Exploring the effect of electrical muscle stimulation as a novel treatment of intractable tremor in Parkinson's disease

2015 ◽  
Vol 358 (1-2) ◽  
pp. 146-152 ◽  
Author(s):  
Onanong Jitkritsadakul ◽  
Chusak Thanawattano ◽  
Chanawat Anan ◽  
Roongroj Bhidayasiri
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Muscle Stimulation ◽  
Electrical Muscle Stimulation ◽  
Novel Treatment ◽  
Electrical Muscle

scholarly journals Using Machine Learning for Predicting the Best Outcomes With Electrical Muscle Stimulation for Tremors in Parkinson’s Disease

2021 ◽  
Vol 13 ◽  
Author(s):  
Onanong Phokaewvarangkul ◽  
Peerapon Vateekul ◽  
Itsara Wichakam ◽  
Chanawat Anan ◽  
Roongroj Bhidayasiri
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Pulse Amplitude ◽  
Muscle Stimulation ◽  
Electrical Muscle Stimulation ◽  
Stimulation Level ◽  
Machine Learning Model ◽  
Reset Time ◽  
Electrical Muscle

Recent studies have identified that peripheral stimulation in Parkinson’s 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 “on” 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’s 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 www.clinicaltrials.gov under the name “The Study of Rest Tremor Suppression by Using Electrical Muscle Stimulation” (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’s Disease Rating Scale (UPDRS) scores, and objectively, with a reduction of gyroscopic data (p < 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.

Stroke Rehabilitation and Parkinson's Disease Tremor Reduction Using BCIs Combined With FES

2021 ◽  
pp. 473-491
Author(s):  
Sophie V. Adama ◽  
Martin Bogdan
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Healthy Subjects ◽  
Stroke Rehabilitation ◽  
Brain Computer Interface ◽  
Electrical Muscle Stimulation ◽  
Brain Signals ◽  
First Case ◽  
Bci System ◽  
Electrical Muscle

This article describes how Stroke and Parkinson's disease are two illnesses that particularly affect motor functions. With the advancements in technology, there is a lot of research focusing on finding solutions: to contribute to neuroplasticity in the first case, and to reduce symptoms in the second case. This manuscript describes the design of a brain-computer interface system (BCI) system paired with an electrical muscle stimulation suit for stroke rehabilitation and the reduction of tremors caused by Parkinson's disease. The idea is to strengthen the sensory-motor feedback loop, which will allow a more stabilized control of the affected extremities by taking into account the patient's motivation. To do so, his brain signals are measured to detect his intention to attempt to execute a movement, in contrast to the classical approach where the movement executions are imposed. A first feasibility study was completed. The author's next step is planning to test the system first with healthy subjects and finally with patients.

Stroke Rehabilitation and Parkinson's Disease Tremor Reduction Using BCIs Combined With FES

2021 ◽  
pp. 679-697
Author(s):  
Sophie V. Adama ◽  
Martin Bogdan
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Healthy Subjects ◽  
Stroke Rehabilitation ◽  
Brain Computer Interface ◽  
Electrical Muscle Stimulation ◽  
Brain Signals ◽  
First Case ◽  
Bci System ◽  
Electrical Muscle

This article describes how Stroke and Parkinson's disease are two illnesses that particularly affect motor functions. With the advancements in technology, there is a lot of research focusing on finding solutions: to contribute to neuroplasticity in the first case, and to reduce symptoms in the second case. This manuscript describes the design of a brain-computer interface system (BCI) system paired with an electrical muscle stimulation suit for stroke rehabilitation and the reduction of tremors caused by Parkinson's disease. The idea is to strengthen the sensory-motor feedback loop, which will allow a more stabilized control of the affected extremities by taking into account the patient's motivation. To do so, his brain signals are measured to detect his intention to attempt to execute a movement, in contrast to the classical approach where the movement executions are imposed. A first feasibility study was completed. The author's next step is planning to test the system first with healthy subjects and finally with patients.

Creating user interfaces with electrical muscle stimulation

interactions ◽  
2016 ◽  
Vol 24 (1) ◽  
pp. 74-77 ◽  
Author(s):  
Stefan Schneegass ◽  
Albrecht Schmidt ◽  
Max Pfeiffer
Keyword(s):  
User Interfaces ◽  
Muscle Stimulation ◽  
Electrical Muscle Stimulation ◽  
Electrical Muscle

Effects of electrical muscle stimulation in a left ventricular assist device patient

2012 ◽  
Vol 160 (3) ◽  
pp. e44-e45 ◽  
Author(s):  
Kentaro Kamiya ◽  
Alessandro Mezzani ◽  
Takashi Masuda ◽  
Atsuhiko Matsunaga ◽  
Tohru Izumi ◽  
...  
Keyword(s):  
Left Ventricular Assist Device ◽  
Ventricular Assist Device ◽  
Left Ventricular ◽  
Muscle Stimulation ◽  
Electrical Muscle Stimulation ◽  
Assist Device ◽  
Ventricular Assist ◽  
Left Ventricular Assist ◽  
Electrical Muscle

DextrEMS: Increasing Dexterity in Electrical Muscle Stimulation by Combining it with Brakes

2021 ◽  
Author(s):  
Romain Nith ◽  
Shan-Yuan Teng ◽  
Pengyu Li ◽  
Yujie Tao ◽  
Pedro Lopes
Keyword(s):  
Muscle Stimulation ◽  
Electrical Muscle Stimulation ◽  
Electrical Muscle
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