Computer Engineering Department, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand.
Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand.
Comput Intell Neurosci. 2018 Jun 13;2018:1869565. doi: 10.1155/2018/1869565. eCollection 2018.
Neurodegenerative diseases that affect serious gait abnormalities include Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington disease (HD). These diseases lead to gait rhythm distortion that can be determined by stride time interval of footfall contact times. In this paper, we present a new method for gait classification of neurodegenerative diseases. In particular, we utilize a symbolic aggregate approximation algorithm to convert left-foot stride-stride interval into a sequence of symbols using a symbolic aggregate approximation. We then find string prototypes of each class using the newly proposed string grammar unsupervised possibilistic fuzzy C-medians. Then in the testing process the fuzzy k-nearest neighbor is used. We implement the system on three 2-class problems, i.e., the classification of ALS against healthy patients, that of HD against healthy patients , and that of PD against healthy patients. The system is also implemented on one 4-class problem (the classification of ALS, HD, PD, and healthy patients altogether) called NDDs versus healthy. We found that our system yields a very good detection result. The average correct classification for ALS versus healthy is 96.88%, and that for HD versus healthy is 97.22%, whereas that for PD versus healthy is 96.43%. When the system is implemented on 4-class problem, the average accuracy is approximately 98.44%. It can provide prototypes of gait signals that are more understandable to human.
影响严重步态异常的神经退行性疾病包括帕金森病 (PD)、肌萎缩侧索硬化症 (ALS) 和亨廷顿病 (HD)。这些疾病导致步态节律失真，可以通过脚步接触时间的步长时间间隔来确定。在本文中，我们提出了一种新的神经退行性疾病步态分类方法。特别是，我们利用符号聚合近似算法将左脚步长-步长间隔转换为符号序列，使用符号聚合近似。然后，我们使用新提出的字符串语法无监督可能性模糊 C-均值找到每个类的字符串原型。然后在测试过程中使用模糊 k-最近邻。我们在三个 2 类问题上实现了该系统，即 ALS 对健康患者的分类、HD 对健康患者的分类和 PD 对健康患者的分类。该系统还在一个 4 类问题（ALS、HD、PD 和健康患者的分类，称为 NDDs 与健康）上实现，称为 NDDs 与健康。我们发现我们的系统产生了非常好的检测结果。ALS 对健康的平均正确分类为 96.88%，HD 对健康的平均正确分类为 97.22%，而 PD 对健康的平均正确分类为 96.43%。当系统在 4 类问题上实现时，平均准确率约为 98.44%。它可以提供更易于人类理解的步态信号原型。
