Intraoperative neurophysiologic monitoring is an integral part of spinal surgeries and involves the recording of somatosensory evoked potentials (SSEP). However, clinical application of IONM still requires anywhere between 200 to 2000 trials to obtain an SSEP signal, which is excessive and introduces a significant delay during surgery to detect a possible neurological damage. The aim of this study is to develop a means to obtain the SSEP using a much less, twelve number of recordings. The preliminary step involved was to distinguish the SSEP with the ongoing brain activity. We first establish that the brain activity is indeed quasi-stationary whereas an SSEP is expected to be identical every time a trial is recorded. An algorithm was developed using Chebychev time windowing for preconditioning of SSEP trials to retain the morphological characteristics of somatosensory evoked potentials (SSEP). This preconditioning was followed by the application of a principal component analysis (PCA)-based algorithm utilizing quasi-stationarity of EEG on 12 preconditioned trials. A unique Walsh transform operation was then used to identify the position of the SSEP event. An alarm is raised when there is a 10% time in latency deviation and/or 50% peak-to-peak amplitude deviation, as per the clinical requirements. The algorithm shows consistency in the results in monitoring SSEP in up to 6-hour surgical procedures even under this significantly reduced number of trials. In this study, the analysis was performed on the data recorded in 29 patients undergoing surgery during which the posterior tibial nerve was stimulated and SSEP response was recorded from scalp. This method is shown empirically to be more clinically viable than present day approaches. In all 29 cases, the algorithm takes 4sec to extract an SSEP signal, as compared to conventional methods, which take several minutes. The monitoring process using the algorithm was successful and proved conclusive under the clinical constraints throughout the different surgical procedures with an accuracy of 91.5%. Higher accuracy and faster execution time, observed in the present study, in determining the SSEP signals provide a much improved and effective neurophysiological monitoring process.

This thesis discusses the research, experimental methods, and data gathered for the investigation of a novel method for the diagnosis of melanoma skin cancer. First, a background about human skin tissue is presented. Then, a detailed description of melanoma along with current diagnosis techniques and treatment options are presented. In the experimental methods, the electrical properties of several types of tissue were analyzed, the purpose of which was to discover if a tissue type can be distinguished by its electrical properties alone. This would allow for the diagnosis of melanoma to be done by examining the electrical properties of the suspected tumor and comparing the results to known values of healthy and cancerous skin. After analyzing the data, it was concluded that tissue types can be identified by their electrical properties and it may be possible to diagnose melanoma through this method. Finally, the possibility of using a similar technology and radiofrequency tissue ablation to treat melanoma is presented.

The Cardiac Rhythm Management Division of St. Jude Medical specializes in the development of implantable cardioverter defibrillators that improve the quality of life for patients diagnosed with a variety of cardiac arrhythmias, especially for patients prone to sudden cardiac death. With the goal to improve detection of cardiac arrhythmias, this study explored the value in time-frequency analysis of intracardiac electrogram in four steps. The first two steps characterized, in the frequency domain, the waveforms that construct the cardiac cycle. The third step developed a new algorithm that putatively provides the least computationally expensive way to identifying cardiac waveforms in the frequency domain. Lastly, this novel approach to analyzing intracardiac electrogram was compared to a threshold crossing algorithm that strictly operates in the time domain and that is currently utilized by St. Jude Medical. The new algorithm demonstrated an equally effective method in identifying the QRS complex on the ventricular channel. The next steps in pursing time-frequency analysis of intracardiac electrogram include implementing the new algorithm on a testing platform that emulates the latest implantable cardioverter defibrillator manufactured by St. Jude Medical and pursuing a similar algorithm that can be employed on the atrial channel.

Optogenetics is a set of technologies that enable optically triggered gain or loss of function in genetically specified populations of cells. Optogenetic methods have revolutionized experimental neuroscience by allowing precise excitation or inhibition of firing in specified neuronal populations embedded within complex, heterogeneous tissue. Although optogenetic tools have greatly improved our ability manipulate neural activity, they do not offer control of neural firing in the face of ongoing changes in network activity, plasticity, or sensory input. In this thesis, I develop a feedback control technology that automatically adjusts optical stimulation in real-time to precisely control network activity levels. I describe hardware and software tools, modes of optogenetic stimulation, and control algorithms required to achieve robust neural control over timescales ranging from seconds to days. I then demonstrate the scientific utility of these technologies in several experimental contexts. First, I investigate the role of connectivity in shaping the network encoding process using continuously-varying optical stimulation. I show that synaptic connectivity linearizes the neuronal response, verifying previous theoretical predictions. Next, I use long-term optogenetic feedback control to show that reductions in excitatory neurotransmission directly trigger homeostatic increases in synaptic strength. This result opposes a large body of literature on the subject and has significant implications for memory formation and maintenance. The technology presented in this thesis greatly enhances the precision with which optical stimulation can control neural activity, and allows causally related variables within neural circuits to be studied independently.

