Modeling the neurophysiology of tremor to develop a peripheral neuroprosthesis for tremor suppression

Pathological tremor is an involuntary oscillation of the body parts around joints. Pharmaceu- ticals and surgical treatments are approved approaches for tremor management; however, their side effects limit their usability. The main objective of this study is, therefore, to design a closed-loop non-invasive electrical stimulation system that could suppress tremor without serious side effects. We started our system design by investigating motor unit (MU) behaviors during postural tremor via decomposition of high-density surface electromyography (EMG) recordings of antagonist pairs of wrist muscles of essential tremor (ET) patients. The common input strength that influences voluntary and tremor movements and the phase difference between activation of motor neurons in antagonist pairs of muscles were assessed to find the correlation of the motor unit activity during different tasks. We observed that, during postural tremor, the motor units in antagonist pairs of muscles were activated with a phase difference that varies over time. An online EMG decomposition method and a phase-locked-loop system were, therefore, implemented in our tremor suppression system to real-timely discriminate motor unit discharge timings, track the phase of the motor unit activity and use that real-time phase estimation to control the stimulation timing. We applied sub-threshold stimulation to the muscle pairs in an out-of-phase manner. The system was validated offline with the data recorded from 13 ET patients before it was tested with an ET patient to prove the concept. Since the spinal cord is the termination of the afferent neurons from the peripheral nervous system and connection to the central nervous system and motor neurons, we hypothesized that electrical stimulation at the spinal cord could also modulate tremor-related neural commands. Russian currents with a 5 kHz-carrier frequency modulated with a slow burst at tremor frequencies were used with sub-threshold intensity to stimulate at C5-C6 cervical spine of 9 ET patients. The reduction of the tremor power was observed via an analysis of the wrist angle recorded using an accelerometer. We present, in this thesis, two electrical stimulation approaches for tremor suppression via the peripheral nerves and spinal cord, providing options for patients to utilize based on their preference.Open Acces

Can the Voluntary Drive to a Paretic Muscle be Estimated from the Myoelectric Signal during Stimulation?

Patients with SCI sometimes recover lost function after using FES. This phenomenon, known as the carry-over effect, is not fully understood. One theory used to explain this mechanism is that electrical stimulation of the peripheral nerve causes antidromic action potentials to reach the anterior horn cells in time with the patient’s voluntary effort. This may reinforce the motor pathways and consequently restore voluntary control. However, the theory has never been properly tested and testing requires a method of measuring the voluntary drive. This project aims to find out whether it is possible to estimate the voluntary drive from measured myoelectric signals. The project is based on an FES cycling system with the ability to adjust the stimulation intensity relating to the corresponding voluntary drive. In paretic muscles, the weak voluntary contraction produces an EMG response. The EMG signal cannot be used directly as an indication of the voluntary drive because of the presence of stimulus artefact and reflexes. Two methods were investigated to estimate the voluntary drive. A time domain method was tested using RMS EMG extracted from a range of time windows following the stimulation pulse. This approach was unsatisfactory because the large variations seen in the RMS EMG amplitudes for the same power output as well as the low sensitivity of it to the change of power output. A frequency domain approach was then tested using coherence between co-contracting muscles. It was encouraging to see that the area under the coherence curve in the β band reflected changes in the power output level. However, further tests showed that this area was also greatly influenced by exercise time, becoming unpredictable after 3 minutes. In conclusion, neither of the two methods of using the myoelectric signal from muscles under stimulation is practical for the estimation of voluntary drive

Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices

Excellent motor control skills are necessary to live a high-quality life. Activities such as walking, getting dressed, and feeding yourself may seem mundane, but injuries to the neuromuscular system can render these tasks difficult or even impossible to accomplish without assistance. Statistics indicate that well over 100 million people are affected by diseases or injuries, such as stroke, Parkinson’s Disease, Multiple Sclerosis, Cerebral Palsy, peripheral nerve injury, spinal cord injury, and amputation, that negatively impact their motor abilities. This wide array of injuries presents a challenge to the medical field as optimal treatment paradigms are often difficult to implement due to a lack of availability of appropriate assessment tools, the inability for people to access the appropriate medical centers for treatment, or altogether gaps in technology for treating the underlying impairments causing the disability. Addressing each of these challenges will improve the treatment of movement impairments, provide more customized and continuous treatment to a larger number of patients, and advance rehabilitative and assistive device technology. In my research, the key approach was to develop tools to assess and treat upper extremity movement impairment. In Chapter 2.1, I challenged a common biomechanical[GV1] modeling technique of the forearm. Comparing joint torque values through inverse dynamics simulation between two modeling platforms, I discovered that representing the forearm as a single cylindrical body was unable to capture the inertial parameters of a physiological forearm which is made up of two segments, the radius and ulna. I split the forearm segment into a proximal and distal segment, with the rationale being that the inertial parameters of the proximal segment could be tuned to those of the ulna and the inertial parameters of the distal segment could be tuned to those of the radius. Results showed a marked increase in joint torque calculation accuracy for those degrees of freedom that are affected by the inertial parameters of the radius and ulna. In Chapter 2.2, an inverse kinematic upper extremity model was developed for joint angle calculations from experimental motion capture data, with the rationale being that this would create an easy-to-use tool for clinicians and researchers to process their data. The results show accurate angle calculations when compared to algebraic solutions. Together, these chapters provide easy-to-use models and tools for processing movement assessment data. In Chapter 3.1, I developed a protocol to collect high-quality movement data in a virtual reality task that is used to assess hand function as part of a Box and Block Test. The goal of this chapter is to suggest a method to not only collect quality data in a research setting but can also be adapted for telehealth and at home movement assessment and rehabilitation. Results indicate that the data collected in this protocol are good and the virtual nature of this approach can make it a useful tool for continuous, data driven care in clinic or at home. In Chapter 3.2 I developed a high-density electromyography device for collecting motor unit action potentials of the arm. Traditional surface electromyography is limited by its ability to obtain signals from deep muscles and can also be time consuming to selectively place over appropriate muscles. With this high-density approach, muscle coverage is increased, placement time is decreased, and deep muscle activity can potentially be collected due to the high-density nature of the device[GV2] . Furthermore, the high-density electromyography device is built as a precursor to a high-density electromyography-electrical stimulation device for functional electrical stimulation. The customizable nature of the prototype in Chapter 3.2 allows for the implementation both recording and stimulating electrodes. Furthermore, signal results show that the electromyography data obtained from the device are of high quality and are correlated with gold standard surface electromyography sensors. One key factor in a device that can record and then stimulate based on the information from the recorded signals is an accurate movement intent decoder. High-quality movement decoders have been designed by closed-loop device controllers in the past, but they still struggle when the user interacts with objects of varying weight due to underlying alterations in muscle signals. In Chapter 4, I investigate this phenomenon by administering an experiment where participants perform a Box and Block Task with objects of 3 different weights, 0 kg, 0.02 kg, and 0.1 kg. Electromyography signals of the participants right arm were collected and co-contraction levels between antagonistic muscles were analyzed to uncover alterations in muscle forces and joint dynamics. Results indicated contraction differences between the conditions and also between movement stages (contraction levels before grabbing the block vs after touching the block) for each condition. This work builds a foundation for incorporating object weight estimates into closed-loop electromyography device movement decoders. Overall, we believe the chapters in this thesis provide a basis for increasing availability to movement assessment tools, increasing access to effective movement assessment and rehabilitation, and advance the medical device and technology field

The Use of Skeletal Muscle to Amplify Action Potentials in Transected Peripheral Nerves

Upper limb amputees suffer with problems associated with control and attachment of prostheses. Skin-surface electrodes placed over the stump, which detect myoelectric signals, are traditionally used to control hand movements. However, this method is unintuitive, the electrodes lift-off, and signal selectivity can be an issue. One solution to these limitations is to implant electrodes directly on muscles. Another approach is to implant electrodes directly into the nerves that innervate the muscles. A significant challenge with both solutions is the reliable transmission of biosignals across the skin barrier. In this thesis, I investigated the use of implantable muscle electrodes in an ovine model using myoelectrodes in combination with a bone-anchor, acting as a conduit for signal transmission. High-quality readings were obtained which were significantly better than skin-surface electrode readings. I further investigated the effect of electrode configurations to achieve the best signal quality. For direct recording from nerves, I tested the effect of adsorbed endoneural basement membrane proteins on nerve regeneration in vivo using microchannel neural interfaces implanted in rat sciatic nerves. Muscle and nerve signal recordings were obtained and improvements in sciatic nerve function were observed. Direct skeletal fixation of a prosthesis to the amputation stump using a bone-anchor has been proposed as a solution to skin problems associated with traditional socket-type prostheses. However, there remains a concern about the risk of infection between the implant and skin. Achieving a durable seal at this interface is therefore crucial, which formed the final part of the thesis. Bone-anchors were optimised for surface pore size and coatings to facilitate binding of human dermal fibroblasts to optimise skin-implant seal in an ovine model. Implants silanised with Arginine-Glycine-Aspartic Acid experienced significantly increased dermal tissue infiltration. This approach may therefore improve the soft tissue seal, and thus success of bone-anchored implants. By addressing both the way prostheses are attached to the amputation stump, by way of direct skeletal fixation, as well as providing high fidelity biosignals for high-level intuitive prosthetic control, I aim to further the field of limb loss rehabilitation

