Baydadaev Shokhrukh, Usmankhujaev Saidrasul, Kwon Jangwoo, Kim Kyu-Sung
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:240-243. doi: 10.1109/EMBC44109.2020.9175969.
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.
前庭眼反射（VOR）是人类大脑的一个动态系统，有助于在头部运动期间保持平衡并稳定视觉。视频头脉冲测试（vHIT）是一种临床测试，它使用轻便的高速视频护目镜，通过计算眼球运动与头部运动速度的比率来检查VOR功能。患者vHIT的主要问题在于，来自护目镜的数据可能存在伪影和其他噪声。本文提出了一种使用一维卷积神经网络的脉冲分类网络（ICN），该网络可以检测噪声数据并对人类VOR脉冲进行分类。我们的ICN以95%的准确率识别出患者脉冲的实际类别。临床相关性——ICN是一种高性能分类方法，通过识别异常和伪影来处理患者的vHIT脉冲数据。该方法是一种先进的临床决策支持系统，可帮助医生快速做出决策。
