Investigation of the baroreflex of the rat : steady state and dynamic features

The baroreflex is one of the most important feedback systems in the body to maintain blood pressure variation within the homeostatic range. In this dissertation, the important features of the carotid and aortic baroreflexes have been extensively investigated on ventilated, central nervous system intact, neuromuscular blocked (NMB) rats using different control system and signal processing tools. Studies have demonstrated that sinoaortic denervation (SAD) caused substantial increases in the blood pressure variability. Comparing the pre- and post-SAD blood pressure spectra, there was a significant increase of power in the very low frequency region (0.00195 -0.2 Hz), and a significant decrease of power in the low frequency region (0.2 - 0.6 Hz) after SAD. The dominant power change after SAD was in the very low frequency region of the blood pressure spectra. The carotid and aortic baroreflexes were accessed by volumetric manipulation of the carotid sinus and electrical manipulation of the aortic depressor nerve (ADN) using step and sinusoidal stimulations. Myelinated ADN-A fibers and myelinated + unmyelinated ADN-A+C fibers were accessed separately in the experiments. Results showed that the baroreflex functions as a \u27low-pass\u27 filter, with -3dB cutoff frequency at approximately \u3c0. I Hz. The major working area of the baroreflex system is in the VLF region of the blood pressure spectra. The estimated system transportation lag was 1.07s, which would cause the baroreflex system to oscillate at frequencies around 0.4 Hz. Analyses demonstrated that it is not likely that the baroreflex is activated only occasionally, such as in response to postural shifts, but operates continuously to bring the blood pressure into balance. It is theoretically and experimentally demonstrated that the absolute gain of the open-loop baroreflex system can be predicted by the ratio of the pre-and post- blood pressure amplitude spectra

Doctor of Philosophy

dissertationMedical intervention to restore motor function lost due to injury, stroke, or disease is increasingly common. Recent research in this field, known as functional electrical stimulation (FES), has produced a new generation of electrode devices that greatly enhance selectivity of access to neural populations, enabling-for the first time-restoration of motor function approaching what healthy humans enjoy. Research with these devices, however, has been severely hampered by the lack of a stimulation platform and control algorithms capable of exploring their full potential. The following dissertation presents the results of research aimed at addressing this problem. A major theme of this work is the use of software algorithms and analysis principles to facilitate both investigation and control of the motor system. Though many of the algorithms are well known in computer science, their application to the field of motor restoration is novel. Associated with use of these algorithms are important methodological considerations such as speed of execution, convergence, and optimality. The first phase of the research involved development of a hardware and software platform designed to support a wide range of closed-loop response mapping and control routines. Software routines to automate three time-consuming tasks-mapping stimulus thresholds, mapping stimulus-response recruitment curves, and mapping electrode pair excitation overlap- were implemented and validated in a cat model. Computer control, combined with the use of an efficient binary search algorithm, reduced the time need to complete required implant mapping tasks by a factor of 4 or more (compared to manual mapping), making feasible-for the first time-acute experiments investigating multi-array, multijoint experimental limb control. The second phase of the research involved investigating the influence of stimulus timing, within multielectrode trains, on the smoothness of evoked muscle responses. A model for predicting responses was developed and used, in conjunction with function optimization techniques, to identify stimulus timings that minimize response variation (ripple). In-vivo validation demonstrated that low-ripple timings can be identified, and that the influence of timing on ripple depends largely on the response kinetics of the motor unit pools recruited by constituent electrodes. The final phase of the research involved using the response prediction model to simulate the behavior of a feedback-based, stimulus-timing adjustment algorithm. Multiple simulations were executed to assess the influence of three algorithm parameters-filter bandwidth, error sampling delay, and timing adjustment gain-on two performance metrics- convergence time and percent reduction in ripple. Results show that all parameters have an influence on algorithm performance. Convergence speed is the metric most a↵ected by parameter adjustment, improving by a factor of more than 3 (13 cycles to approximately 4 cycles). Ripple reduction is also a↵ected-exhibiting a 17% reduction with appropriate selection of error sampling delay. These results demonstrate the value of using this simulation approach for parameter tuning

