TY  - JOUR
PY  - 2020//
TI  - Impulse classification network for video head impulse test
JO  - Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
A1  - Baydadaev, Shokhrukh
A1  - Usmankhujaev, Saidrasul
A1  - Kwon, Jangwoo
A1  - Kim, Kyu-Sung
SP  - 240
EP  - 243
VL  - 2020
IS  - 
N2  - 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>
LA  - en
SN  - 2375-7477
UR  - http://dx.doi.org/10.1109/EMBC44109.2020.9175969
ID  - ref1
ER  -