Matching Items (77)
Description
From time immemorial, epilepsy has persisted to be one of the greatest impediments to human life for those stricken by it. As the fourth most common neurological disorder, epilepsy causes paroxysmal electrical discharges in the brain that manifest as seizures. Seizures have the effect of debilitating patients on a physical and psychological level. Although not lethal by themselves, they can bring about total disruption in consciousness which can, in hazardous conditions, lead to fatality. Roughly 1\% of the world population suffer from epilepsy and another 30 to 50 new cases per 100,000 increase the number of affected annually. Controlling seizures in epileptic patients has therefore become a great medical and, in recent years, engineering challenge.
In this study, the conditions of human seizures are recreated in an animal model of temporal lobe epilepsy. The rodents used in this study are chemically induced to become chronically epileptic. Their Electroencephalogram (EEG) data is then recorded and analyzed to detect and predict seizures; with the ultimate goal being the control and complete suppression of seizures.
Two methods, the maximum Lyapunov exponent and the Generalized Partial Directed Coherence (GPDC), are applied on EEG data to extract meaningful information. Their effectiveness have been reported in the literature for the purpose of prediction of seizures and seizure focus localization. This study integrates these measures, through some modifications, to robustly detect seizures and separately find precursors to them and in consequence provide stimulation to the epileptic brain of rats in order to suppress seizures. Additionally open-loop stimulation with biphasic currents of various pairs of sites in differing lengths of time have helped us create control efficacy maps. While GPDC tells us about the possible location of the focus, control efficacy maps tells us how effective stimulating a certain pair of sites will be.
The results from computations performed on the data are presented and the feasibility of the control problem is discussed. The results show a new reliable means of seizure detection even in the presence of artifacts in the data. The seizure precursors provide a means of prediction, in the order of tens of minutes, prior to seizures. Closed loop stimulation experiments based on these precursors and control efficacy maps on the epileptic animals show a maximum reduction of seizure frequency by 24.26\% in one animal and reduction of length of seizures by 51.77\% in another. Thus, through this study it was shown that the implementation of the methods can ameliorate seizures in an epileptic patient. It is expected that the new knowledge and experimental techniques will provide a guide for future research in an effort to ultimately eliminate seizures in epileptic patients.
In this study, the conditions of human seizures are recreated in an animal model of temporal lobe epilepsy. The rodents used in this study are chemically induced to become chronically epileptic. Their Electroencephalogram (EEG) data is then recorded and analyzed to detect and predict seizures; with the ultimate goal being the control and complete suppression of seizures.
Two methods, the maximum Lyapunov exponent and the Generalized Partial Directed Coherence (GPDC), are applied on EEG data to extract meaningful information. Their effectiveness have been reported in the literature for the purpose of prediction of seizures and seizure focus localization. This study integrates these measures, through some modifications, to robustly detect seizures and separately find precursors to them and in consequence provide stimulation to the epileptic brain of rats in order to suppress seizures. Additionally open-loop stimulation with biphasic currents of various pairs of sites in differing lengths of time have helped us create control efficacy maps. While GPDC tells us about the possible location of the focus, control efficacy maps tells us how effective stimulating a certain pair of sites will be.
The results from computations performed on the data are presented and the feasibility of the control problem is discussed. The results show a new reliable means of seizure detection even in the presence of artifacts in the data. The seizure precursors provide a means of prediction, in the order of tens of minutes, prior to seizures. Closed loop stimulation experiments based on these precursors and control efficacy maps on the epileptic animals show a maximum reduction of seizure frequency by 24.26\% in one animal and reduction of length of seizures by 51.77\% in another. Thus, through this study it was shown that the implementation of the methods can ameliorate seizures in an epileptic patient. It is expected that the new knowledge and experimental techniques will provide a guide for future research in an effort to ultimately eliminate seizures in epileptic patients.
ContributorsShafique, Md Ashfaque Bin (Author) / Tsakalis, Konstantinos (Thesis advisor) / Rodriguez, Armando (Committee member) / Muthuswamy, Jitendran (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2016
Description
In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.
