Methodological Aspects Of Using A Wearable Eye Tracker In Differential Diagnostics Of Disorders Of Consciousness

Abstract Background: The differential diagnosis of disorders of consciousness (DOC) is an important element in prognostication, clinical decision making, and information to families. Diagnosis currently relies to a large extent on clinical assessment using standardized neurobehavioral rating scales. Interpretation of the patient’s responses may be difficult due to cognitive, linguistic, physical, and/or motor impairments associated with the brain injury. Analysis of eye movements using eye tracking technology has been suggested as a potential support in improving diagnostic accuracy.Methods: The aim of the study was to evaluate the feasibility of incorporating eye tracking in a selection of subtests in the Coma Recovery Scale Revised (CRS-R). A secondary aim was to develop a protocol for comparison of the clinician-assessed findings versus the findings based on the eye movement recording. Four adult patients with a persistent disorder of consciousness were assessed with four test items from the CRS-R while eye movements were recorded with a wearable eye tracker.Results: No major adverse reactions were observed suggesting likely patient acceptability. Calibration issues due to oculomotor dysfunction complicated the analysis. However, distinct eye movements were discernible from the recorded data and analysis of these gave findings considered to support clinical assessment. Feasibility and data analysis issues are discussed.Conclusions: Based on patient responses, and the finding that it was possible to record eye movement patterns even without calibration, we conclude that the method is feasible. Further study will help clarify the full potential but the option to record and replay responses in these easily fatigued patients where assessment sessions need to be brief is of great interest. The current hardware and software limitations can be overcome with manual data processing and analysis; however, significant developments in automizing data processing will be required for broader clinical application. Hardware adaptations and the calibration process appear to be important primary targets in this process.