Zhang Qing, Zhou Xihui, Li Yajun, Yang Xiaodong, Abbasi Qammer H
First Affiliated Hospital of Xi'an Jiaotong University, Xi'an Jiaotong University Health Science Center, Xi'an Jiaotong University, Xi'an, China.
Northwest Women's and Children's Hospital, Xi'an Jiaotong University Health Science Center, Xi'an, China.
Front Hum Neurosci. 2021 Apr 1;15:639871. doi: 10.3389/fnhum.2021.639871. eCollection 2021.
Ataxia is a kind of external characteristics when the human body has poor coordination and balance disorder, it often indicates diseases in certain parts of the body. Many internal factors may causing ataxia; currently, observed external characteristics, combined with Doctor's personal clinical experience play main roles in diagnosing ataxia. In this situation, different kinds of diseases may be confused, leading to the delay in treatment and recovery. Modern high precision medical instruments would provide better accuracy but the economic cost is a non-negligible factor. In this paper, novel non-contact sensing technique is used to detect and distinguish sensory ataxia and cerebellar ataxia. Firstly, Romberg's test and gait analysis data are collected by the microwave sensing platform; then, after some preprocessing, some machine learning approaches have been applied to train the models. For Romberg's test, time domain features are considered, the accuracy of all the three algorithms are higher than 96%; for gait detection, Principal Component Analysis (PCA) is used for dimensionality reduction, and the accuracies of Back Propagation (BP) neural Network, Support Vector Machine (SVM), and Random Forest (RF) are 97.8, 98.9, and 91.1%, respectively.
共济失调是人体协调能力差和平衡紊乱时的一种外在表现，它常常预示着身体某些部位出现疾病。许多内在因素可能导致共济失调；目前，观察到的外在表现，结合医生的个人临床经验在共济失调的诊断中起主要作用。在这种情况下，可能会混淆各种疾病，导致治疗和康复延迟。现代高精度医疗仪器能提供更高的准确性，但经济成本是一个不可忽视的因素。本文采用新型非接触传感技术来检测和区分感觉性共济失调和小脑性共济失调。首先，通过微波传感平台收集闭目难立试验和步态分析数据；然后，经过一些预处理后，应用一些机器学习方法来训练模型。对于闭目难立试验，考虑时域特征，三种算法的准确率均高于96%；对于步态检测，使用主成分分析（PCA）进行降维，反向传播（BP）神经网络、支持向量机（SVM）和随机森林（RF）的准确率分别为97.8%、98.9%和91.1%。
