Gazi University, Graduate School of Natural and Applied Sciences, Electrical and Electronics Engineering, Ankara, Turkey.
Gazi University, Faculty of Technology, Electrical and Electronics Engineering, Ankara, Turkey.
Int J Med Inform. 2024 Nov;191:105542. doi: 10.1016/j.ijmedinf.2024.105542. Epub 2024 Jul 8.
Neurodegenerative diseases (NDDs), which are caused by the degeneration of neurons and their functions, affect a significant part of the world's population. Although gait disorders are one of the critical and common markers to determine the presence of NDDs, diagnosing which NDD the patients have among a group of NDDs using gait data is still a significant challenge to be addressed. In this study, we addressed the multi-class classification of NDDs and aim to diagnose Parkinson's disease (PD), Amyotrophic lateral sclerosis disease (AD), and Huntington's disease (HD) from a group containing NDDs and healthy control subjects. We also examined the impact of disease-specific identified features derived from VGRF signals. Detrended Fluctuation Analysis (DFA), Dynamic Time Warping (DTW) and Autocorrelation (AC) were used for feature extraction on Vertical Ground Reaction Force (VGRF) signals. To compare the performance of the features, we employed Support Vector Machines, K-Nearest Neighbors, and Neural Networks as classifiers. In three-class problem addressing the classification of AD, PD and HD 93.3% accuracy rate was achieved, while in the four classes case, in which NDDs and HC groups were considered together, 93.5% accuracy rate was yielded. Considering the disease-specific impact of features, it is revealed that while DFA based features diagnose patients with AD with the highest accuracy, DTW has been shown to be more successful in diagnosing PD. AC based features provided the highest accuracy in diagnosing HD. Although gait disorder is common for NDDs, each disease may have its own distinctive gait rhythms; therefore, it is important to identify disease-specific patterns and parameters for the diagnosis of each disease. To increase the diagnostic accuracy, it is necessary to use a combination of features, which were effective for each disease diagnosis. Determining a limited number of disease-specific features would provide NDD diagnostic systems suitable to be deployed in edge-computing environments.
神经退行性疾病（NDDs）是由神经元及其功能的退化引起的，影响了世界上很大一部分人口。尽管步态障碍是确定 NDD 存在的关键和常见标志之一，但使用步态数据从一组 NDD 中诊断患者患有哪种 NDD 仍然是一个需要解决的重大挑战。在这项研究中，我们解决了 NDD 的多类分类问题，并旨在从包含 NDD 和健康对照组的组中诊断帕金森病（PD）、肌萎缩性侧索硬化症（ALS）和亨廷顿病（HD）。我们还研究了源自 VGRF 信号的疾病特异性识别特征的影响。去趋势波动分析（DFA）、动态时间 warping（DTW）和自相关（AC）用于从垂直地面反力（VGRF）信号中提取特征。为了比较特征的性能，我们使用支持向量机、K-最近邻和神经网络作为分类器。在三分类问题中，AD、PD 和 HD 的分类准确率达到了 93.3%，而在四分类问题中，同时考虑 NDD 和 HC 组的情况下，准确率达到了 93.5%。考虑到特征的疾病特异性影响，结果表明，基于 DFA 的特征在诊断 AD 患者时具有最高的准确性，而 DTW 在诊断 PD 方面更成功。基于 AC 的特征在诊断 HD 方面提供了最高的准确性。虽然步态障碍在 NDDs 中很常见，但每种疾病可能都有其独特的步态节律；因此，识别每种疾病的特定模式和参数对于诊断每种疾病非常重要。为了提高诊断准确性，有必要结合对每种疾病诊断有效的特征。确定数量有限的疾病特异性特征将为 NDD 诊断系统提供适合在边缘计算环境中部署的系统。
