Development of a closed-loop system for tremor suppression in patients with Parkinson's disease

Electromagnetic radiation therapy for Parkinson's disease tremor reduction- systematic reviews and Bayesian meta-analyses for comparing the effectiveness of electric, magnetic and light stimulation methods

PURPOSE

Tremor is one of the key characteristics of Parkinson's disease (PD), leading to physical disabilities and often showing limited responses to pharmacological treatments. To suppress tremors in PD patients, several types of non-invasive and non-pharmacological methods have been proposed so far. In the current systematic review, three electromagnetic-based radiation strategies including electrical stimulation, magnetic stimulation, and light stimulation methods were reviewed and compared.

METHODS

Major databases were searched to retrieve eligible studies. For the meta-analysis, a random-effect Bayesian framework was used. Also, heterogeneity between studies was assessed using I2 statistic, prediction interval, and tau2. Publication bias was assessed using funnel plot, and the effectiveness of methods for reducing tremor was compared using network Bayesian meta-analysis.

RESULTS AND CONCLUSION

Thirty-one studies were found for qualitative analysis, and 16 studies were found for quantitative synthesis. Based on the suppression ratio, methods can be ordered as electrical stimulation, light therapy, and magnetic stimulation. Furthermore, the results showed that electrical and magnetic stimulation were more effective for tremor suppression at early stages of PD, while light therapy was found to be more effective during the later stages of PD.

Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.

A Review on Wearable Technologies for Tremor Suppression

Tremor is defined as a rhythmic, involuntary oscillatory movement of a body part. Although everyone exhibits a certain degree of tremor, some pathologies lead to very disabling tremors. These pathological tremors constitute the most prevalent movement disorder, and they imply severe difficulties in performing activities of daily living. Although tremors are currently managed through pharmacotherapy or surgery, these treatments present significant associated drawbacks: drugs often induce side effects and show decreased effectiveness over years of use, while surgery is a hazardous procedure for a very low percentage of eligible patients. In this context, recent research demonstrated the feasibility of managing upper limb tremors through wearable technologies that suppress tremors by modifying limb biomechanics or applying counteracting forces. Furthermore, recent experiments with transcutaneous afferent stimulation showed significant tremor attenuation. In this regard, this article reviews the devices developed following these tremor management paradigms, such as robotic exoskeletons, soft robotic exoskeletons, and transcutaneous neurostimulators. These works are presented, and their effectiveness is discussed. The article also evaluates the different metrics used for the validation of these devices and the lack of a standard validation procedure that allows the comparison among them.

Peripheral electrical stimulation to reduce pathological tremor: a review

Interventions to reduce tremor in essential tremor (ET) and Parkinson’s disease (PD) clinical populations often utilize pharmacological or surgical therapies. However, there can be significant side effects, decline in effectiveness over time, or clinical contraindications for these interventions. Therefore, alternative approaches must be considered and developed. Some non-pharmacological strategies include assistive devices, orthoses and mechanical loading of the tremorgenic limb, while others propose peripheral electrical stimulation. Specifically, peripheral electrical stimulation encompasses strategies that activate motor and sensory pathways to evoke muscle contractions and impact sensorimotor function. Numerous studies report the efficacy of peripheral electrical stimulation to alter tremor generation, thereby opening new perspectives for both short- and long-term tremor reduction. Therefore, it is timely to explore this promising modality in a comprehensive review. In this review, we analyzed 27 studies that reported the use of peripheral electrical stimulation to reduce tremor and discuss various considerations regarding peripheral electrical stimulation: the stimulation strategies and parameters, electrodes, experimental designs, results, and mechanisms hypothesized to reduce tremor. From our review, we identified a high degree of disparity across studies with regard to stimulation patterns, experimental designs and methods of assessing tremor. Having standardized experimental methodology is a critical step in the field and is needed in order to accurately compare results across studies. With this review, we explore peripheral electrical stimulation as an intervention for tremor reduction, identify the limitations and benefits of the current state-of-the-art studies, and provide ideas to guide the development of novel approaches based on the neural circuitries and mechanical properties implied in tremor generation.

