Impulse Classification Network for Video Head Impulse Test

被引：0

作者：

Baydadaev, Shokhrukh ^{[1
]}

Usmankhujaev, Saidrasul ^{[1
]}

Kwon, Jangwoo ^{[1
]}

Kim, Kyu-Sung ^{[2
]}

机构：

[1] Inha Univ, Dept Comp Sci & Engn, Incheon 22212, South Korea

[2] Inha Univ, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Incheon 22332, South Korea

来源：

42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 | 2020年

基金：

新加坡国家研究基金会;

关键词：

Vestibulo-ocular Reflex (VOR); Video Head Impulse Test (vHIT); Superior Semicircular Canal (SSC); Impulse Classification Network (ICN); Convolutional Neural Network (CNN);

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页码：240 / 243

页数：4