# Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis
## 4 Results
### Classification Performance  
In Table 5, we depict the prediction models performance in discriminating stressed and neutral states, for each experimental phase using the sequence of IBIs or blinks sequences as input to the CNN and LSTM networks. 8 classifiers (4 CNN and 4 LSTM) were implemented, one for each experimental phase, comparing stressed and neutral tasks per cognitive process, the parameters of which are presented in Section 3.4. The IBI sequence per subject is fed into the network architecture. For the CNN classifier, the true positive rate (Recall) is greater than the positive predictive value (Precision). The opposite is observed for the LSTM classifier. Comparing the range of values across phases for the two classifiers, for the Precision and Recall metrics, the difference in performance for the Precision is most notable, the range for the CNN classifier being [49,3,40,41,42,43,44,5], while for the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Phase** & **Two-Sample Log-Normal Z-Score** & \(\mathbf{p}\)**-Value** & **Z-Test** \\ \hline Social Exposure & 21.84 & \textless{}0.01 & \(\downarrow\) \\ Emotional recall & 9.49 & \textless{}0.01 & \(\downarrow\) \\ Mental tasks & 2.33 & \textless{}0.01 & \(\downarrow\) \\ Stressful videos & 1.48 & \textgreater{}0.05 & ns \\ \hline \hline \end{tabular} \(\downarrow\)/ns are significantly decreased and no significant changes during 2-sample Z-test.  
\end{table} 
Table 4: Two-sample log-normal Z-test for each experimental phase utilizing all the 3 available datasets. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Experimental Task** & **Fitted Distribution** & \
\begin{tabular}{c} **Parameters** \\ \(\mu\) \\ \end{tabular} &
\begin{tabular}{c} **Kolmogorov X\({}^{2}\) Test** \\ \(\sigma\) **or \(\lambda\)** \\ \end{tabular} \\ \hline
**Social Exposure** & & & & \\ Neutral (reference) & lognormal & 0.54 & 0.74 & \(\downarrow\) \\ Stress (Interview, text reading) & lognormal & 0.22 & 0.68 & \(\downarrow\) \\ \hline
**Emotional recall** & & & & \\ Neutral (reference) & lognormal & 0.65 & 0.77 & \(\downarrow\) \\ Stress (recall anxious/stressful & lognormal & 0.39 & 0.76 & \\ \hline
**Mental tasks** & & & & \\ Neutral (reference) & loglogistic & 0.59 & 0.80 & \(\downarrow\) \\ Stress (SCWT, images) & lognormal & 0.54 & 0.43 & \(\downarrow\) \\ \hline
**Stressful videos** & & & & \\ Calming video (reference) & lognormal & 0.63 & 0.89 & ns \\ Stress (stressful videos) & lognormal & 0.60 & 0.87 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Best Fitted distribution and corresponding parameters (\(\mu\), \(\sigma\) or \(\lambda\)). LSTM classifier being [80.6-99.0]. Having in mind that a \"positive\" classification result corresponds to classifiying a recording as belonging to a stressful task, while a \"negative\" classification result corresponds to classifying a recording as belonging to a neutral task, the results provide an indication that LSTM performs better than CNN when presented with recordings belonging to neutral tasks. Figure 4: The blue line represents the kernel density estimator of the distribution of IBIs. The red line represents the second derivative of the kernel density estimator. The threshold for dividing the two states (short and long IBIs) is taken to be the IBI value (indicated by the square) that corresponds to the largest peak in the second derivative curve (indicated by square), which is to the right of the maximum of the kernel density curve. (a–c) correspond to the 1 st, 2 nd and 3 rd experiment, respectively. Furthermore, the best classification accuracy results are achieved for the mental tasks phase for both the CNN and the LSTM classifier, closely followed by the emotional recall phase for the CNN and the social exposure phase for the LSTM classifier. These findings might provide in turn indications that the blink generation under these processes/phases has different patterns.