A Wearable System for Attenuating Essential Tremor Based on Peripheral Nerve Stimulation

OBJECTIVE

Currently available treatments for kinetic tremor can cause intolerable side effects or be highly invasive and expensive. Even though several studies have shown the positive effects of external feedback (i.e., electrical stimulation) for suppressing tremor, such approaches have not been fully integrated into wearable real-time feedback systems.

METHOD

We have developed a wireless wearable stimulation system that analyzes upper limb tremor using a three-axis accelerometer and that modulates/attenuates tremor using peripheral-nerve electrical stimulation with adjustable stimulation parameters and a real-time tremor detection algorithm. We outfitted nine subjects with tremor with a wearable system and a set of surface electrodes placed on the skin overlying the radial nerve and tested the effects of stimulation with nine combinations of parameters for open- and closed-loop stimulation on tremor. To quantify the effects of the stimulation, we measured tremor movements, and analyzed the dominant tremor frequency and tremor power.

RESULTS

Baseline tremor power gradually decreased over the course of 18 stimulation trials. During the last trial, compared with the control trial, the reduction rate of tremor power was 42.17 ± 3.09%. The dominant tremor frequency could be modulated more efficiently by phase-locked closed-loop stimulation. The tremor power was equally reduced by open- and closed-loop stimulation.

CONCLUSION

Peripheral nerve stimulation significantly affects tremor, and stimulation parameters need to be optimized to modulate tremor metrics. Clinical Impact: This preliminary study lays the foundation for future studies that will evaluate the efficacy of the proposed closed-loop peripheral nerve stimulation method in a larger group of patients with kinetic tremor.

Electromyography Biomarkers for Quantifying the Intraoperative Efficacy of Deep Brain Stimulation in Parkinson's Patients With Resting Tremor

Introduction: Deep brain stimulation (DBS) is an effective therapy for resting tremor in Parkinson's disease (PD). However, quick and objective biomarkers for quantifying the efficacy of DBS intraoperatively are lacking. Therefore, we aimed to study how DBS modulates the intraoperative neuromuscular pattern of resting tremor in PD patients and to find predictive surface electromyography (sEMG) biomarkers for quantifying the intraoperative efficacy of DBS. Methods: Intraoperative sEMG of 39 PD patients with resting tremor was measured with the DBS on and off, respectively, during the intraoperative DBS testing stage. Twelve signal features (time and frequency domains) were extracted from the intraoperative sEMG data. These sEMG features were associated with the clinical outcome to evaluate the efficacy of intraoperative DBS. Also, an sEMG-based prediction model was established to predict the clinical improvement rate (IR) of resting tremor with DBS therapy. Results: A typical resting tremor with a peak frequency of 4.93 ± 0.98 Hz (mean ± SD) was measured. Compared to the baseline, DBS modulated significant neuromuscular pattern changes in most features except for the peak frequency, by decreasing the motor unit firing rate, amplitude, or power and by changing the regularity pattern. Three sEMG features were detected with significant associations with the clinical improvement rate (IR) of the tremor scale: peak frequency power (R = 0.37, p = 0.03), weighted root mean square (R = 0.42, p = 0.01), and modified mean amplitude power (R = 0.48, p = 0.003). These were adopted to train a Gaussian process regression model with a leave-one-out cross-validation procedure. The prediction values from the trained sEMG prediction model (1,000 permutations, p = 0.003) showed a good correlation (r = 0.47, p = 0.0043) with the true IR of the tremor scale. Conclusion: DBS acutely modulated the intraoperative resting tremor, mainly by suppressing the amplitude and motor unit firing rate and by changing the regularity pattern, but not by modifying the frequency pattern. Three features showed strong robustness and could be used as quick intraoperative biomarkers to quantify and predict the efficacy of DBS in PD patients with resting tremor.