Citation

Baydadaev S, Usmankhujaev S, Kwon J, Kim KS. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2020; 2020: 240-243.

Copyright

(Copyright © 2020, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC44109.2020.9175969

PMID

33017973

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.

Language: en