## 4 Results  
The obtained results based on the previously mentioned evaluation metrics for each category and for each fine-tuning step are explained below. Starting with the statistical features, the resulting classification output is 83 TP's, 148 TN's, 87 FN's and 132 FP's. This result is fixed over the different test trials score-wise and patient-wise. In other words, the correctly classified patients are fixed over the different test trials because of using the linear hard margin SVM. For dynamic features, the resulting classification output considering only the dynamic features is 32 TP's, 247 TN's, 138 FN and 33 FP's. Despite the remarkable reduction in the number of TP's, 18 new TP's are recognised by the dynamic features that are not recognised by the statistical features, in addition to 116 new TN's. This result is again fixed over the different test trials score-wise and patient-wise. With both statistical and dynamic features, the classifier performance is improved slightly compared to only statistical features with 2.8% increment in the accuracy. As the resulting classification output after combining both categories is 85 TP's, 159 TN's, 85 FN and 121 FP's. Despite the weak performance at this stage, the correctly classified instances are fixed with each test trial. This means that extracted features at this level are able to discriminate clearly between the correctly classified patients. For physiological features, namely the \(PP\), the resulting classification output with exclusively the extracted statistical and dynamic features of \(PP\) is 45 TP's, 222 TN's, 125 FN's and 58 FP's. It is important to note that the investigated FN's at the earlier stage are correctly classified by the \(PP\) extracted features. However, adding the \(PP\) extracted features to both statistical and dynamic features provided the following results: 83 TP's, 118 TN's, 87 FN's and 162 FP's. The classification output of the different feature-categories combinations are shown in Figure 6(a). Moreover, feature extraction results are combined and depicted in Table 1.  
Before showing the results of the fine-tuning phase, we present the results of using the feature selection and ranking technique of automatic relevance determination (ARD) [28] based on backward selection method. The classification output of the ARD selected dimensions is 92 TP's, 218 TN's, 78 FN's and 62 FP's. For the fine-tuning phase, the results are depicted in Table 2 and Figure 6(b) in a cumulative way. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline
**Feature Combination** & **TP** & **TN** & **FN** & **FP** & **Sensitivity (\%)** & **Precision (\%)** & **F1-Score** & **Accuracy (\%)** \\ \hline Statistical (Stat) & 83 & 148 & 87 & 132 & 48.88 & 38.60 & 43.14 & 51.33 \\ Dynamic (Dyn) & 32 & 247 & 138 & 33 & 18.82 & 49.23 & 27.23 & 62.00 \\ Stat+Dyn & 85 & 159 & 85 & 121 & 50.00 & 41.26 & 45.21 & 54.22 \\ Physiological (Phy) & 45 & 222 & 125 & 58 & 26.47 & 43.69 & 32.97 & 59.33 \\ Phy+Stat+Dyn & 83 & 118 & 87 & 162 & 48.88 & 33.88 & 40.02 & 44.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature Extraction results. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline
**Cumulative** & & & & & & & & & \\
**Fine-Tuning Steps** & & & & & & & & & \\ \hline ARD & 92 & 218 & 78 & 62 & 54.12 & 59.74 & 56.80 & 68.89 \\ FT1 & 99 & 164 & 71 & 116 & 58.23 & 46.04 & 51.42 & 58.44 \\ +FT2 & 101 & 179 & 69 & 101 & 59.41 & 50.00 & 54.30 & 59.41 \\ +FT3 & 106 & 185 & 64 & 95 & 62.35 & 52.74 & 57.14 & 64.67 \\ +FT4 & 129 & 219 & 41 & 61 & 75.88 & 67.89 & 71.66 & 82.67 \\ +FT5 & 143 & 243 & 27 & 37 & 84.11 & 79.44 & 81.70 & 85.78 \\ +FT6 & 147 & 251 & 23 & 29 & 86.47 & 83.52 & 84.97 & 88.44 \\ +FT7 & 154 & 256 & 16 & 24 & 90.59 & 86.52 & 88.50 & 91.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Feature Fine-tuning results.