Automatic identification of oculomotor behavior using pattern recognition techniques

dc.contributor.author	Κορδά, Αλεξάνδρα Ι.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Σμύρνης, Νικόλαος Π.	el
dc.date.accessioned	2015-06-07T06:14:15Z
dc.date.available	2015-06-07T06:14:15Z
dc.date.issued	2015-06-07
dc.identifier.uri	http://hdl.handle.net/11400/15383
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.sciencedirect.com/science/article/pii/S0010482515000803	en
dc.subject	Blinks
dc.subject	Neural network
dc.subject	Αντιδράσεις
dc.subject	Νευρωνικό δίκτυο
dc.title	Automatic identification of oculomotor behavior using pattern recognition techniques	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.identifier.secondary	doi:10.1016/j.compbiomed.2015.03.002
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2015-05
heal.bibliographicCitation	Korda, A., Asvestas, P., Matsopoulos, G., Ventouras, E. and Smyrnis, N. (May 2015). Automatic identification of oculomotor behavior using pattern recognition techniques. Computers in Biology and Medicine. 60. pp. 151-162. Elsevier Ltd: 2015. Available from: http://www.sciencedirect.com/science/article/pii/S0010482515000803 [Accessed 11/03/2015]	en
heal.abstract	In this paper, a methodological scheme for identifying distinct patterns of oculomotor behavior such as saccades, microsaccades, blinks and fixations from time series of eye׳s angular displacement is presented. The first step of the proposed methodology involves signal detrending for artifacts removal and estimation of eye׳s angular velocity. Then, feature vectors from fourteen first-order statistical features are formed from each angular displacement and velocity signal using sliding, fixed-length time windows. The obtained feature vectors are used for training and testing three artificial neural network classifiers, connected in cascade. The three classifiers discriminate between blinks and non-blinks, fixations and non-fixations and saccades and microsaccades, respectively. The proposed methodology was tested on a dataset from 1392 subjects, each performing three oculomotor fixation conditions. The average overall accuracy of the three classifiers, with respect to the manual identification of eye movements by experts, was 95.9%. The proposed methodological scheme provided better results than the well-known Velocity Threshold algorithm, which was used for comparison. The findings of the present study indicate that the utilization of pattern recognition techniques in the task of identifying the various eye movements may provide accurate and robust results.	en
heal.publisher	Elsevier Ltd	en
heal.journalName	Computers in Biology and Medicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true