Increasing useof nanomaterials in consumer products and biomedical applications creates the possibilities of intentional/unintentional exposure to humans and the environment. Beyond the physiological limit, the nanomaterialexposure to humans can induce toxicity. It is difficult to define toxicity of nanoparticles on humans as it varies by nanomaterialcomposition, size, surface properties and the target organ/cell line. Traditional tests for nanomaterialtoxicity assessment are mostly based on bulk-colorimetric assays. In many studies, nanomaterials have found to interfere with assay-dye to produce false results and usually require several hours or days to collect results. Therefore, there is a clear need for alternative tools that can provide accurate, rapid, and sensitive measure of initial nanomaterialscreening. Recent advancement in single cell studies has suggested discovering cell properties not found earlier in traditional bulk assays. A complex phenomenon, like nanotoxicity, may become clearer when studied at the single cell level, including with small colonies of cells. Advances in lab-on-a-chip techniques have played a significant role in drug discoveries and biosensor applications, however, rarely explored for nanomaterialtoxicity assessment. We presented such cell-integrated chip-based approach that provided quantitative and rapid response of cellhealth, through electrochemical measurements. Moreover, the novel design of the device presented in this study was capable of capturing and analyzing the cells at a single cell and small cell-population level. We examined the change in exocytosis (i.e. neurotransmitterrelease) properties of a single PC12 cell, when exposed to CuOand TiO2 nanoparticles. We found both nanomaterials to interfere with the cell exocytosis function. We also studied the whole-cell response of a single-cell and a small cell-population simultaneously in real-time for the first time. The presented study can be a reference to the future research in the direction of nanotoxicity assessment to develop miniature, simple, and cost-effective tool for fast, quantitative measurements at high throughput level. The designed lab-on-a-chip device and measurement techniques utilized in the present work can be applied for the assessment of othernanoparticles' toxicity, as well.

Personal computers and portable electronics continue to rapidly advance and integrate into our lives as tools that facilitate efficient communication and interaction with the outside world. Now with a multitude of different devices available, personal computers are accessible to a wider audience than ever before. To continue to expand and reach new users, novel user interface technologies have been developed, such as touch input and gyroscopic motion, in which enhanced control fidelity can be achieved. For users with limited-to-no use of their hands, or for those who seek additional means to intuitively use and command a computer, novel sensory systems can be employed that interpret the natural electric signals produced by the human body as command inputs. One of these novel sensor systems is the myoelectric detection circuit, which can measure electromyographic (EMG) signals produced by contracting muscles through specialized electrodes, and convert the signals into a usable form through an analog circuit. With the goal of making a general-purpose myoelectric detection circuit platform for computer interface applications, several electrical circuit designs were iterated using OrCAD software, manufactured using PCB fabrication techniques, and tested with electrical measurement equipment and in a computer simulation. The analog circuit design culminated in a 1.35” x 0.8” manufactured analog myoelectric detection circuit unit that successfully converts a measured EMG input signal from surface skin electrodes to a clean and usable 0-5 V DC output that seamlessly interfaces with an Arduino Leonardo microcontroller for further signal processing and logic operations. Multiple input channels were combined with a microcontroller to create an EMG interface device that was used to interface with a PC, where simulated mouse cursor movement was controlled through the voluntary EMG signals provided by a user. Functional testing of the interface device was performed, which showed a long battery life of 44.6 hours, and effectiveness in using a PC to type with an on-screen keyboard.

Indiana University-Purdue University Indianapolis (IUPUI) / Understanding the mechanical properties of peripheral nerves is essential for chronically implanted device design. The work in this thesis aimed to understand the relationship between local deformation responses to global strain changes in peripheral nerves. A custom-built mechanical testing rig and sample holder enabled an improved cyclical uniaxial tensile testing environment on rabbit sciatic nerves (N=5). A speckle was placed on the surface of the nerve and recorded with a microscope camera to track local deformations. The development of a semi-automated digital image processing algorithm systematically measured local speckle dimension and nerve diameter changes. Combined with the measured force response, local and global strain values constructed a stress-strain relationship and corresponding elastic modulus. Preliminary exploration of models such as Fung and 2-Term Mooney-Rivlin confirmed the hyperelastic nature of the nerve. The results of strain analysis show that, on average, local strain levels were approximately five times smaller than globally measured strains; however, the relationship was dependent on global strain magnitude. Elastic modulus values corresponding to ~9% global strains were 2.070 ± 1.020 MPa globally and 10.15 ± 4 MPa locally. Elastic modulus values corresponding to ~6% global strains were 0.173 ± 0.091 MPa globally and 1.030 ± 0.532 MPa locally.

Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy. A nonlinear recurrence-based method is applied to scalp EEG recordings to obtain connectivity maps using phase synchronization attributes. The pairwise connectivity measure is obtained from time domain data without any conversion to the frequency domain. The phase coupling value, which indicates the broadband interdependence of input data, is utilized for the graph theory interpretation of local and global assessment of connectivity activities. The method is applied to the population of pediatric individuals to delineate the epileptic cases from normal controls. A probabilistic approach proved a significant difference between the two groups by successfully separating the individuals with an accuracy of 92.8%. The investigation of connectivity patterns of the interictal epileptic discharges (IED), which were originated from focal and generalized seizures, was resulted in a significant difference ( ) in connectivity matrices. It was observed that the functional connectivity maps of focal IED showed local activities while generalized cases showed global activated areas. The investigation of connectivity maps that resulted from temporal lobe epilepsy individuals has shown the temporal and frontal areas as the most affected regions. In general, functional connectivity measures are considered higher order attributes that helped the delineation of epileptic individuals in the classification process. The functional connectivity patterns of interictal activities can hence serve as indicators of the seizure type and also specify the irritated regions in focal epilepsy. These findings can indeed enhance the diagnosis process in context to the type of epilepsy and effects of relative location of the 3D source of seizure onset on other brain areas.

Peripheral neuropathies are a set of common diseases that affect the peripheral nervous system, causing damage to vital connections between various parts of the body and the brain and spinal cord. Different clinical conditions are known to selectively impact various size nerve fibers, which often makes it difficult to diagnose which peripheral neuropathy a patient might have. The nerve conduction velocity diagnostic test provides clinically useful information in the diagnosis of some peripheral neuropathies. This method is advantageous because it tends to be minimally invasive yet it provides valuable diagnostic information. However, this test does not determine characteristics of peripheral nerve fiber size distributions, and therefore does not show any detailed information regarding the nerve fibers within the nerve trunk. Being able to determine which nerve fibers are contributing to the evoked potential within a nerve trunk could provide additional information to clinicians for the diagnosis of specific pathologies of the peripheral nervous system, such as chronic inflammatory demyelinating polyneuropathy or early diabetic peripheral neuropathy. In this study, three rat sciatic nerves are sectioned and stained with hematoxylin and eosin in order to measure the nerve fiber diameters within the nerve trunk. Stained samples are viewed using brightfield microscopy and images are analyzed using ImageJ. Histograms were created to show the frequency of various nerve fiber diameters. The nerve fiber diameters measured during this research are consistent with the range of previously published diameter values and will be used to support continuing research for a novel method to characterize peripheral nerve fiber size distributions using group delay.

Alzheimer’s disease (AD) is a chronic and progressive, irreversible syndrome that deteriorates the cognitive functions. Official death certificates of 2013 reported 84,767 deaths from Alzheimer’s disease, making it the 6th leading cause of death in the United States. The rate of AD is estimated to double by 2050. The neurodegeneration of AD occurs decades before symptoms of dementia are evident. Therefore, having an efficient methodology for the early and proper diagnosis can lead to more effective treatments. Neuroimaging techniques such as magnetic resonance imaging (MRI) can detect changes in the brain of living subjects. Moreover, medical imaging techniques are the best diagnostic tools to determine brain atrophies; however, a significant limitation is the level of training, methodology, and experience of the diagnostician. Thus, Computer aided diagnosis (CAD) systems are part of a promising tool to help improve the diagnostic outcomes. No publications addressing the use of Feedforward Artificial Neural Networks (ANN), and MRI image attributes for the classification of AD were found. Consequently, the focus of this study is to investigate if the use of MRI images, specifically texture and frequency attributes along with a feedforward ANN model, can lead to the classification of individuals with AD. Moreover, this study compared the use of a single view versus a multi-view of MRI images and their performance. The frequency, texture, and MRI views in combination with the feedforward artificial neural network were tested to determine if they were comparable to the clinician’s performance. The clinician’s performances used were 78 percent accuracy, 87 percent sensitivity, 71 percent specificity, and 78 percent precision from a study with 1,073 individuals. The study found that the use of the Discrete Wavelet Transform (DWT) and Fourier Transform (FT) low frequency give comparable results to the clinicians; however, the FT outperformed the clinicians with an accuracy of 85 percent, precision of 87 percent, sensitivity of 90 percent and specificity of 75 percent. In the case of texture, a single texture feature, and the combination of two or more features gave results comparable to the clinicians. However, the Gray level co-occurrence matrix (GLCOM), which is the combination of texture features, was the highest performing texture method with 82 percent accuracy, 86 percent sensitivity, 76 percent specificity, and 86 percent precision. Combination CII (energy and entropy) outperformed all other combinations with 78 percent accuracy, 88 percent sensitivity, 72 percent specificity, and 78 percent precision. Additionally, a combination of views can increase performance for certain texture attributes; however, the axial view outperformed the sagittal and coronal views in the case of frequency attributes. In conclusion, this study found that both texture and frequency characteristics in combinations with a feedforward backpropagation neural network can perform at the level of the clinician and even higher depending on the attribute and the view or combination of views used.