{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T14:56:45Z","timestamp":1789570605519,"version":"build-2803163510"},"reference-count":59,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 108\u20132628-E-006\u2013003 -MY3"],"award-info":[{"award-number":["MOST 108\u20132628-E-006\u2013003 -MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007750","name":"National Cheng Kung University","doi-asserted-by":"publisher","award":["Medical Device Innovation Center"],"award-info":[{"award-number":["Medical Device Innovation Center"]}],"id":[{"id":"10.13039\/501100007750","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2019s disease (PD), HC vs. Huntington\u2019s 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.<\/jats:p>","DOI":"10.3390\/e22121340","type":"journal-article","created":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T20:48:12Z","timestamp":1606337292000},"page":"1340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Development of a Neurodegenerative Disease Gait Classification Algorithm Using Multiscale Sample Entropy and Machine Learning Classifiers"],"prefix":"10.3390","volume":"22","author":[{"given":"Quoc Duy","family":"Nam Nguyen","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan City 701, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6249-5238","authenticated-orcid":false,"given":"An-Bang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and Tzu Chi University, Hualien 970473, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1894-1189","authenticated-orcid":false,"given":"Che-Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, College of Engineering\/Medical Device Innovation Center, National Cheng Kung University, Tainan City 704, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1007\/s00702-017-1686-y","article-title":"Epidemiology of Parkinson\u2019s disease","volume":"124","author":"Tysnes","year":"2017","journal-title":"J. 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