Τίτλος:
Automatic identification of oculomotor behavior using pattern recognition techniques
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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[U+05F3]s angular displacement is presented. The first step of the proposed methodology involves signal detrending for artifacts removal and estimation of eye[U+05F3]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. © 2015 Elsevier Ltd.
Συγγραφείς:
Korda, A.I.
Asvestas, P.A.
Matsopoulos, G.K.
Ventouras, E.M.
Smyrnis, N.P.
Περιοδικό:
Computers in Biology and Medicine
Εκδότης:
Elsevier Ireland Ltd
Λέξεις-κλειδιά:
Automation; Behavioral research; Biomedical signal processing; Classification (of information); Neural networks; Nitrogen fixation; Pattern recognition; Velocity, Artificial neural network classifiers; Automatic identification; Blinks; Manual identification; Microsaccades; Pattern recognition techniques; Training and testing; Velocity threshold, Eye movements, adult; algorithm; Article; artifact; artificial neural network; automation; clinical practice; cognition; comparative study; controlled study; eye fixation; eye movement; eyelid reflex; human; information processing; male; mathematical analysis; pattern recognition; priority journal; saccadic eye movement; task performance; time series analysis; visual field; adolescent; automated pattern recognition; physiology; reproducibility; saccadic eye movement; signal processing; statistical model; young adult, Adolescent; Adult; Algorithms; Artifacts; Eye Movements; Humans; Male; Models, Statistical; Neural Networks (Computer); Pattern Recognition, Automated; Reproducibility of Results; Saccades; Signal Processing, Computer-Assisted; Young Adult
DOI:
10.1016/j.compbiomed.2015.03.002