ContributorsPadmanaban, Subash (Author) / Greger, Bradley (Thesis advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Crook, Sharon (Committee member) / Arizona State University (Publisher)
Created2017
Description
Prosthetic users abandon devices due to difficulties performing tasks without proper graded or interpretable feedback. The inability to adequately detect and correct error of the device leads to failure and frustration. In advanced prostheses, peripheral nerve stimulation can be used to deliver sensations, but standard schemes used in sensorized prosthetic systems induce percepts inconsistent with natural sensations, providing limited benefit. Recent uses of time varying stimulation strategies appear to produce more practical sensations, but without a clear path to pursue improvements. This dissertation examines the use of physiologically based stimulation strategies to elicit sensations that are more readily interpretable. A psychophysical experiment designed to investigate sensitivities to the discrimination of perturbation direction within precision grip suggests that perception is biomechanically referenced: increased sensitivities along the ulnar-radial axis align with potential anisotropic deformation of the finger pad, indicating somatosensation uses internal information rather than environmental. Contact-site and direction dependent deformation of the finger pad activates complimentary fast adapting and slow adapting mechanoreceptors, exhibiting parallel activity of the two associate temporal patterns: static and dynamic. The spectrum of temporal activity seen in somatosensory cortex can be explained by a combined representation of these distinct response dynamics, a phenomenon referred in this dissertation to “biphasic representation.” In a reach-to-precision-grasp task, neurons in somatosensory cortex were found to possess biphasic firing patterns in their responses to texture, orientation, and movement. Sensitivities seem to align with variable deformation and mechanoreceptor activity: movement and smooth texture responses align with potential fast adapting activation, non-movement and coarse texture responses align with potential increased slow adapting activation, and responses to orientation are conceptually consistent with coding of tangential load. Using evidence of biphasic representations’ association with perceptual priorities, gamma band phase locking is used to compare responses to peripheral nerve stimulation patterns and mechanical stimulation. Vibrotactile and punctate mechanical stimuli are used to represent the practical and impractical percepts commonly observed in peripheral nerve stimulation feedback. Standard patterns of constant parameters closely mimic impractical vibrotactile stimulation while biphasic patterns better mimic punctate stimulation and provide a platform to investigate intragrip dynamics representing contextual activation.
ContributorsTanner, Justin Cody (Author) / Helms Tillery, Stephen I (Thesis advisor) / Santos, Veronica J (Committee member) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Buneo, Christopher A (Committee member) / Arizona State University (Publisher)
Created2017
Description
The use of a non-invasive form of energy to modulate neural structures has gained wide spread attention because of its ability to remotely control neural excitation. This study investigates the ability of focused high frequency ultrasound to modulate the excitability the peripheral nerve of an amphibian. A 5MHz ultrasound transducer is used for the study with the pulse characteristics of 57msec long train burst and duty cycle of 8% followed by an interrogative electrical stimulus varying from 30μsecs to 2msecs in pulse duration. The nerve excitability is determined by the compound action potential (CAP) amplitude evoked by a constant electrical stimulus. We observe that ultrasound's immediate effect on axons is to reduce the electrically evoked CAP amplitude and thereby suppressive in effect. However, a subsequent time delayed increased excitability was observed as reflected in the CAP amplitude of the nerve several tens of milliseconds later. This subsequent change from ultrasound induced nerve inhibition to increased excitability as a function of delay from ultrasound pulse application is unexpected and not predicted by typical nerve ion channel kinetic models. The recruitment curve of the sciatic nerve modified by ultrasound suggests the possibility of a fiber specific response where the ultrasound inhibits the faster fibers more than the slower ones. Also, changes in the shape of the CAP waveform when the nerve is under the inhibitive effect of ultrasound was observed. It is postulated that these effects can be a result of activation of stretch activation channels, mechanical sensitivity of the nerve to acoustic radiation pressure and modulation of ion channels by ultrasound.
The neuromodulatory capabilities of ultrasound in tandem with electrical stimulation has a significant potential for development of neural interfaces to peripheral nerve.
The neuromodulatory capabilities of ultrasound in tandem with electrical stimulation has a significant potential for development of neural interfaces to peripheral nerve.
ContributorsChirania, Sanchit (Author) / Towe, Bruce (Thesis advisor) / Abbas, James (Committee member) / Muthuswamy, Jitendran (Committee member) / Arizona State University (Publisher)
Created2016
Description
Over the past fifty years, the development of sensors for biological applications has increased dramatically. This rapid growth can be attributed in part to the reduction in feature size, which the electronics industry has pioneered over the same period. The decrease in feature size has led to the production of microscale sensors that are used for sensing applications, ranging from whole-body monitoring down to molecular sensing. Unfortunately, sensors are often developed without regard to how they will be integrated into biological systems. The complexities of integration are underappreciated. Integration involves more than simply making electrical connections. Interfacing microscale sensors with biological environments requires numerous considerations with respect to the creation of compatible packaging, the management of biological reagents, and the act of combining technologies with different dimensions and material properties. Recent advances in microfluidics, especially the proliferation of soft lithography manufacturing methods, have established the groundwork for creating systems that may solve many of the problems inherent to sensor-fluidic interaction. The adaptation of microelectronics manufacturing methods, such as Complementary Metal-Oxide-Semiconductor (CMOS) and Microelectromechanical Systems (MEMS) processes, allows the creation of a complete biological sensing system with integrated sensors and readout circuits. Combining these technologies is an obstacle to forming complete sensor systems. This dissertation presents new approaches for the design, fabrication, and integration of microscale sensors and microelectronics with microfluidics. The work addresses specific challenges, such as combining commercial manufacturing processes into biological systems and developing microscale sensors in these processes. This work is exemplified through a feedback-controlled microfluidic pH system to demonstrate the integration capabilities of microscale sensors for autonomous microenvironment control.
