Functional Status and the Eye-tracking Response - A Data Mining Classification Study in the Vegetative and Minimaly Conscious States

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). 1 BACKGROUND & RATIONALE Eye-tracking, the pursuit eye movement or sustained fixation that occurs in direct response to moving or salient stimuli (Vanhaudenhuyse, Schnakers, Brédart and Laureys, 2008), it is usually observed in 20% and 82% of subjects in the vegetative (VS) and minimal conscious (MCS) states, respectively (Giacino, Zasler, Katz, Kelly, Rosenberg, and Filley, 1997; Royal College of Physicians, 1996; Schnakers, Vanhaudenhuyse, Giacino, Boly, Majerus, Moonen and Laureys, 2009).). It is a key descriptor of the evolution from VS to MCS. We retrospectively observed eye-tracking in 73% of 395 patients in a vegetative state, referred to the S. Anna RAN Institute from intensive care, neurological or neurosurgery units in the years 19982008. These 395 patients could be clustered by etiology of brain damage in 3 different groups: posttraumatic (n=248), vascular (n=119) or anoxichypoxic (n=28). Eye-tracking was already observed within 50 days from brain injury in about 50% of posttraumatic and vascular subjects and in 20% of anoxic-hypozxic patients. After 230 days, eyetracking had re-appeared in about 90% of posttraumatic and vascular subjects and in 67% of anoxic patients. Subjects with early recovered eyetracking had a better outcome at discharge or at the 138 Candelieri A., Riganello F., Cortese D. and G. Sannita W.. FUNCTIONAL STATUS AND THE EYE-TRACKING RESPONSE A Data Mining Classification Study in the Vegetative and Minimaly Conscious States. DOI: 10.5220/0003128201380141 In Proceedings of the International Conference on Health Informatics (HEALTHINF-2011), pages 138-141 ISBN: 978-989-8425-34-8 Copyright c 2011 SCITEPRESS (Science and Technology Publications, Lda.) end of follow-up (Spearman nonlinear correlation coefficient=-0.365, p-value<0.001). In this respect, eye-tracking proved an efficient predictor of outcome also in a study assessing at regular time intervals the presence/absence of 21 neurological signs. A data mining decision tree model identified eye-tracking as the best predictor of favorable outcome in the vegetative state (Dolce, Quintieri, Serra, Lagani and Pignolo, 2008; Pignolo, Riganello, Candelieri and Lagani, 2009). We also searched for eye-tracking at different times over the day (3 observations in the morning and 3 in the afternoon) in subjects in VS (n=9) or MCS (n=13). Eye-tracking was observed at any time during the day in 62% of MCS subjects, and never in 67% of VS patients. About 33% of subjects in VS presented eye-tracking at least once in the day, while 38% of subjects in MCS never showed it. These percentages are consistent with the reported rate of misdiagnosis between VS and MCS and suggest that eye-tracking may depend on the subject’s psychophysiological condition to occur (submitted). We decided to test the relationship between eyetracking and the physiological condition as characterized by Heart Rate Variability (HRV) analyses. HRV is an emerging objective measure of the continuous interplay between sympathetic and parasympathetic subsystems (Task Force of European Society of Cardiology and North American Society of Pacing and Electrophysiology of Circulation, 1996) and provides information on complex brain activation as well (Dolce, Riganello, Quintieri, Candelieri and Conforti, 2008; Riganello, Quintieri, Candelieri, Conforti and Dolce, 2008; Appealhans and Luecken, 2006, Friedman, (2007) Kreibig, 2010). In previous studies on controls, brain injured conscious patients and subjects in a vegetative state we obtained evidence f a correlation between the response to external stimuli and HRV (mainly expressed by the normalized low-frequency [0.04-0.15 Hz] band power descriptor nuLF) (Riganello, Pignolo, Lagani and Candelieri, 2009; Riganello and Candelieri, 2010; Riganello, Candelieri, Quintieri, Conforti and Dolce, 2010). In this respect, our working hypothesis is that a subject’s physiological status can be (partially) described by the HRV parameters and that a consistent response, in our case an eye-tracking, may depend on its variations. 2 MATERIALS & METHODS Eye-tracking was searched for in 9 and 13 patients in VS and MCS, respectively. Three different visual stimuli, namely a mirror, a green light and a bright red ball were used. The test was repeated several times for each subject in the absence of indications of sleepiness, stress, pain or discomfort. During this procedures, several physiological parameters were recorded (Nexus-10 device, Mind Media BW, Roermond-Herten, NL): Galvanic Skin Response (GSR), respiratory rate and amplitude, heart rate, coherence between the heart and respiratory rates, blood volume pulse (BVP), and the heart rate variability normalized band power and peak frequency in the low frequency interval (nuLF and peakLF). A dataset including 220 test conditions (also including the stimulus used to elicit an eyetracking response and clinical condition [VS or MCS]) was built for the data mining classification task. Data Mining classification approaches were to identify the fuctional condition that best correlated with the observation of eye-tracking. The established updated data mining techniques provided by WEKA (Waikato Environment for Knowledge Analysis) open-source software were used in the classification task (Witten and Eibe, 2005). Decision Trees, Rulebased Learning algorithm (OneR, Ridor and JRip) and Support Vector Machines (SVM) were used. A Chi-Squared Feature Selection method was used to rank the study variables based on their correlation with the class value (presence and absence of eye-tracking). Such an approach usually improves the classifiers performance and should provide medical experts with information on the physiological parameters facilitating eye-tracking. The classifiers’ reliability was evaluated by the Balanced Classification Accuracy (BCA) computed as the mean of correct classifications among classes. In addition, all instances related to a single patient were entered into a separate fold; the training was performed on all remaining folds and the extracted model was tested on the fold left apart. Such a cross validation procedure (repeated for each patient and comparable to the leave-one-out validation) avoids over-fitting, dependency by the patient related information, circularity in the analysis (double dipping) (Kriegeskorte, Simmons, Bellgowan and Baker, 2009), and estimates the reliability of the extracted criteria.