## 5 Results  
A comparison between the classifiers described above, and the difference between the complete feature set and the reduced input set, is made. Table 4 shows the accuracy results. Results with the reduced feature set are similar to those achieved with all features, confirming that the feature reduction method neglected the redundant features and was able to preserve the relevant features able to distinguish STN-IN and STN-OUT classes. Actually, when using all 24 features, the average performance decreases due to redundancies and higher confusion among features. Thus, the rest of results focus on the reduced set of features (11 features). The results were individually analyzed for each patient. Despite each patient being anatomically different, Table 5 shows that the classification results ranged from 99% to 100%, except for patient 3, with 96%. Concerning the ROC curves, Figure 6 shows a very narrow area, with all classifiers having similar behavior. This is also supported by the confusion matrices shown in Figure 7. All classifiers achieved a similar accuracy, ranging from 99% to 100%. An analysis of features was also carried out in order to evaluate if spike-dependent (SD) or spike-independent (SIN) features have an impact on the result. For this analysis, the PNN classifier was used. Thus, a set of 3 and 7 SD features were randomly chosen, while keeping 14 SIN features. Then, four trials were made by using 3, 7, 14, and 17 SIN features with no SD features. Figure 8 shows the accuracy results. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**ML Classifier** & **KNNs** & **SVM** & **RF** & **DT** & **DA** & **PNN** \\ \hline The 11 features selected & 99.9 & 100.0 & 99.9 & 99.8 & 100.0 & 100.0 \\ All 24 features selected & 99.8 & 99.9 & 99.5 & 99.9 & 99.8 & 100.0 \\ \hline \end{tabular}
\end{table} 
Table 4: STN localization accuracy comparison between the reduced feature set and the complete feature set. As already obtained by the NCA feature reduction algorithm, it is clear that SIN features are more relevant for the correct detection. However, it is not necessary to use most of the SIN features since a reduced set provides successful classification ratios while reducing computational cost. As an example of the class separation provided by the chosen features, Figure 9 shows the scatter plot for \(Sk\) and \(SD\), showing that they can separate the STN-IN and STN-OUT classes. The remaining scatter plots are shown in Appendix C. Thus, scatter plots for the other selected features are also shown, which show a clear distinction. Some other features show overlapping areas, which means that they should be plotted in higher dimensions, they are non-linearly separable, or outliers exist. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Patient** & **Precision** & **Recall** & **Kappa** & **F1-Score** & **Accuracy** \\ \hline P1 & 100 & 100 & 86.8 & 100 & 100 \\ P2 & 100 & 100 & 87.0 & 100 & 100 \\ P3 & 86.9 & 95.1 & 87.7 & 90.1 & 96.3 \\ P4 & 100 & 99.9 & 84.9 & 99.9 & 100 \\ P5 & 96.6 & 99.6 & 87.9 & 98.0 & 99.5 \\ P6 & 99.8 & 99.8 & 84.4 & 99.8 & 99.9 \\ P7 & 99.8 & 99.8 & 84.8 & 99.8 & 99.9 \\ P8 & 99.8 & 99.9 & 88.4 & 99.3 & 99.6 \\ P9 & 98.8 & 99.9 & 88.4 & 99.3 & 99.6 \\ P10 & 99.6 & 99.5 & 81.2 & 99.5 & 99.6 \\ P11 & 99.8 & 99.8 & 80.8 & 99.8 & 99.8 \\ P12 & 99.7 & 100 & 86.1 & 99.8 & 99.9 \\ P13 & 99.7 & 100 & 86.1 & 99.8 & 99.9 \\ P14 & 99.5 & 99.9 & 87.0 & 99.6 & 99.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance indices individualized per patient using PNN classification and the reduced set of features. Figure 6: ROC curves for all the classifiers used in this work for STN-IN and STN-OUT area detection. A visualization of the feature space formed by the two features selected using NCA for STN target localization is shown in Appendix C. The spectral features show a clearer separation compared to other SIN features due to the use of band frequency characteristics.