# Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis
## 4 Results
### Markovian Analysis
Taking into account the finding that the auto-attractor nature of the short IBIs state exists for both the relaxation and the stress-inducing tasks and since stress has been shown to induce higher BR (therefore shorter IBIs), it would be interesting to further investigate whether the short IBI state, during which a subject produces relatively many blinks in sequence during the task, corresponds to an intra-task differentiation of stress levels, even for relaxation-inducing tasks. As a further step of investigating the Markov dynamics of states 1 and 2, we compared the percentages of difference between \(P_{ii}\) and \(P_{ii}^{(0)}\), i = 1,2 between neutral and stressful tasks in the various phases. A higher percentage of difference for one task compared to the other, for a given state, might provide an indication that the attractor nature of that state is stronger for the task where the percentage is greater. The aim of the investigation was whether there existed a trend across experiments and phases, \"linking\" attractor states to neutral or stressful tasks. According to the above rationale, in the social exposure phase and emotional recall phase (except from experiment 2), the percentage of difference between \(P_{11}\) and \(P_{11}^{(0)}\) is greater in neutral than in stressed tasks, while, conversely, in the stressful videos phase the percentage of the difference between \(P_{22}\) and \(P_{22}^{(0)}\) is greater in stressed than in neutral tasks. In the emotional recall phase the same occurs in the 1 st and 2 nd experiment. For the mental task phase and the stressful videos phase, in the 3 rd experiment, the percentage of difference between \(P_{11}\) and \(P_{11}^{(0)}\) and \(P_{22}\) and \(P_{22}^{(0)}\) is greater in neutral than in stressed tasks. Additionally, for the stressful videos phase, in experiment 2, the percentage of the difference between \(P_{11}\) and \(P_{11}^{(0)}\) is greater in neutral than in stressed tasks, while the percentage of the difference between \(P_{22}\) and \(P_{22}^{(0)}\) is greater in stressed than in neutral tasks. It was mentioned above that the rate of blink generation is increasing under stressed tasks, so the \"short\" attractor (#1) could be expected to be strongest in stressed tasks, while, in contradistinction, the \"long\" attractor (#2) is expected to be strongest in the neutral tasks. This expectation was not verified across all the experiments based on the Markovian Analysis presented above. In order to investigate the impact of the attractor's existence on the classification accuracy, further analyses were implemented. Specifically, for each phase, we selected the experiment that presented concurrently the strongest attractor values, as indicated by the percentage difference between values \(P_{11}\) and \(P_{11}^{(0)}\) (denoted \(dP_{11}(\%)_{s}\) in the following), as well as between \(P_{22}\) and \(P_{22}^{(0)}\) (denoted \(dP_{22}(\%)_{n}\) in the following), under stressed and neutral tasks, respectively, and then trained, per phase, the classifiers using only the subjects from the selected experiment. As far as the classifier structure and classification testing are concerned, the hyperparameters of the 1D CNN architecture and LSTM classifier remained the same. The nested cross-validation scheme was selected to \(3\times 5\) after trial-error investigation. The classification results are presented in Table 7.  
Specifically, in the social exposure phase under stressed tasks \(dP_{11}(\%)_{s}\) is strongest in experiments 1 and 3 (3%), while \(dP_{22}(\%)_{n}\) in the social exposure phase under neutral tasks is strongest in experiment 1 (79%). So the experiment for which both \(dP_{11}(\%)_{s}\) and \(dP_{22}(\%)_{n}\) reached their peaks was experiment 1. Therefore, the IBI sequences of experiment 1 were fed into the classifiers (CNN and LSTM). For the other phases, the