A computational framework to simulate the endolymph flow due to vestibular rehabilitation maneuvers assessed from accelerometer data

Study the biomechanical performance of the membranous semicircular canal based on bionic models

A BA (bionic ampulla) was designed and fabricated using an SMPF (Symmetric electrodes Metal core PVDF Fiber) sensor, which could imitate the sensory hair cells to sense the deformation of the cupula of the BA. Based on the BA, a bionic semicircular canal with membrane semicircular canal (MBSC) and a bionic semicircular canal without membrane semicircular canal (NBSC) were designed and fabricated. The biomechanical models of the MBSC and NBSC were established. The biomechanical models were verified through the perception experiments of the MBSC and the NBSC. The results showed that the SMPF could sense the deformation of the cupula. The MBSC and NBSC could sense the angular velocity and accelerations. What's more, it was speculated that in a human body, the endolymph probably had a function of liquid mass while the membranous semicircular canal and the cupula had a function similar to a spring in the human semicircular canal.

Sparse classification of discriminant nystagmus features using combined video-oculography tests and pupil tracking for common vestibular disorder recognition

Vertigo is a common sign related to a problem with the brain or vestibular system. Detection of ocular nystagmus can be a support indicator to distinguish different vestibular disorders. In order to get reliable and accurate real time measurements from nystagmus response, video-oculography (VOG) plays an important role in the daily clinical examination. However, vestibular diseases present a large diversity in their characteristics that leads to many complications for usual analysis. In this paper, we propose a novel automated approach to achieve both selection and classification of nystagmus parameters using four tests and a pupil tracking procedure in order to give reliable evaluation and standardized indicators of frequent vestibular dysfunction that will assist clinicians in their diagnoses. Indeed, traditional tests (head impulse, caloric, kinetic and saccadic tests) are applied to obtain clinical parameters that highlight the type of vertigo (peripheral or central vertigo). Then, a pupil tracking method is used to extract temporal and frequency nystagmus features in caloric and kinetic sequences. Finally, all extracted features from the tests are reduced according to their high characterization degree by linear discriminant analysis, and classified into three vestibular disorders and normal cases using sparse representation. The proposed methodology is tested on a database containing 90 vertiginous subjects affected by vestibular Neuritis, Meniere's disease and Migraines. The presented technique highly reduces labor-intensive workloads of clinicians by producing the discriminant features for each vestibular disease which will significantly speed up the vertigo diagnosis and provides possibility for fully computerized vestibular disorder evaluation.

Hybrid clustering system using Nystagmus parameters discrimination for vestibular disorder diagnosis

BACKGROUD AND OBJECTIVE

The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition.

METHODS

In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region. Furthermore, the proposed methodology enabled us to select the most optimum VNG features to effectively conduct quantitative evaluation of nystagmus signal. The proposed scheme using a multilayer neural network classifier (MNN) was tested using a dataset involving 98 patients affected by VD and 41 normal subjects.

RESULTS

The new MNN scheme uses only five temporal and frequency parameters selected out of initial thirteen parameters. The scheme generated results reached 94% of classification accuracy.

CONCLUSIONS

The developed expert system is promising in solving the problem of VNG analysis and achieving accurate results of vestibular disorder recognition or diagnosis comparing to other methods or classifiers.