Optimisation of a Wearable Neuromodulator for Migraine Using Computational Methods

Migraine is the third most common neurological disorder and the sixth cause of disability. It may be characterized by a headache, nausea, vomiting, photo- phobia and phonophobia. Available pharmaceutical treatments of migraine are not completely effective and have troublesome side-effects. Thus, there is a need for alternative treatments such as neuromodulation. Neuromodulation may be delivered invasively; however, this exposes the patients to the associated risks. Transcutaneous electrical nerve stimulation is a non-invasive technique that is widely used to relieve pain. A significant number of migraine sufferers complaint the symptoms of pain originating in the frontal region of the head. Thus, mi- graine may be associated with the supraorbital nerve and supratrochlear nerve which passes below the frontal bone exits from the orbital rim and penetrates the corrugator and frontalis muscles. Transcutaneous frontal nerve stimulation has been applied on a large group of patients who have episodic migraine us- ing a device called Cefaly. This study produced mixed results (50% response rate). A post–marketing survey led to 53% satisfaction while the most limiting factor is reported to be paraesthesia and painful sensation. The possible causes of these inconclusive results may be associated with neuroanatomical variations, patient compliance and neurophysiological effects. The most plausible cause may be related to the neuroanatomical variations across different subjects. The neu- roanatomical variations may lead to excessively high current levels being required. Since this solution is patient–operated, these relatively high required levels are not applied. In addition, as the electrodes are positioned near pain–sensitive structures, pain may be induced even at low current levels, further limiting the efficacy of the solution. There has been no robust investigation identifying the underlying causes of ineffi- cacy. This is partly due to the physical limitations of studying the neuroanatomy of each subject and different settings of electrode arrangements. Computational models may enable researchers to estimate current stimulation thresholds in neu- romodulation therapy and investigate the effects of various parameters. Such computational models are composed of a volume conductor model and an ad- vanced Hodgkin–Huxley–type model of neural tissue referred to as a hybrid model. Once the human head anatomy, the human nervous system and available solu- tions for migraine are detailed, the computational model of the human head is generated. A highly detailed human head model based on magnetic resonance imaging (MRI) studies, microscopic structure of the skin(including sweat ducts, keratinocytes and lipid) and those of a simplified head model (which built from geometric shapes) are compared based on neural excitation to assess the usabil- ity of geometrically realistic(simplified) human head models in the subsequent studies to save computations cost. The induced electric field due to an electrode setting is simulated in the volume conductor model and the resulting electric potential values along the nerve are passed on to the neural model to simulate nerve’s response. It is shown that a simplified model may be used with a marginal error (≈2%) in the subsequent work when assessing the effect of neuroanatomical variations on the efficacy of the target solution and possible ensuing optimiza- tions. The first step is to identify if neuroanatomical variations had any effect on the required stimulus current levels using state of the art computational bio–models. Ten realistic human head models are developed by varying thirteen neuroanatom- ical features including human head size, thicknesses of the tissue layers and vari- ations in the courses of the nerve by considering their respective statistical distributions as reported in the literature. A novel algorithm is developed to account for the variations of the nerve in different individuals and mimic statistically relevant large population. In each case, the required stimulus current levels are simulated. The findings show that the combined neuroanatomical variations have a significant effect on the neural response for the electrode setting used in Cefaly device. Therefore, a potential improvement is to align the axis of electrodes with the target nerve, so that the electrical potential along the trajectory of the nerve changes polarity. This may lead to lower required stimulus current levels. Align- ing electrodes with the nerve, the required current may be reduced by at least 60%. This new orientation reduces current density near pain– sensitive struc- tures by diverting the current away from them, which may lead to a higher level of patient compliance, further improving the efficacy of the solution. Using an electrodes array arrangement, the required current levels is further reduced due to incorporating multiple electrodes array elements to maximise the variations of the electrical field in the simulation of the fibres in one phase. The findings of this thesis indicate that the highly detailed human head model can be simplified while minimally affecting the outcome. Additionally, it is shown that neuroanatomical variations have a significant impact on the stimulus current thresholds but it is not possible to conclude if these thresholds solely depend on a specific neuroanatomical variation. The relatively high required levels of the stimulus currents are beyond the current capabilities of existing device and pos- sible pain thresholds. Furthermore, the proposed new electrode arrangement has multiple benefits including the reduction of the stimulus current levels and diver- sion of current spread from possible pain–sensitive structures. This improvement, based on modelling, can potentially improve the clinical outcome of the neuro- modulator substantially if confirmed in the subsequent clinical studies

Control of Quarter-Car Active Suspension System Based on Optimized Fuzzy Linear Quadratic Regulator Control Method

Vehicle suspension systems, which affect driving performance and passenger comfort, are actively researched with the development of technology and the insufficient quality of passive suspension systems. This paper establishes the suspension model of a quarter of the car and active control is realized. The suspension model was created using the Lagrange-Euler method. LQR, fuzzy logic control (FLC), and fuzzy-LQR control algorithms were developed and applied to the suspension system for active control. The purpose of these controllers is to improve car handling and passenger comfort. Undesirable vibrations occur in passive suspension systems. These vibrations should be reduced using the proposed control methods and a robust system should be developed. To enhance the performance of the fuzzy logic control (FLC) and fuzzy-LQR control methods, the optimal values of the coefficients of the points where the feet of the member functions touch are calculated using the particle swarm optimization (PSO) algorithm. Then, the designed controllers were simulated in the computer environment. The success of the control performance of the applied methods concerning the passive suspension system was compared in percentages. The results are presented and evaluated graphically and numerically. Using the integral time-weighted absolute error (ITAE) criterion, the methods were compared with each other and with the studies in the literature. As a result, it was found that the proposed control method (fuzzy-LQR) is about 84.2% more successful in body motion, 90% in car acceleration, 84.5% in suspension deflection, and 86.7% in tire deflection compared to the studies in the literature. All these results show that the car's ride comfort has been significantly improved

A fast and reliable three-dimensional centerline tracing: application to virtual cochlear implant surgery

This paper presents a rapid and unsupervised three-dimensional (3D) tubular structure tracing algorithm for the detection of safe trajectories in cochlear surgery. The algorithm utilizes a generalized 3D cylinder model which offers low-order parameterization, enabling low-cost recursive directional tubular boundary analysis and derivation of tubular statistics (i.e. centerline coordinates). Unlike previous work, the proposed algorithm circumvents excessive computation per voxel while enhancing angular centerline traversing efficiency which is critical in cochlear implant surgery navigation. To accomplish this, design considerations include: 1) accurate engineering of kernels used for border analysis, 2) modifying decision-making in identifying optimal tracing angle with homogeneity criterion, 3) reducing tubular change exploratory search cost through discrete convolution analysis, and 4) a cross-section calibration engine which suppresses centerline angular deviations as well as recording a history of geometrical changes while tracing. When evaluated on synthetic imagery mimicking cochlea structural complexity and real reconstructed cochlea models, it consistently produced accurate estimates of centerline coordinates and widths-heights in the presence of noise and spatial artefacts. Validation has shown that the centerline error for the proposed algorithm is below 6 pixels and the average traced pixel performance is 92.9% of the true centerline pixels on the examined cochlea models. By restricting the image analysis to the regions of interest, the proposed algorithm performs rapid centerline tracing of the cochlea needed for real-time surgery (0.48 seconds per electrode insertion).</p