Preparation of NiO catalyst on FeCrAI substrate using various techniques at higher oxidation process

The cheap nickel oxide (NiO) is a potential catalyst candidate to replace the expensive available platinum group metals (PGM). However, the current methods to adhere the NiO powder on the metallic substrates are complicated. Therefore, this work explored the development of nickel oxide using nickel (Ni) on FeCrAl substrate through the combination of nickel electroplating and oxidation process for catalytic converter application. The approach was started with assessment of various nickel electroplating process based on the weight gain during oxidation. Then, the next experiment used the best process in which the pre-treatment using the solution of SiC and/or Al2O3 in methanol. The specimens then were carried out to short term oxidation process using thermo gravimetric analysis (TGA) at 1000 o C. Meanwhile, the long term oxidation process was conducted using an automatic furnace at 900, 1000 and 1100 o C. The atomic force microscopy (AFM) was used for surface analysis in nanometer range scale. Meanwhile, roughness test was used for roughness measurement analysis in micrometer range scale. The scanning electron microscope (SEM) attached with energy dispersive X-ray (EDX) were used for surface and cross section morphology analysis. The specimen of FeCrAl treated using ultrasonic prior to nickel electroplating showed the lowest weight gain during oxidation. The surface area of specimens increased after ultrasonic treatment. The electroplating process improved the high temperature oxidation resistance. In short term oxidation process indicated that the ultrasonic with SiC provided the lower parabolic rate constant (kp) and the Al2O3 and NiO layers were also occurred. The Ni layer was totally disappeared and converted to NiO layer on FeCrAl surface after long term oxidation process. From this work, the ultrasonic treatment prior to nickel electroplating was the best method to adhere NiO on FeCrAl substrate

Doctor of Philosophy

dissertationParalysis due to spinal cord injury or stroke can leave a person with intact peripheral nerves and muscles, but deficient volitional motor control, thereby reducing their health and quality of life. Functional neuromuscular stimulation (FNS) has been widely studied and employed in clinical devices to aid and restore lost or deficient motor function. Strong, selective, and fatigue-resistant muscle forces can be evoked by asynchronously stimulating small independent groups of motor neurons via multiple intrafascicular electrodes on an implanted Utah slanted electrode array (USEA). Determining the parameters of asynchronous intrafascicular multi-electrode stimulation (aIFMS), i.e., the per-electrode stimulus intensities and the interelectrode stimulus phasing, to evoke precise muscle force or joint motion presents unique challenges because this system has multiple-inputs, the n independently stimulated electrodes, but only one measurable output, the evoked endpoint isometric force or joint position. This dissertation presents three studies towards developing robust real-time control of aIFMS. The first study developed an adaptive feedforward algorithm for selecting aIFMS per-electrode stimulus intensities and interelectrode stimulus phasing to evoke a variety of isometric ankle plantar-flexion force trajectories. In simulation and experiments, desired step, sinusoidal, and more-complex time-varying isometric forces were successfully evoked. The second study developed a closed-loop feedback control method for determining aIFMS per-electrode stimulus intensities to evoke precise single-muscle isometric ankle plantar-flexion force trajectories, in real-time. Using a proportional closed-loop force-feedback controller, desired step, sinusoid, and more complex time-varying forces were evoked with good response characteristics, even in the presence of nonlinear system dynamics, such as muscle fatigue. The third study adapted and extended the closed-loop feedback controller to the more demanding task of controlling joint position in the presence of opposing joint torques. A proportional-plus-velocity-plus-integral (PIV) joint-angle feedback controller evoked and held desired steps in position with responses th a t were stable, consistent, and robust to disturbances. The controller evoked smooth ramp-up (concentric) and ramp-down (eccentric) motion, as well as precise slow moving sinusoidal motion. The control methods developed in this dissertation provide a foundation for new lower-limb FNS-based neuroprostheses that can generate sustained and coordinated muscle forces and joint motions that will be desired by paralyzed individuals on a daily basis. proportional-plus-velocity-plus-integral (PIV) joint-angle feedback controller evoked and held desired steps in position with responses th a t were stable, consistent, and robust to disturbances. The controller evoked smooth ramp-up (concentric) and ramp-down (eccentric) motion, as well as precise slow moving sinusoidal motion. The control methods developed in this dissertation provide a foundation for new lower-limb FNS-based neuroprostheses that can generate sustained and coordinated muscle forces and joint motions that will be desired by paralyzed individuals on a daily basis

