## 3 Results
### Classification Results  
Figure 5 shows a box-plot of the real part of the estimated pole for the synthetic data set containing the simulated healthy and PPD groups over a period of time. The box plot shows the median, range, and quartiles of the population distributions. It is clear from the box plot that the real part of the estimated poles remains in the same range for simulated healthy individuals and increases for PPD over a significant period of time. Next, we apply machine learning algorithms to these data. To show the efficacy of the proposed method, we compare our results of SVM (incremental delay) with the proposed features (C1 and C2) with the results of the LSVM (incremental delay). The classification results for the simulated (incremental delay) data set with a noise variance of 0.01 are shown in Table 2 and those with a noise variance of 0.1 are shown in Table 3. It is seen from Tables 2 and 3 that the results of SVM with features C2  
Figure 4: Effect of noise variance on the estimation of poles using MPM: (a) change in poles due to change in delay (b) change in poles due to change in gain. are comparable with the results of LSVM. It is clear from Table 3 that the above results are almost unaffected by a noise variance of 0.1. Further, on repeating the above procedure with ten independently generated synthetic data sets, we find that the above results are reproducible. Note that all of the methods have a high sensitivity of above 96% and specificity of above 98%. In addition, they perform very well with the validation synthetic data set, with a sensitivity above 95% and specificity above 98%. All three approaches seem to perform well with this synthetic data set. With a further increase in the variance of the noise by 100%, the sensitivity and specificity only drop by a modest margin by about a dozen percentage points despite the big bump in the noise variance, and these numbers further improve if a longer duration is utilized. This is promising especially considering the much larger overlap between the healthy and PPD data in such a situation. Next, to demonstrate that the same method is effective when the source of instability is different from an increased delay, we consider the synthetic data set with a gradual increasing gain (as mentioned in Appendix A.1). With the C1 choice of features and the SVM method, we observe that the sensitivity is 96% and the specificity is 94.33% with the training synthetic data set (incremental gain), while for the synthetic validation data set (incremental gain), the sensitivity is 98% with a specificity of 93%. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Training Synthetic Data Set**} \\ \cline{2-6}  & **Sensitivity (\%)** & **Specificity (\%)** & **False Positive (\%)** & **False Negative (\%)** & **Error in Classification (\%)** \\ \hline
**LSVM** & 96.73 & 100 & 0 & 3.27 & 1.67 \\ \hline
**SVM (C1)** & 96.40 & 98.97 & 1.03 & 3.60 & 2.34 \\ \hline
**SVM (C2)** & 98.36 & 99.66 & 0.34 & 1.64 & 1 \\ \hline \multicolumn{6}{c}{**Validation Synthetic Data Set**} \\ \hline
**LSVM** & 97.42 & 100 & 0 & 2.58 & 1.25 \\ \hline
**SVM (C1)** & 95.36 & 99.51 & 0.49 & 4.64 & 2.5 \\ \hline
**SVM (C2)** & 98.96 & 99.02 & 0.98 & 1.04 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of classification results of the synthetic (incremental delay) data set with disturbance and measurement noise with zero mean and 0.01 variance, with the choice of features for C1 being the minimum of the percentage change rate in the real part of the characteristic value and the maximum of the percentage change rate in the successive difference and the choice of features for C2 being the statistical value of the slope and the constant of a straight line fit with data and the real part of characteristic value of the first trail as features.