Ghaderyan Peyvand, Ghoreshi Beyrami Seyede Marziyeh
Computational Neuroscience Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
Comput Biol Med. 2020 May;120:103736. doi: 10.1016/j.compbiomed.2020.103736. Epub 2020 Apr 1.
Gait rhythm fluctuations are of great importance for automatic neurodegenerative diseases (NDDs) detection. They provide a cost-effective and noninvasive monitoring tool in which their parameters are related to neuromuscular function. This study investigated a new solution based on a set of new symmetric features and sparse non-negative least squares (NNLS) coding classifier. Dynamic gait series warping (DGSW), Euclidean, Manhattan, Minkowski, Chebyshev, Canberra distances, and cosine function were used to quantify the amount of divergence between the left and right stride, swing, and stance intervals. The algorithm was evaluated using the gait signals of 16 healthy control subjects, 13 patients with amyotrophic lateral sclerosis (ALS), 15 patients with Parkinson's disease (PD) and 20 patients with Huntington's disease (HD). The proposed new approach using symmetric features and NNLS technique achieved outstanding accuracies of 98%, 97%, and 95% on the patients with PD, ALS, and HD, respectively. The findings also suggested that the new DGSW, cosine function, and Chebyshev distance, which are designed to dynamically, geometrically, or nonlinearly quantify the similarity between two time series, provide the discriminatory measures to describe how NDDs alter the gait symmetry. In comparison with other studies, combining symmetric features with a sparse NNLS coding classifier can improve the detection accuracy providing an efficient and cost-effective framework for the development of a NDDs detection system.
步态节奏波动对于自动检测神经退行性疾病（NDDs）非常重要。它们提供了一种经济高效且非侵入性的监测工具，其参数与神经肌肉功能相关。本研究调查了一种基于一组新的对称特征和稀疏非负最小二乘（NNLS）编码分类器的新解决方案。动态步态序列规整（DGSW）、欧几里得距离、曼哈顿距离、闵可夫斯基距离、切比雪夫距离、堪培拉距离和余弦函数被用于量化左右步幅、摆动和站立间隔之间的差异量。使用16名健康对照受试者、13名肌萎缩侧索硬化症（ALS）患者、15名帕金森病（PD）患者和20名亨廷顿舞蹈病（HD）患者的步态信号对该算法进行了评估。所提出的使用对称特征和NNLS技术的新方法在PD、ALS和HD患者中分别取得了98%、97%和95%的出色准确率。研究结果还表明，旨在动态、几何或非线性地量化两个时间序列之间相似性的新DGSW、余弦函数和切比雪夫距离提供了判别措施，以描述NDDs如何改变步态对称性。与其他研究相比，将对称特征与稀疏NNLS编码分类器相结合可以提高检测准确率，为开发NDDs检测系统提供一个高效且经济高效的框架。
