# Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis
## 5 Discussion  
The aim of this study is the investigation of blinks patterns as manifested in the IBI sequences produced during neutral and stress conditions, provided by experimental tasks related to social exposure, emotional recall and cognitive processes. The IBIs appear to follow log-normal distribution in the vast majority of experimental tasks for the three available datasets. The distributions corresponding to neutral and stress conditions appear to be different in 3 out of 4 experimental phases (social exposure, emotional recall and mental tasks). Especially, during the social exposure and emotional recall phases, the stress has an effect the concentration of significantly more short IBIs. This pattern seems to be consistent along the three different datasets, representing an interesting finding, indicating that during stress conditions there are increased blinks. Then, we explored classification of stressed and neutral tasks on different cognitive tasks using deep CNN architectures and Markov Chain. Although CNN has been applied widely on blink detection [29, 30], to our knowledge, it is the first time that inter-blink interval is analyzed with both CNN and Markov Chain models, with the aim of investigating both the existence of differentiation between the two stress-related categories of tasks and more in-depth state relationships as can be revealed by Markov Analysis. By combining data from all the experiments, the best CNN model in the present study achieved 76.4% testing accuracy for the mental tasks phase, while the LSTM network 79.5% for the same experimental phase. The use of Markovian Analysis, for selecting data from experiments where the \"attractor\" states were stronger improved notably the classification accuracy (81%). Future work would be the detection of the stress level using the IBI sequence, as many studies have been shown high accuracies on recognizing levels of psychological stress, e.g., stress level can be automatically recognized by analyzing ECG signals in a multi-modal fusion and respiratory signals [31, 32, 33] with high accuracy. Markov Analysis provided statistically significant indications that the blink generation is not purely random but depends on the history of previous blinks occurrences, implying the presence of memory in the system generating the blinks. We showed that, if the IBI sequence is modeled as a first-order Markov Chain, the generation of blinks during stressful and neutral cognitive possesses characteristics according to which the duration of a particular IBI depends significantly on the duration of the immediately previous IBI. If the occurrence of blinks were purely random, then one would expect that the occurrence of a given IBI would follow a renewal process, meaning that the IBI would be independent of previous IBIs. That was shown not to be the case, implying that the blink generation was not purely random. Moreover, the above findings were observed for the generation of blinks in all three experiments and four cognitive process for both tasks, neutral and stressed. Our detailed analysis of matrices P and \(P^{(0)}\), given the assumption of a first-order Markov process, provided further insight into the possible mechanism of blink generation. Given a short IBI, a tendency to repeat a short IBI was shown. Moreover, given a long IBI, a tendency to repeat a long IBI was shown well, albeit in a more nuanced way than the trend concerning short IBIs. Therefore, our analysis provided evidence that the system of blink generation may operate in two \"attractor\" states, one in which blinks occur at short consecutive IBIs and one in which blinks occur at long consecutive IBIs. Furthermore, we found that the short IBI attractor state characteristic existed in all cognitive tasks, suggesting that this mechanism does not depend on the particular task demands. Similar attractor behavior has been shown in a previous study [28], investigating the inter-saccadic intervals under fixation tasks.