Intraoperative Localization of Subthalamic Nucleus during Deep Brain Stimulation Surgery using Machine Learning Algorithms

This thesis presents a novel technique for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery. DBS is an accepted treatment for individuals living with Parkinson\u27s Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. The STN is a small grey matter structure within the brain, which makes accurate placement a challenging task for the surgical team. Prior to placement of the permanent electrode, intraoperative microelectrode recordings (MERs) of neural activity are used to localize the STN. The placement of the permanent electrode and the success of the stimulation therapy depend on accurate localization. In this study, an objective approach was implemented to help the surgical team in localizing the STN. This is achieved by processing the MER signals and extracting features during the surgery to be used in a Machine Learning algorithm for defining the electrophysiological borders of the STN. A classification approach that can detect the borders of the STN during the operation is proposed. MER signals from 100 PD patients were recorded and used to validate the performance of the proposed method. The results show that by extracting wavelet transformation features from MER signals and using a deep neural network architecture, it is possible to detect the border of the STN with an accuracy of 92%. The proposed method can be implemented in real-time during the surgery to assist the surgical team with the goal of enhancing the accuracy and consistency of electrode placement in the STN

Training in the practice of noninvasive brain stimulation: Recommendations from an IFCN committee

As the field of noninvasive brain stimulation (NIBS) expands, there is a growing need for comprehensive guidelines on training practitioners in the safe and effective administration of NIBS techniques in their various research and clinical applications. This article provides recommendations on the structure and content of this training. Three different types of practitioners are considered (Technicians, Clinicians, and Scientists), to attempt to cover the range of education and responsibilities of practitioners in NIBS from the laboratory to the clinic. Basic or core competencies and more advanced knowledge and skills are discussed, and recommendations offered regarding didactic and practical curricular components. We encourage individual licensing and governing bodies to implement these guidelines

Pathological Tremor as a Mechanical System: Modeling and Control of Artificial Muscle-Based Tremor Suppression

Central nervous system disorders produce the undesired, approximately rhythmic movement of body parts known as pathological tremor. This undesired motion inhibits the patient\u27s ability to perform tasks of daily living and participate in society. Typical treatments are medications and deep brain stimulation surgery, both of which include risks, side effects, and varying efficacy. Since the pathophysiology of tremor is not well understood, empirical investigation drives tremor treatment development. This dissertation explores tremor from a mechanical systems perspective to work towards theory-driven treatment design. The primary negative outcome of pathological tremor is the undesired movement of body parts: mechanically suppressing this motion provides effective tremor treatment by restoring limb function. Unlike typical treatments, the mechanisms for mechanical tremor suppression are well understood: applying joint torques that oppose tremor-producing muscular torques will reduce tremor irrespective of central nervous system pathophysiology. However, a tremor suppression system must also consider voluntary movements. For example, mechanically constraining the arm in a rigid cast eliminates tremor motion, but also eliminates the ability to produce voluntary motions. Indeed, passive mechanical systems typically reduce tremor and voluntary motions equally due to the close proximity of their frequency content. Thus, mechanical tremor suppression requires active actuation to reduce tremor with minimal influence on voluntary motion. However, typical engineering actuators are rigid and bulky, preventing clinical implementations. This dissertation explores dielectric elastomers as tremor suppression actuators to improve clinical implementation potential of mechanical tremor suppression. Dielectric elastomers are often called artificial muscles due to their similar mechanical properties as human muscle; these similarities may enable relatively soft, low-profile implementations. The primary drawback of dielectric elastomers is their relatively low actuation levels compared to typical actuators. This research develops a tremor-active approach to dielectric elastomer-based tremor suppression. In a tremor-active approach, the actuators only actuate to oppose tremor, while the human motor system must overcome the passive actuator dynamics. This approach leverages the low mechanical impedance of dielectric elastomers to overcome their low actuation levels. Simulations with recorded tremor datasets demonstrate excellent and robust tremor suppression performance. Benchtop experiments validate the control approach on a scaled system. Since dielectric elastomers are not yet commercially available, this research quantifies the necessary dielectric elastomer parameters to enable clinical implementations and evaluates the potential of manufacturing approaches in the literature to achieve these parameters. Overall, tremor-active control using dielectric elastomers represents a promising alternative to medications and surgery. Such a system may achieve comparable tremor reduction as medications and deep brain stimulation with minimal risks and greater efficacy, but at the cost of increased patient effort to produce voluntary motions. Parallel advances in scaled dielectric elastomer manufacturing processes and high-voltage power electronics will enable consumer implementations. In addition to tremor suppression, this dissertation investigates the mechanisms of central nervous system tremor generation from a control systems perspective. This research investigates a delay-based model for parkinsonian tremor. Besides tremor, Parkinson\u27s disease generally inhibits movement, with typical symptoms including rigidity, bradykinesia, and increased reaction times. This fact raises the question as to how the same disease produces excessive movement (tremor) despite characteristically inhibiting movement. One possible answer is that excessive central nervous system inhibition produces unaccounted feedback delays that cause instability. This dissertation develops an optimal control model of human motor control with an unaccounted delay between the state estimator and controller. This delay represents the increased inhibition projected from the basal ganglia to the thalamus, delaying signals traveling from the cerebellum (estimator) to the primary motor cortex (controller). Model simulations show increased delays decrease tremor frequency and increase tremor amplitude, consistent with the evolution of tremor as the disease progresses. Simulations that incorporate tremor resetting and random variation in control saturation produce simulated tremor with similar characteristics as recorded tremor. Delay-induced tremor explains the effectiveness of deep brain stimulation in both the thalamus and basal ganglia since both regions contribute to the presence of feedback delay. Clinical evaluation of mechanical tremor suppression may provide clinical evidence for delay-induced tremor: unlike state-independent tremor, suppression of delay-induced tremor increases tremor frequency. Altogether, establishing the mechanisms for tremor generation will facilitate pathways towards improved treatments and cure development

