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Aging Neurosci."],"abstract":"<jats:p>Recent studies have identified that peripheral stimulation in Parkinson\u2019s disease (PD) is effective in tremor reduction, indicating that a peripheral feedback loop plays an important role in the tremor reset mechanism. This was an open-label, quasi-experimental, pre- and post-test design, single-blind, single-group study involving 20 tremor-dominant PD patients. The objective of this study is to explore the effect of electrical muscle stimulation (EMS) as an adjunctive treatment for resting tremor during \u201con\u201d period and to identify the best machine learning model to predict the suitable stimulation level that will yield the longest period of tremor reduction or tremor reset time. In this study, we used a Parkinson\u2019s glove to evaluate, stimulate, and quantify the tremors of PD patients. This adjustable glove incorporates a 3-axis gyroscope to measure tremor signals and an EMS to provide an on-demand muscle stimulation to suppress tremors. Machine learning models were applied to identify the suitable pulse amplitude (stimulation level) in five classes that led to the longest tremor reset time. The study was registered at the <jats:ext-link>www.clinicaltrials.gov<\/jats:ext-link> under the name \u201cThe Study of Rest Tremor Suppression by Using Electrical Muscle Stimulation\u201d (NCT02370108). Twenty tremor-dominant PD patients were recruited. After applying an average pulse amplitude of 6.25 (SD 2.84) mA and stimulation period of 440.7 (SD 560.82) seconds, the total time of tremor reduction, or tremor reset time, was 329.90 (SD 340.91) seconds. A significant reduction in tremor parameters during stimulation was demonstrated by a reduction of Unified Parkinson\u2019s Disease Rating Scale (UPDRS) scores, and objectively, with a reduction of gyroscopic data (<jats:italic>p<\/jats:italic> &amp;lt; 0.05, each). None of the subjects reported any serious adverse events. We also compared gyroscopic data with five machine learning techniques: Logistic Regression, Random Forest, Support Vector Machine (SVM), Neural Network (NN), and Long-Short-Term-Memory (LSTM). The machine learning model that gave the highest accuracy was LSTM, which obtained: accuracy = 0.865 and macro-F1 = 0.736. This study confirms the efficacy of EMS in the reduction of resting tremors in PD. LSTM was identified as the most effective model for predicting pulse amplitude that would elicit the longest tremor reset time. Our study provides further insight on the tremor reset mechanism in PD.<\/jats:p>","DOI":"10.3389\/fnagi.2021.727654","type":"journal-article","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T06:09:45Z","timestamp":1631254185000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Using Machine Learning for Predicting the Best Outcomes With Electrical Muscle Stimulation for Tremors in Parkinson\u2019s Disease"],"prefix":"10.3389","volume":"13","author":[{"given":"Onanong","family":"Phokaewvarangkul","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peerapon","family":"Vateekul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itsara","family":"Wichakam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chanawat","family":"Anan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roongroj","family":"Bhidayasiri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics10060421","article-title":"A supervised machine learning approach to detect the on\/off state in Parkinson\u2019s disease using wearable based gait signals.","volume":"10","author":"Aich","year":"2020","journal-title":"Diagnostics"},{"key":"B2","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1111\/nyas.12084","article-title":"Principles of interneuron development learned from Renshaw cells and the motoneuron recurrent inhibitory circuit.","volume":"1279","author":"Alvarez","year":"2013","journal-title":"Ann. 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