## 5 Results
In addition, a nonlinear feature provides better detection of the relevant spiking activity of STN neurons and reduces sensitivity to outliers. Then, statistical features are ideal for capturing linear or periodic variation, and the Cumulative bispectrum is ideal for detecting irregular spiking activity induced along STN borders. Figure 7: Confusion matrix for the ML classifiers. (**a**) KNNs. (**b**) SVM. (**c**) RF. (**d**) DT. (**e**) DA. (**f**) BNN. (**g**) FNN. (**h**) PNN. Previous studies have shown that MER signals are non-stationary and that STN borders can be modeled as high-background electric activity zones with some artifacts, where the number of firing spikes depends on the area zone of the basal ganglia [19; 36]. These observations about background patterns have already been demonstrated in clinical trials where the surgeon can discriminate among basal ganglia areas by listening to the audio variation that occurred. This is related to the frequency distribution of the signal. Figure 10 shows an example of spectrum energy representations (STFT) from the STN  
Figure 8: Comparison of STN area detection accuracy with PNN by using different combinations of spike-dependent (SD) and spike-independent features (SIN). Figure 9: Scatter plot of Skewness (\(Sk\)) and Standard Deviation (\(SD\)) features extracted from MER segments (patient 1). STN-IN and STN-OUT classes are mostly separated. IN and STN-OUT areas: an irregular distribution variation in STN-IN, and a regular distribution variation in STN-OUT exists. Our study is compared with recent existing works. The main difference between the accuracy of the proposed model and other works on the STN target localization is based on the data settings, preprocessing step, the type of handcraft features, and the classifier chosen. The comparison results are shown in Table 6. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **\# Patients** & **\# Features** & **Classifier** & **Training** & **Accuracy (\%)** \\ \hline [PERSON] et al. [37] & 27 & 13 SD and SIN & Fuzzy clustering & Hold-out & 89.6 \\ \hline [PERSON] et al. [38] & 48 & 3 SD and SIN & Unsupervised & NA & 88.0 \\ \hline [PERSON] et al. [36] & 17 & 3 SD and SIN & Bayesian, KNNs, and KNNs-DTW & LOOCV & 89.6 \\ \hline [PERSON] et al. [39] & 4 & 6 SIN & SVM & 10-fold CV & 99.4 \\ \hline [PERSON] et al. [40] & 4 & 3 SD and SIN & Bayesian and KNNs & NA & 85.0 \\ \hline [PERSON] et al. [18] & 26 & 13 SD and SIN & Logistic regression & 10-fold CV & 84.0 \\ \hline [PERSON] et al. [41] & 8 & 15 SD and SIN & KNNs and Fuzzy KNNs & LOOCV & 72.0 \\ \hline [PERSON] et al. [42] & 15 & 16 SD and SIN & KNNs & 10-fold CV & 86.1 \\ \hline [PERSON] et al. [43] & 81 & 2 SIN & SVM and HMM & 10-fold CV & 94.0 \\ \hline [PERSON] et al. [44] & 14 & 18 SD and SIN & KNNs & LOOCV & 94.4 \\ \hline [PERSON] et al. [14] & 26 & 1 SIN & Random Forest & LOOCV & 83.0 \\ \hline [PERSON] et al. [45] & 50 & 1 SIN &
\begin{tabular}{} \end{tabular} & k-means clustering and SOM & k-CV & 80.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of algorithms used for STN detection: authors, number of patients used, number of features, classifier, training method, and accuracy result. Figure 10: Spectrum energy plot (STFT) for 12,000 randomly selected MER segments in STN-IN (**a**) and STN-OUT (**b**).