A STUDY ON DYNAMIC SYSTEMS RESPONSE OF THE PERFORMANCE CHARACTERISTICS OF SOME MAJOR BIOPHYSICAL SYSTEMS

Dynamic responses of biophysical systems - performance characteristic

Enhanced recording paradigms and advanced analyses of peripheral nerve fibers SPiike software

[eng] The aim of this work is to investigate the human nociceptive system at the peripheral level. Researchers are still debating how the pain perception arises from this very intricate network. The human perception is the most elusive part of our knowledge since different subsystems are involved. The external information such as noxious stimuli must be processed at the peripheral level and through signal cascades and transduction this signal must reach the brain. At the brain level the information is processed and some decisions are taken, such as the well-known fight-or-flight response. In the introduction, the author describes how the human nociceptive system works and in which way the noxious stimulus is converted into a signal understandable by the brain. Several cortical and subcortical areas are involved in this signal processing and going deeper in this assembly line the information becomes more abstracted. The whole pathway is fundamental for pain perception, however some diseases start at the peripheral level. This in turn makes wrong signals reaching the brain. The brain is then processing information that are not real and the responses do not suit with the needs. Therefore, the peripheral system must be investigated and understood firstly, since some central diseases may have a peripheral component as well. With this purpose in mind the microneurography technique has been used. This technique has got some complexity and a computer-aided system must be implemented. The hardware aims to filter out the noisy signal and perform recording and stimulation of the neural fibers. The software is instead used to make the stimulation and recording as automatic as possible in a way that researchers do not have to deal with a lot of parameters and steps to carry out this powerful but also time consuming technique. Some software are already available in the market however even if they work fine with slow conduction fibers such as C-fibers they cannot cope with faster neurons (e.g. Aδ fibers). The aim of this work is to create a software (i.e. SPiike) able to stimulate and record every type of fibers implementing advanced analysis technique as well. Furthermore, considering that some in vivo experiments have been pursued within the project to check the functionality of the software, more specifically in rats and mice, the comparison between human nociceptors and mouse nociceptors is depicted in this section. In the method section, the experimental approach is described step by step. This is composed by several systems that work together for the stimulation, recording and analysis of the neural fibers. The control and acquisition module is composed by the software and a data acquisition board that trigger the stimulator and record the filtered signal. The stimulation module is composed by a stimulator that can be tuned as wish through dedicated knobs. Then the stimulus is delivered to the animal model (or the human patient) and the signal is recorded though a microelectrode inserted into the sciatic nerve. The amplification module is filtering out the noisy signal and is feeding a audio monitor for helping the researcher during the insertion of the electrode inside the nerve and it provides support during the whole experiment giving insights on fiber discharges. In this section the whole setup is described in details as well as the devices needed for the recording. Furthermore, the software development that is the core of this project is described as well, with all the considerations that must be considered during coding. Indeed, the flow chart must be followed methodically in order to minimize bugs and errors that may arise in the final product. Thus a description of the compiler and the Matlab IDE is given along with system and software requirements for the making of the SPiike software. Eventually the explanation of embedded functionalities and capabilities of SPiike is depicted in the final part of this section. This software is indeed able to stimulate slow conducting fibers as well as faster ones, and enhanced analysis techniques such as supervised machine learning are implemented. In the results section, the graphical user interface of the Spiike software is reveled. It resembles the one of another software already available in the market, with a filtered signal and a raster plot embedded on it. However, this software is more user-friendly and it accounts with icons and drop-down menus that enhance the experience of the users during the use of the tool, making their interactions smooth and intuitive. The SPiike software is subdivide into two different tools, a recording module and a analysis module. The former allows the stimulation and recording of neural fibers with a stimulation frequency up to 1000Hz and some online analysis can be conducted to have insights on fibers type and behavior. The analysis module is instead a more powerful analysis environment that can retrieve the dataset recorded with the other module or with the LabChart software. Advanced analysis techniques are implemented in this module, this is meant to speed up fiber classification and analysis. Conclusion and discussion provide a overview on some results. These will be compared to those obtainable through other software available in the market. In this section, pros and cons of the new implemented software, SPiike, will be described as well

