Clinical Recognition of Sensory Ataxia and Cerebellar Ataxia
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 expe...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-04-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2021.639871/full |
| _version_ | 1818933142668443648 |
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| author | Qing Zhang Qing Zhang Xihui Zhou Yajun Li Xiaodong Yang Qammer H. Abbasi |
| author_facet | Qing Zhang Qing Zhang Xihui Zhou Yajun Li Xiaodong Yang Qammer H. Abbasi |
| author_sort | Qing Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T04:43:41Z |
| format | Article |
| id | doaj.art-3ba4c92ca8644081a2740f3896f1198a |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-20T04:43:41Z |
| publishDate | 2021-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-3ba4c92ca8644081a2740f3896f1198a2022-12-21T19:53:04ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612021-04-011510.3389/fnhum.2021.639871639871Clinical Recognition of Sensory Ataxia and Cerebellar AtaxiaQing Zhang0Qing Zhang1Xihui Zhou2Yajun Li3Xiaodong Yang4Qammer H. Abbasi5First Affiliated Hospital of Xi’an Jiaotong University, Xi’an Jiaotong University Health Science Center, Xi’an Jiaotong University, Xi’an, ChinaNorthwest Women’s and Children’s Hospital, Xi’an Jiaotong University Health Science Center, Xi’an, ChinaFirst Affiliated Hospital of Xi’an Jiaotong University, Xi’an Jiaotong University Health Science Center, Xi’an Jiaotong University, Xi’an, ChinaNorthwest Women’s and Children’s Hospital, Xi’an Jiaotong University Health Science Center, Xi’an, ChinaSchool of Electronic Engineering, Xidian University, Xi’an, ChinaJames Watt School of Engineering, University of Glasgow, Glasgow, United KingdomAtaxia 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.https://www.frontiersin.org/articles/10.3389/fnhum.2021.639871/fullcerebellar ataxiaclinical recognitionmicrowavesensory ataxiawireless sensing technology |
| spellingShingle | Qing Zhang Qing Zhang Xihui Zhou Yajun Li Xiaodong Yang Qammer H. Abbasi Clinical Recognition of Sensory Ataxia and Cerebellar Ataxia Frontiers in Human Neuroscience cerebellar ataxia clinical recognition microwave sensory ataxia wireless sensing technology |
| title | Clinical Recognition of Sensory Ataxia and Cerebellar Ataxia |
| title_full | Clinical Recognition of Sensory Ataxia and Cerebellar Ataxia |
| title_fullStr | Clinical Recognition of Sensory Ataxia and Cerebellar Ataxia |
| title_full_unstemmed | Clinical Recognition of Sensory Ataxia and Cerebellar Ataxia |
| title_short | Clinical Recognition of Sensory Ataxia and Cerebellar Ataxia |
| title_sort | clinical recognition of sensory ataxia and cerebellar ataxia |
| topic | cerebellar ataxia clinical recognition microwave sensory ataxia wireless sensing technology |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2021.639871/full |
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