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Age-Sensitive Features for Detection of Muscle Fatigue using the High-Density Electromyogram

The processes behind fatigue development within the muscles have been a topic of interest for exercise scientists for decades. This is because fatigue is one of the primary reasons for a decrease in performance and increase in likelihood of injury during exercise[1]. Typically, muscle fatigue is detected through modifications of the amplitude and spectral characteristics of a surface electromyogram (sEMG), or the variability of torque signals recorded throughout a sustained contraction. However, the behaviour of these parameters with the generation of fatigue depends on a variety of factors. One major factor is age, where the age-related loss of muscle fibers, and changes in neuromuscular system impact how muscles adapt to and develop fatigue. The purpose of this study was to examine age-sensitive High Density Surface Electromyogram (HD-sEMG) features and investigate the effect of spatial filter type on intramuscular coherence analysis in fatigue detection. Fatiguing submaximal isometric contractions of the bicep brachii was performed by eight young (24.40 ± 2.42 years) and five elderly (72.90 ± 2.21 years) males, while HD-sEMG recorded signals from the biceps brachii and a dynamometer recorded torque signals. The task was performed at 20% maximal voluntary contraction (MVC). From the HD-sEMG signals, the mean intramuscular coherence was calculated in the alpha (11-15Hz), beta (16-29Hz), and gamma (30-50Hz) frequency bands each of which stems from different neurological origins. Statistical differences were only found in the alpha (p=0.0006), and beta (p=0.0207) bands between the pre-and post-fatigue conditions of the young group. Furthermore, a correlation between mean coherence and torque variability during the final 25% of the contraction before task failure revealed that both the age groups had positive correlation in the alpha band. Different correlations were found in the beta and gamma bands, with positive correlations being observed in the elderly group and negative correlations in the young group. These results suggest that age-related changes in the corticospinal pathway exist causing the elderly to be less fatigable when compared to the young population. This proposes that the introduced intramuscular coherence analysis can be used to obtain fatigue related features from HD-sEMG signals that are age-sensitive

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

The Slippery Slope Between Falling And Recovering: An Examination Of Sensory And Somatic Factors Influencing Recovery After A Slip

Background: Slips and falls account for large rates of injury and mortality in multiple populations. During an unexpected slip, sensory mechanisms are responsible for signaling the slip to the central nervous system, and a series of corrective responses is generated to arrest the slip and prevent a fall. While previous research has examined the corrective responses elicited, the answer of how these systems break down during a fall remains elusive. Purpose: To examine differences in postural control (slip detection), lower extremity corrective responses (slip recovery), and cortical control of the slip recovery response between individuals who fall and those who recover. Methods: One hundred participants were recruited for this study (50 males & 50 females). Participant’s gait kinematics and kinetics were collected during normal gait (NG) and an unexpected slip (US). The slip was classified as a fall or recovery, and by slip severity. Once classified, postural control, reaction times, corrective moments, and cortical contribution were examined between groups using ANOVAs and independent t-tests. Additionally, prediction equations for slip outcome, and slip severity were created using a binary logistic regression model. Slip Detection Results: Postural sway when the proprioceptive (OR = 0.02, CI: 0.01-1.34) and vestibular (OR = 0.60, CI: 0.26-1.39) systems are stressed were negatively associated with odds of falling. While postural sway when the visual system was stressed (OR = 3.18, CI: 0.887-11.445) was positively associated with odds of falling. Slip Recovery Results: Increased time to peak hip extension (OR = 1.006, CI: 1.00-1.01) and ankle dorsiflexion (OR = 1.005, CI: 1.00-1.01) moments increased the odds of falling. While the average ankle moment was negatively associated with falling (OR = 0.001, CI: 0.001-0.005). Cortical Contribution Results: Spectral power in the Piper frequency band was increased in US trials compared to NG. Further, fallers exhibited an increase in cortical activity compared to those who recovered. Conclusions: Rapid lower extremity corrective responses appear critical in arresting the slip and preventing a fall, and the temporal nature of this response may depend on slip detection and subsequent response selection. Moreover, our results suggest that more severe slips may require increased activation of higher centers of the motor cortex