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Using Machine Learning for Predicting the Best Outcomes With Electrical Muscle Stimulation for Tremors in Parkinson's Disease.

ABSTRACT: 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.

SUBMITTER: Phokaewvarangkul O

PROVIDER: S-EPMC8461308 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:AIMS:Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thirds of individuals relapse when provided even the most robust treatments. Identifying for whom treatment is effective will improve the success of our treatments and perhaps identify strategies for improving current approaches. METHODS:Two cohorts (training: N = 90, validation: N = 71) of cigarette smokers enrolled in group cognitive-behavioral therapy (CBT). Generalized estimating equations were used to identify baseline predictors of outcome, as defined by breath carbon monoxide and urine cotinine. Significant measures were entered as candidate variables to predict quit status. The resulting decision trees were used to predict cessation outcomes in a validation cohort. RESULTS:In the training cohort, the decision trees significantly improved on chance classification of smoking status following treatment and at 6-month follow-up. The first split of all decision trees, which was delay discounting, significantly improved on chance classification rates in both the training and validation cohort. Delay discounting emerged as the single best predictor of group CBT treatment response with an average baseline discount rate of ln(k) = -7.1, correctly predicting smoking status of 80% of participants at posttreatment and 81% of participants at follow-up. CONCLUSIONS:This study provides a first step toward personalized care for smoking cessation though future work is needed to identify individuals that are likely to be successful in treatments beyond group CBT. IMPLICATIONS:This study provides a first step toward personalized care for smoking cessation. Using a novel machine-learning approach, baseline measures of clinical and executive functioning are used to predict smoking cessation outcomes following group CBT. A decision point is recommended for the single best predictor of treatment outcomes, delay discounting, to inform future research or clinical practice in an effort to better allocate patients to treatments that are likely to work.

Project description:Stimulation of the mouse hindlimb via the sciatic nerve was used to induce contractions for 4 hours to investigate acute muscle gene activation in a model of muscle phenotype conversion. Initial force production (1.6 + 0.1 g/g body weight) declined 45% within 10 min and was maintained for the remainder of the experiment. Force returned to initial levels upon completion of the study. An immediate-early growth response was present in the EDL (FOS, JUN, ATF3, MAFK) with a similar but attenuated pattern in the soleus. Transcript profiles showed decreased fast fiber specific mRNA (myosin heavy chains 2A, 2B; troponins T3, I; -tropomyosin, m-creatine kinase) and increased slow transcripts (myosin heavy chain slow/1, troponin C, tropomyosin 3) in the EDL. Histological analysis of the EDL revealed glycogen depletion without inflammatory cell infiltration or myofiber damage in stimulated vs. control muscles. Several fiber type specific transcription factors (EYA1, TEAD1, NFATc1 and c4, PPARG, PPARGC1 and β, BHLHB2) increased in the EDL along with transcription factors characteristic of embryogenesis (KLF4, SOX17, TCF15, PKNOX1, ELAV). No established in vivo satellite cell markers or the genes activated during our parallel studies of satellite cell proliferation in vitro (CYCLINS A2, B2, C, E1, MyoD) increased in the stimulated muscles. These data indicated that onset of fast to slow phenotype conversion occurred in the EDL within 4 hours of stimulation without satellite cell recruitment or muscle injury but was driven by phenotype specific transcription factors from resident fiber myonuclei including activation of nascent developmental transcriptional programs. Adult male Swiss Webster mice (30-35 g) were anesthetized, a bipolar electrode was implanted adjacent to the sciatic nerve and the hindlimb immobilized. The voltage-force relation was determined to establish supramaximal stimulation conditions and the length-tension relation was determined to set the resting length for maximum twitch tension. Contractions were induced by sciatic nerve stimulation (0.5 msec duration, 2-5 volts). The muscles were allowed to rest 15 minutes for full metabolic recovery at physiologic temperatures. Supramaximal stimulation was applied at a rate of 10 Hz for 4 hours. At the end of each experiment the soleus muscles were carefully dissected and flash frozen in liquid nitrogen for analysis of mRNA expression via microarray analysis. The contralateral, unstimulated Soleus provided a genetically matched, paired control for each specimen.

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Using Machine Learning for Predicting the Best Outcomes With Electrical Muscle Stimulation for Tremors in Parkinson's Disease.

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