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 psychophysiological 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).
Candelieri, A., Riganello, F., Cortese, D., Sannita, W. (2011). Functional Status and the Eye - Tracking Response a Data Mining Classification Study in the Vegetative and Minimaly Conscious States. Intervento presentato a: HEALTHINF 2011 - International Conference on Health Informatics, Roma, Italy.
Functional Status and the Eye - Tracking Response a Data Mining Classification Study in the Vegetative and Minimaly Conscious States
Candelieri, A;
2011
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 psychophysiological 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).
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