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Abstract：
The prevalence of neurodegenerative diseases (NDD) has grown rapidly in recent years and NDD screening receives much attention. NDD could cause gait abnormalities so that to screen NDD using gait signal is feasible. The research aim of this study is to develop an NDD classification algorithm via gait force (GF) using multiscale sample entropy (MSE) and machine learning models. The Physionet NDD gait database is utilized to validate the proposed algorithm. In the preprocessing stage of the proposed algorithm, new signals were generated by taking one and two times of differential on GF and are divided into various time windows (10/20/30/60-sec). In feature extraction, the GF signal is used to calculate statistical and MSE values. Owing to the imbalanced nature of the Physionet NDD gait database, the synthetic minority oversampling technique (SMOTE) was used to rebalance data of each class. Support vector machine (SVM) and k-nearest neighbors (KNN) were used as the classifiers. The best classification accuracies for the healthy controls (HC) vs. Parkinson\'s disease (PD), HC vs. Huntington\'s disease (HD), HC vs. amyotrophic lateral sclerosis (ALS), PD vs. HD, PD vs. ALS, HD vs. ALS, HC vs. PD vs. HD vs. ALS, were 99.90%, 99.80%, 100%, 99.75%, 99.90%, 99.55%, and 99.68% under 10-sec time window with KNN. This study successfully developed an NDD gait classification based on MSE and machine learning classifiers.
摘要：
近年来，神经退行性疾病（NDD）的患病率迅速增长，NDD筛查备受关注。NDD可能导致步态异常，因此使用步态信号筛查NDD是可行的。本研究的研究目的是通过使用多尺度样本熵（MSE）和机器学习模型，通过步态力（GF）开发NDD分类算法。PhysionetNDD步态数据库用于验证所提出的算法。在所提出的算法的预处理阶段,通过在GF上进行一次和两次差分来生成新信号，并将其分为各种时间窗口（10/20/30/60-sec）。在特征提取中，GF信号用于计算统计和MSE值。由于PhysionetNDD步态数据库的不平衡性质，使用合成少数过采样技术（SMOTE）重新平衡每个类别的数据。使用支持向量机（SVM）和k最近邻（KNN）作为分类器。健康对照(HC)与健康对照的最佳分类精度帕金森病（PD），HCvs.亨廷顿病（HD），HCvs.肌萎缩侧索硬化(ALS),PDvs.HD,PDvs.ALS,HDvs.ALS,HCvs.PDvs.HDvs.ALS,99.90%,99.80%,100%,99.75%,99.90%,99.55%,和99.68%在10秒的时间窗口与KNN。本研究成功开发了基于MSE和机器学习分类器的NDD步态分类。
