Authors:
A. Candelieri
1
;
F. Riganello
2
;
D. Cortese
2
and
W. G. Sannita
3
Affiliations:
1
S. Anna Institute and RAN - Research in Advanced Neurorehabilitation; Laboratory of Decision Engineering for Healthcare Delivery and University of Calabria, Italy
;
2
S. Anna Institute and RAN - Research in Advanced Neurorehabilitation, Italy
;
3
University of Genova; State University of New York, Italy
Keyword(s):
Vegetative State, Minimally Conscious State, Eye-tracking, Data Mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Clinical Problems and Applications
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Pattern Recognition and Machine Learning
;
Physiological Modeling
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Eye-tracking is defined as the “pursuit eye movement or sustained fixation that occurs in direct response to moving or salient stimuli”; it is a key descriptor of the evolution from the vegetative (VS) to the minimally conscious (MCS) state and predicts better outcome. In this study, several physiological parameters (such as heart beat, Galvanic Skin Response [GSR], Blood Volume Pulse [BVP], respiratory rate and amplitude) were recorded while a medical examiner searched for eye-tracking by slowly moving a visual stimulus horizontally and vertically in front of the subject. Seven patients in VS and 8 in MCS were studied. The Heart Rate Variability (HRV) was analyzed to obtain time and frequency descriptors. Different classification methods were adopted to search for a plausible relationship between the subject psycho-physiological state and observable eye-tracking to stimuli. The performance of different classifiers was computed as Balanced Classification Accuracy (BCA) and evaluated
through suitable validation technique. A Support Vector Machine (SVM) classifier provided the most reliable relationship: BCA mean was about 84% on fold cross validation and about 75% on an independent test set of 6 patients (3 VS and 3 MCS).
(More)