data complexity is increased with the addition of more classes. It is observed that the classification performance is affected negatively whether the data are balanced or not. We conclude that the multiclass problem cannot be simply solved by adding more classes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{SINGLE FC ELECTRODE} \\ \hline \multicolumn{6}{c}{NREM and REM} \\ \hline
**Classification Problem** & **Stages** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline \multirow{8}{*}{**Normal**} & NREM & 0.92 & 0.99 & 0.96 & 46,138 \\ \cline{2-6}  & REM & 0.60 & 0.14 & 0.22 & 4360 \\ \cline{2-6}  & Accuracy & \multicolumn{4}{c}{**0.92**} \\ \cline{2-6}  & REM & 0.63 & 0.12 & 0.21 & 4247 \\ \cline{2-6}  & Accuracy & \multicolumn{4}{c}{**0.85**} \\ \cline{2-6}  & NREM, REM, Wake & & & \\ \hline \multirow{8}{*}{**Normal**} & NREM & 0.82 & 0.91 & 0.86 & 46,230 \\ \cline{2-6}  & REM & 0.55 & 0.14 & 0.23 & 4307 \\ \cline{2-6}  & WAKE & 0.74 & 0.69 & 0.71 & 23,454 \\ \cline{2-6}  & Accuracy & \multicolumn{4}{c}{**0.79**} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance measures for binary and multiclass classifications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{SINGLE PC ELECTRODE} \\ \hline \multicolumn{6}{c}{NREM and REM} \\ \hline
**Classification Problem** & **Stages** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline \multirow{8}{*}{**Normal**} & NREM & 0.92 & 0.99 & 0.96 & 46,138 \\ \cline{2-6}  & REM & 0.60 & 0.14 & 0.22 & 4360 \\ \cline{2-6}  & Accuracy & \multicolumn{4}{c}{**0.92**} \\ \cline{2-6}  & \multicolumn{4}{c}{**NREM and Wake**} \\ \cline{2-6}  & NREM & 0.82 & 0.90 & 0.86 & 46,312 \\ \cline{2-6}  & WAKE & 0.75 & 0.60 & 0.67 & 23,374 \\ \cline{2-6}  & Accuracy & \multicolumn{4}{c}{**0.80**} \\ \cline{2-6}  & \multicolumn{4}{c}{**Wake and REM**} \\ \cline{2-6}  & WAKE & 0.87 & 0.98 & 0.92 & 23,455 \\ \cline{2-6}  & REM & 0.64 & 0.22 & 0.32 & 4344 \\ \cline{2-6}  & Accuracy & \multicolumn{4}{c}{**0.86**} \\ \hline \hline \multicolumn{6}{c}{NREM, REM, Wake} \\ \hline
**Normal**} & NREM & 0.80 & 0.88 & 0.84 & 46,158 \\ \cline{2-6}  & REM & 0.61 & 0.23 & 0.33 & 4362 \\ \cline{2-6}  & WAKE & 0.72 & 0.66 & 0.69 & 23,471 \\ \cline{2-6}  & Accuracy & \multicolumn{4}{c}{**0.77**} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance measures for binary and multiclass classifications.