Training in the practice of noninvasive brain stimulation: Recommendations from an IFCN committee

© 2020 As the field of noninvasive brain stimulation (NIBS) expands, there is a growing need for comprehensive guidelines on training practitioners in the safe and effective administration of NIBS techniques in their various research and clinical applications. This article provides recommendations on the structure and content of this training. Three different types of practitioners are considered (Technicians, Clinicians, and Scientists), to attempt to cover the range of education and responsibilities of practitioners in NIBS from the laboratory to the clinic. Basic or core competencies and more advanced knowledge and skills are discussed, and recommendations offered regarding didactic and practical curricular components. We encourage individual licensing and governing bodies to implement these guidelines

Robotic implantation of intracerebral electrodes for deep brain stimulation

Dissertação de mestrado integrado em Engenharia BiomédicaThe objective of this dissertation is to develop an initial approach of a robotic system to play an assistive role in Deep Brain Stimulation (DBS) stereotactic neurosurgery. The robot is expected to position and manipulate several surgical instrumentation in a passive or semi-active role according to pre-operative directives and to medical team instructions. The current impact of neurological disorders sensitive to DBS, the underlying knowledge of neurostimulation and neuroanatomy, and practical insight about DBS surgery is studied to understand the ultimate goal of our project. We elaborated a state of the art search on neurosurgery robots to get the picture of what was done and what could be improved. Upon determining the optimal robotic system characteristics for DBS surgery, we conducted a search on industrial robotic manipulators to select the best candidates. The geometric and differential kinematic equations are developed for each robotic manipulator. To test the kinematic equations and the control application in a virtual operating room environment, we used the CoopDynSim simulator. Being this simulator oriented to mobile robots, we introduced the serial manipulator concept and implemented the selected robots with all specifications. We designed a control application to manoeuvre the robot and devised an initial interface towards positioning/manipulation of instrumentation along surgical trajectories, while emphasizing safety procedures. Although it was impossible to assess the robot’s precision in simulation, we studied how and where to place the manipulator to avoid collisions with surrounding equipment without restricting its flexibility.O objectivo desta dissertação é o desenvolvimento de uma abordagem inicial a um sistema robótico para desempenhar um papel de assistência em neurocirurgia estereotáxica de Estimulação Cerebral Profunda (DBS). O robô deve posicionar e manipular variados instrumentos cirúrgicos de uma forma passiva ou semi-ativa de acordo com diretivas pré-operativas ou com as instruções da equipa médica. O impacto atual dos distúrbios neurológicos sensíveis a DBS, o conhecimento subjacente de neuro-estimulação e neuro-anatomia, e conhecimento prático sobre a cirurgia de DBS são estudados para concluir sobre o objectivo final do nosso projeto. Nós elaborámos uma pesquisa sobre o estado da arte em robots neurocirúrgicos para perceber o que tem sido feito e o que pode ser melhorado. Após determinar o conjunto óptimo de características de um sistema robótico para cirurgia de DBS, nós procuramos manipuladores robóticos industriais para escolher os melhores candidatos. As cinemáticas geométricas e diferenciais são desenvolvidas para cada manipulador robótico. Para testar as equações cinemáticas e a aplicação de controlo num ambiente virtual de uma sala de operações, nós usamos o simulador CoopDynSim. Sendo este manipulador orientado a robôs móveis, nós introduzimos o conceito de manipuladores em série e implementamos os robôs selecionados com todas as especificações. Nós projetamos uma aplicação de controlo para manobrar os robôs e desenvolvemos uma interface inicial no sentido do posicionamento/manipulação de instrumentação ao longo de trajetórias cirúrgicas, enfatizando os procedimentos de segurança. Embora não tenha sido possível avaliar a precisão do robô em simulação, nós estudamos como e onde posicionar o manipulador de forma a evitar colisões com o equipamento circundante sem restringir a sua flexibilidade

