@article{ref1,
title="Impulse classification network for video head impulse test",
journal="Annual International Conference of the IEEE Engineering in Medicine and Biology Society.",
year="2020",
author="Baydadaev, Shokhrukh and Usmankhujaev, Saidrasul and Kwon, Jangwoo and Kim, Kyu-Sung",
volume="2020",
number="",
pages="240-243",
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.  Clinical Relevance-ICN is a high-performance classification method that works on a patient's vHIT impulse data by identifying abnormalities and artifacts. This method is an advanced clinical decision support system that can help doctors quickly make decisions.<p /> <p>Language: en</p>",
language="en",
issn="2375-7477",
doi="10.1109/EMBC44109.2020.9175969",
url="http://dx.doi.org/10.1109/EMBC44109.2020.9175969"
}