[Clinical and functional assessment of the vestibular nerve]

How a Subclinical Unilateral Vestibular Signal Improves Binocular Vision

The present study aimed to determine if an infra-liminal asymmetric vestibular signal could account for some of the visual complaints commonly encountered in chronic vestibular patients. We used infra-liminal galvanic vestibular stimulation (GVS) to investigate its potential effects on visuo-oculomotor behavior. A total of 78 healthy volunteers, 34 aged from 20 to 25 years old and 44 aged from 40 to 60 years old, were included in a crossover study to assess the impact of infra-liminal stimulation on convergence, divergence, proximal convergence point, and stereopsis. Under GVS stimulation, a repeated measures ANOVA showed a significant variation in near convergence (p < 0.001), far convergence (p < 0.001), and far divergence (p = 0.052). We also observed an unexpected effect of instantaneous blocking of the retest effect on the far divergence measurement. Further investigations are necessary to establish causal relationships, but GVS could be considered a behavioral modulator in non-pharmacological vestibular therapies.

Prediction of a state of a subject on the basis of a stabilogram signal and video oculography test

Postural stability decreases with ageing and may lead to accidental falls, isolation and a reduction in the quality of life. The age at the onset of postural derangement, its extent and the reason for deterioration are poorly known within an individual, but in general it becomes more severe with age. In order to prevent falls and avoid severe injuries the postural derangement has to be noticed by the person and the possible nursing personnel. In this work we propose such numerical features, which can discriminate the persons having good or poor postural stability. These features can also be utilized to measure the outcome and progression of balance training. With these postural stability algorithms providing stability features for a subject we managed to classify correctly the type of stance on the force platform in more than 80% of sixty subjects. We used k-nearest neighbor algorithm as an intuitive baseline method and compared its results with those of support vector machines and hidden Markov models.