The Mechanisms and Roles of Neural Feedback Loops for Visual Processing

Feedback pathways are widely present in various sensory systems transmitting time-delayed and partly-processed information from higher to lower visual centers. Although feedback loops are abundant in visual systems, investigations focusing on the mechanisms and roles of feedback in terms of micro-circuitry and system dynamics have been largely ignored. Here, we investigate the cellular, synaptic and circuit level properties of a cholinergic isthmic neuron: Ipc) to understand the role of isthmotectal feedback loop in visual processing of red-ear turtles, Trachemys scripta elegans. Turtle isthmotectal complex contains two distinct nuclei, Ipc and Imc, which interact exclusively with the optic tectum, but are otherwise isolated from other brain areas. The cholinergic Ipc neurons receive topographic glutamatergic inputs from tectal SGP neurons and project back to upper tectal layers in a topographic manner while GABAergic Imc neurons, which also get inputs from the SGP neurons project back non-topographically to both the tectum and Ipc nucleus. We have used an isolated eye-attached whole-brain preparation for our investigations of turtle isthmotectal feedback loop. We have investigated the cellular properties of the Ipc neurons by whole-cell blind-patch recordings and found that all Ipc neurons exhibit tonic firing responses to somatic current injections that are well-modeled by a leaky integrate-and-fire neuron with spike rate adaptation. Further investigations reveal that the optic nerve stimulations generate balanced excitatory and inhibitory synaptic currents in the Ipc neurons. We have also found that synaptic connection between the Imc to Ipc neuron is inhibitory. The visual response properties of the Ipc neurons to a range of computer-generated stimuli are investigated using extracellular recordings. We have found that the Ipc neurons have a localized excitatory receptive field and show stimulus selectivity and stimulus-size tuning. We also investigate lateral interactions in the Ipc neurons in response to multiple stimuli within the visual field. Finally, we quantify the oscillatory bursts observed in Ipc responses under visual stimulations

User friendly knowledge acquisition system for medical devices actuation

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaInternet provides a new environment to develop a variety of applications. Hence, large amounts of data, increasing every day, are stored and transferred through the internet. These data are normally weakly structured making information disperse, uncorrelated, non-transparent and difficult to access and share. Semantic Web, proposed by theWorldWideWeb Consortium (W3C), addresses this problem by promoting semantic structured data, like ontologies, enabling machines to perform more work involved in finding, combining, and acting upon information on theWeb. Pursuing this vision, a Knowledge Acquisition System (KAS) was created, written in JavaScript using JavaScript Object Notation (JSON) as the data structure and JSON Schema to define that structure. It grants new ways to acquire and store knowledge semantically structured and human readable. Plus, structuring data with a Schema generates a software robust and error – free. A novel Human Computer Interaction (HCI) framework was constructed employing this KAS, allowing the end user to configure and control medical devices. To demonstrate the potential of this tool, we present the configuration and control of an electrostimulator. Nowadays, most of the software for Electrostimulation is made with specific purposes, and in some cases they have complicated user interfaces and large, bulky designs that deter usability and acceptability. The HCI concedes the opportunity to configure and control an electrostimulator that surpasses the specific use of several electrostimulator software. In the configuration the user is able to compile different types of electrical impulses (modes) in a temporal session, automating the control, making it simple and user-friendly