Tremor decomposition: A method for determining which muscles are most responsible for a patient's tremor

Brigham Young University, Provo UT

Investigators

Abstract

Tremor is one of the most common movement deficits. It affects patients suffering from multiple disorders, including Essential Tremor, Parkinson's disease, Dystonia, Cerebellar Ataxia (loss of control of bodily movements) and others. For these patients, tremor makes activities of daily living (eating, clothing, writing, etc.) difficult or impossible. Although medication and surgical interventions have significantly reduced patient suffering, they are only recommended for a subset of patients. And even in these patients, interventions are only partially effective, leaving many patients without effective treatment options. Surprisingly, there are few tremor-suppressing devices available to patients. One might envision, for example, a wearable upper-limb device (e.g. a brace) designed to suppress tremor. However, a significant obstacle to developing effective tremor-suppressing devices is that, currently, there is no way to know where (which muscles/joints) to intervene because there is no key way of determining which muscles are most responsible for a patient's tremor. The purpose of this work is to develop a "tremor decomposition method" that can determine which muscles are most responsible for a patient's tremor. In contrast to a "composition method" that would analyze muscle activities to predict a movement at a joint, a "decomposition method" works in the opposite direction, i.e., analyzes the movement at a joint to predict what muscles are involved. The method can be applied to all types of tremor, whether the tremor be pathological (e.g., tremor related to injury or disease) or physiological (e.g., tremor common in healthy adults). Once the origin of the tremor (or treatment target) is identified, the method can be applied to a wide diversity of tremor-suppressing strategies. Finally, tremor is an interdisciplinary problem, and tremor suppression requires expertise in both engineering and neuroscience. Thus, research assistants (undergraduate and graduate students) from both engineering and neuroscience will work together in teams to complete all aspects of this research, leading to graduates who are used to working together on interdisciplinary teams to solve problems of impact. To assist the tremor research community in general, a website will be created that will provide access to datasets and the computational methods developed, including the ability to interact with the data to see which muscles contribute to selected movements. This project is focused on developing and validating a method, termed "tremor decomposition method," to determine which muscles are most responsible for a patient's tremor (to enable targeted tremor suppression in the future). The work builds on the Co-Investigator's recent discovery of a solution to a problem that is mathematically similar: surface electromyogram (sEMG) decomposition. Although sEMG generation and tremor propagation are physically different, they are mathematically analogous. Thus the first objective is to develop the tremor decomposition method by applying the method used to solve sEMG decomposition, i.e., to: 1) model tremor propagation as a mixture of sources filtered by unknown impulse responses, 2) determine if the uniqueness requirement necessary to perform decomposition from measurements of the output (joint displacement) alone is sufficiently satisfied, and 3) adapt, for tremor, two methods (spatial filtering and blind source separation) that proved successful for sEMG decomposition. The second objective is to validate the decomposition method across a range of tremor severities, movement conditions, limb configurations, and subject characteristics using an existing data set collected from patients diagnosed with Essential Tremor. The data set includes both input (sEMG) and output (joint displacement), thus can be used to determine if tremor decomposition can be performed from joint displacement alone. If not, the method will be altered using a method similar to that used in the sEMG decomposition work. The third objective is to apply the decomposition methods to the existing dataset to determine patterns in tremor origin, i.e., to determine the feasibility of time-invariant or even patient-generic tremor-suppressing devices. Although the method is developed in the context of Essential Tremor, the proposed method applies to all types of tremors and has the potential to increase the efficacy of almost all peripheral tremor-suppressing strategies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.