Figure - available from: Journal of Sensors
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System block diagram. After loading the data file (.csv), the head impulse and eye response are first smoothed, then used for detecting parameters. Those include onset, minimum and maximum values, significant peaks, and catch-up saccades (CUS). If the CUS appears before the head impulse end, it is labeled as covert CUS, and the gain is calculated via desaccaded position gain algorithm. Otherwise, it can be a normal response, or contains overt CUS, which can be separated due to the number and time of significant peaks. After all, the gain and CUS proportion is calculated and inputted to fuzzy inference system to obtain normality and artifact index. ∗Artifact handling included smoothing filter, peaks (#, amplitude, and times), and gain. ∗∗Artifact index is theoretically contradicting such as low gain but no CUS, or gain within normal range but still have CUS.
Source publication
This paper represents the clinical decision support system for video head impulse test (vHIT) based on fuzzy inference system. It examines the eye and head movement recorded by the eye movement tracking device, calculates the vestibulo-ocular reflex (VOR) gain, and applies fuzzy inference system to output the normality and artifact index of the tes...
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Background:
The aim of this study was to determine whether the extent and intensity of pain caused by wearing goggles during the video head impulse test (vHIT) could be reduced by adjusting the direction in which the band pulls the goggles, without increasing the number of artifacts recorded during vHIT.
Methods:
vHIT tests were performed in 65...
Objective: This study aimed to identify differences in vestibulo-ocular reflex gain (VOR gain) and saccadic response in the suppression head impulse paradigm (SHIMP) between predictable and less predictable head movements, in a group of healthy subjects. It was hypothesized that higher prediction could lead to a lower VOR gain, a shorter saccadic l...
This study aimed to compare the diagnostic efficacy of the Halmágyi-Curthoys head impulse (thrust) test and Romberg's test in detecting vestibular hypofunctioning among two groups of 50 vertigo patients each; the two groups were randomly assigned. The assessment utilized the visual analog scale (VAS) to quantify subjective experiences of vertigo. T...
Abstract—The vestibulo-ocular reflex (VOR) is a dynamic system of the human brain that helps to maintain balance and to stabilize vision during head movement. The video head impulse test (vHIT) is a clinical test that uses lightweight, high-speed video goggles to examine the VOR function by calculating the ratio of eye-movement to head-movement vel...
Citations
... For example, if the patient must focus on something while rotating the head right-to-left, and if the patient keeps seeing the object without losing sight of it or feeling dizzy, the patient has a healthy dynamic vestibulo-ocular reflex (VOR) function [1,3,5]. The human inner ear has three paired semicircular canals, which work as detectors of head acceleration that activate the VOR system and generate eye movement during head rotation or translation [1,2,3,9]. ...
... The VOR system occurs with high-frequency motion [2]. That motion maintains our vision orientation and allows physicians to check the normality or abnormality of the vestibular function. ...
... Normality of VOR, known as VOR gain, is defined as the ratio of eye velocity to head velocity. The gain value should be close to 1 with normal vestibular function [2]. There are three types of vHIT data: lateral (test for horizontal canal), left anterior-right posterior (LARP), and right anterior-left posterior (RALP) for vertical canals [8,9]. ...
Abstract—The vestibulo-ocular reflex (VOR) is a dynamic system of the human brain that helps to maintain balance and to stabilize vision during head movement. The video head impulse test (vHIT) is a clinical test that uses lightweight, high-speed video goggles to examine the VOR function by calculating the ratio of eye-movement to head-movement velocities. The main problem with a patient's vHIT is that data coming from the goggles may have artifacts and other noise. This paper proposes an impulse classification network (ICN) using a one-dimensional convolutional neural network that can detect noisy data and classify human VOR impulses. Our ICN found actual classes of a patient’s impulses with 95% accuracy.
