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Acknowledgment to Reviewers of Signals in 2020
 
 
Article

Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis

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Department of Psychiatry and Psychotherapy, University Hospital Lübeck (UKSH), Ratzeburger Allee 160, 23538 Lübeck, Germany
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Institute of Computer Science, Foundation for Research and Technology Hellas, 70013 Heraklion, Greece
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Institute of AgriFood and Life Sciences, University Research Centre, Hellenic Mediterranean University, 71410 Heraklion, Greece
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Biomedical Engineering Department, University of West Attica, 12243 Athens, Greece
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Laboratory of Cognitive Neuroscience and Sensorimotor Control, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, University Mental Health, 10433 Athens, Greece
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Second Department of Psychiatry, Medical School, “ATTIKON” University General Hospital, National and Kapodistrian University of Athens, 15772 Athens, Greece
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Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
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School of Electrical and Computer Engineering, National Technical University of Athens, 15772 Athens, Greece
*
Author to whom correspondence should be addressed.
Signals 2021, 2(1), 55-71; https://doi.org/10.3390/signals2010006
Received: 16 September 2020 / Revised: 8 November 2020 / Accepted: 22 December 2020 / Published: 23 January 2021
(This article belongs to the Special Issue Biosignals Processing and Analysis in Biomedicine)
This paper investigates eye behaviour through blinks activity during stress conditions. Although eye blinking is a semi-voluntary action, it is considered to be affected by one’s emotional states such as arousal or stress. The blinking rate provides information towards this direction, however, the analysis on the entire eye aperture timeseries and the corresponding blinking patterns provide enhanced information on eye behaviour during stress conditions. Thus, two experimental protocols were established to induce affective states (neutral, relaxed and stress) systematically through a variety of external and internal stressors. The study populations included 24 and 58 participants respectively performing 12 experimental affective trials. After the preprocessing phase, the eye aperture timeseries and the corresponding features were extracted. The behaviour of inter-blink intervals (IBI) was investigated using the Markovian Analysis to quantify incidence dynamics in sequences of blinks. Moreover, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network models were employed to discriminate stressed versus neutral tasks per cognitive process using the sequence of IBI. The classification accuracy reached a percentage of 81.3% which is very promising considering the unimodal analysis and the noninvasiveness modality used. View Full-Text
Keywords: stress; blinks; eye activity; convolutional neural networks; CNN; Markovian Analysis; Inter Blink Interval; machine learning stress; blinks; eye activity; convolutional neural networks; CNN; Markovian Analysis; Inter Blink Interval; machine learning
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MDPI and ACS Style

Korda, A.I.; Giannakakis, G.; Ventouras, E.; Asvestas, P.A.; Smyrnis, N.; Marias, K.; Matsopoulos, G.K. Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis. Signals 2021, 2, 55-71. https://doi.org/10.3390/signals2010006

AMA Style

Korda AI, Giannakakis G, Ventouras E, Asvestas PA, Smyrnis N, Marias K, Matsopoulos GK. Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis. Signals. 2021; 2(1):55-71. https://doi.org/10.3390/signals2010006

Chicago/Turabian Style

Korda, Alexandra I., Giorgos Giannakakis, Errikos Ventouras, Pantelis A. Asvestas, Nikolaos Smyrnis, Kostas Marias, and George K. Matsopoulos. 2021. "Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis" Signals 2, no. 1: 55-71. https://doi.org/10.3390/signals2010006

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