ContributorsWelch, David (Author) / Blain Christen, Jennifer (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Frakes, David (Committee member) / LaBelle, Jeffrey (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2012
Description
The use of microelectrode arrays (MEA) to electroporate cells is now a reliable way of transfecting RNA interfering substances with high viability and efficiency. However, as the 50-200 micron electrodes are coated with many cells, there are differences in both viability and efficiency between the outside and inside of the electrode. This is due to the field created by the electrode, which has higher intensities toward the outside and lower intensities toward the middle. In order to get the electric field to spread in a more even manner, an "Anodisc" inorganic membrane seeded with cells was placed on the MEA to act as a buffer to the electric fields. One hundred percent transfection efficiency on live cells was found on one sample, though there were problems encountered along the experimental process that introduced error into the results, some of which included the inability for cells to grow to high levels of confluency on the Anodisc as well as the inverted imaging technique used on the opaque disc.
ContributorsDonnelly, Kyle Robert (Author) / Muthuswamy, Jitendran (Thesis director) / Haynes, Karmella (Committee member) / LaBelle, Jeffrey (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2013-05
Description
The emerging field of neuroprosthetics is focused on the development of new therapeutic interventions that will be able to restore some lost neural function by selective electrical stimulation or by harnessing activity recorded from populations of neurons. As more and more patients benefit from these approaches, the interest in neural interfaces has grown significantly and a new generation of penetrating microelectrode arrays are providing unprecedented access to the neurons of the central nervous system (CNS). These microelectrodes have active tip dimensions that are similar in size to neurons and because they penetrate the nervous system, they provide selective access to these cells (within a few microns). However, the very long-term viability of chronically implanted microelectrodes and the capability of recording the same spiking activity over long time periods still remain to be established and confirmed in human studies. Here we review the main responses to acute implantation of microelectrode arrays, and emphasize that it will become essential to control the neural tissue damage induced by these intracortical microelectrodes in order to achieve the high clinical potentials accompanying this technology.
ContributorsFernandez, Eduardo (Author) / Greger, Bradley (Author) / House, Paul A. (Author) / Aranda, Ignacio (Author) / Botella, Carlos (Author) / Albisua, Julio (Author) / Soto-Sanchez, Cristina (Author) / Alfaro, Arantxa (Author) / Normann, Richard A. (Author) / Ira A. Fulton Schools of Engineering (Contributor)
Created2014-07-21
Description
Intrauterine devices, or IUDs, are long-lasting forms of birth control that have effectiveness comparable to sterilization, while they can be removed at any time. However, the insertion process can be very painful, especially for individuals who have never given vaginal birth. The most common form of pain management for this procedure is having the patient take an ibuprofen an hour or so before the procedure, but this only helps with cramping afterwards, not the acute pain caused by insertion. Pain, and anxiety and fear regarding potential pain, serve as a barrier between users and this highly effective form of birth control. This report uses COMSOL to model lidocaine diffusion from 4% topical hydrogel into the cervix (the main site of acute pain during IUD insertion) over 180 minutes. The cervix was modeled axisymmetrically, using average experimental values for cervix size. Concentration at four specific probe points were measured over time and compared at different concentrations. A sensitivity analysis was performed by adjusting the diffusion coefficient of the epithelial layer. This model was developed to serve as a predictor for future drug applications across the cervix, to determine in advance whether a novel formulation of drug would be effective to significantly reduce pain. This model may be refined further with experimental values for the constants, and with further testing of different lidocaine concentrations.
ContributorsRuby, Sarah (Author) / Arquiza, J.M.R. Apollo (Thesis director) / Greger, Bradley (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2022-05
ContributorsRuby, Sarah (Author) / Arquiza, J.M.R. Apollo (Thesis director) / Greger, Bradley (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2022-05
ContributorsRuby, Sarah (Author) / Arquiza, J.M.R. Apollo (Thesis director) / Greger, Bradley (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2022-05