EEG and ECoG features for Brain Computer Interface in Stroke Rehabilitation

The ability of non-invasive Brain-Computer Interface (BCI) to control an exoskeleton was used for motor rehabilitation in stroke patients or as an assistive device for the paralyzed. However, there is still a need to create a more reliable BCI that could be used to control several degrees of Freedom (DoFs) that could improve rehabilitation results. Decoding different movements from the same limb, high accuracy and reliability are some of the main difficulties when using conventional EEG-based BCIs and the challenges we tackled in this thesis. In this PhD thesis, we investigated that the classification of several functional hand reaching movements from the same limb using EEG is possible with acceptable accuracy. Moreover, we investigated how the recalibration could affect the classification results. For this reason, we tested the recalibration in each multi-class decoding for within session, recalibrated between-sessions, and between sessions. It was shown the great influence of recalibrating the generated classifier with data from the current session to improve stability and reliability of the decoding. Moreover, we used a multiclass extension of the Filter Bank Common Spatial Patterns (FBCSP) to improve the decoding accuracy based on features and compared it to our previous study using CSP. Sensorimotor-rhythm-based BCI systems have been used within the same frequency ranges as a way to influence brain plasticity or controlling external devices. However, neural oscillations have shown to synchronize activity according to motor and cognitive functions. For this reason, the existence of cross-frequency interactions produces oscillations with different frequencies in neural networks. In this PhD, we investigated for the first time the existence of cross-frequency coupling during rest and movement using ECoG in chronic stroke patients. We found that there is an exaggerated phase-amplitude coupling between the phase of alpha frequency and the amplitude of gamma frequency, which can be used as feature or target for neurofeedback interventions using BCIs. This coupling has been also reported in another neurological disorder affecting motor function (Parkinson and dystonia) but, to date, it has not been investigated in stroke patients. This finding might change the future design of assistive or therapeuthic BCI systems for motor restoration in stroke patients

Determination and quantitative evaluation of image-based registration accuracy for robotic neurosurgery

Stereotactic neurosurgical robots allow quick, accurate location of small targets within the brain, relying on accurate registration of preoperative MRI/CT images with patient and robot coordinate systems. Fiducial markers or a stereotactic frame are used as registration landmarks and the patient’s head is fixed in position. An image-based system could be quick, non-invasive and allow the head to be moved during surgery giving greater ease of access. Submillimetre surgical precision at the target point is required. An octant representation is utilized to investigate full region of interest (ROI) head registration using parts only, with registration performed using the Iterative Closest Point (ICP) algorithm. Use of two octants sequentially obtained a mean RMS distance of 0.813±0.026 mm; adding subsequent octants did not significantly improve performance. An RMS distance of 0.812±0.025 mm was obtained for three octants used simultaneously. ICP was compared with Coherent Point Drift, and 3D Normal Distribution Transform, with and without added or smoothed noise, and was least affected by starting position or noise added; a mean accuracy of 0.884±0.050 mm across ten noise levels and four starting positions was achieved, which was shown to translate to submillimetre accuracy at points within the head

Shaping the future by engineering: 58th IWK, Ilmenau Scientific Colloquium, Technische Universität Ilmenau, 8 - 12 September 2014 ; programme

Druckausgabe erschienen im Universitätsverlag Ilmenau: Shaping the future by engineering : 58th IWK, Ilmenau Scientific Colloquium, Technische Universität Ilmenau, 8 - 12 September 2014 ; programme / Department of Mechanical Engineering, Technische Universität Ilmenau. [Hrsg.: Peter Scharff. Red.: Andrea Schneider] Ilmenau : Univ.-Verl. Ilmenau, 2014. - 155 S. ISBN 978-3-86360-085-

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not