Abstract
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.
Highlights
“Ataxia” was initially used to describe various uncoordinated characteristics of different diseases, such as gait, movement, heartbeat, etc
Normal Sensory ataxia Cerebellar ataxia the accuracies of three algorithms are higher than 96% for Roberg’s test and gait detection, demonstrating the feasibility and effectiveness of the method
The physical significance of each time domain feature is as follows: the mean value describes the stable component of the signal, the mean square value reflects the energy of the signal, FIGURE 9 | Signal amplitudes for sensory ataxia subject, cerebellar ataxia subject and normal person in Romberg’s test. (A) Signal amplitudes for sensory ataxia subject, (B) signal amplitudes for cerebellar ataxia subject, and (C) signal amplitudes for normal person
Summary
“Ataxia” was initially used to describe various uncoordinated characteristics of different diseases, such as gait, movement, heartbeat, etc. The contribution of this paper can be summarized as follows: (1) detection and distinguishing of sensory ataxia and cerebellar ataxia can be achieved by using wireless sensing technology, and the patients’ privacy can be protected; (2) Romberg’s test and gait detection are validated, the accuracy of clinical diagnosis can be improved; (3) various machine learning algorithms are used to increase the stability and credibility of the results. The physical significance of each time domain feature is as follows: the mean value describes the stable component of the signal, the mean square value reflects the energy of the signal, FIGURE 9 | Signal amplitudes for sensory ataxia subject, cerebellar ataxia subject and normal person in Romberg’s test. In order to avoid information loss in the original data and to eliminate redundant information, the first 64 principal components are extracted as features After these steps, we have obtained the dataset of Romberg’s test and gait detection. To increase the credibility and accuracy of the results, we adopted a four-fold cross-validation (Demsar, 2006) method to divide the training set and test set, and adopted three classification algorithms including BP Neural Network, SVM, and RF
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