Matching Items (38)
Description
This thesis discusses the experiences of starting and building a support group for teenagers who have a loved one with Parkinson's Disease. One of the goals of this thesis was to share our experiences with the staff at the Muhammad Ali Parkinson's Center, and the teenagers who will be taking over this group in the future. We discuss why we wanted to start the group, how it's foundation was built, and the challenges we faced and overcame. This is done by highlighting three significant group meetings, and various implications. Transportation, funding, and other issues are discussed.
ContributorsVilla, Roberto (Co-author) / Kisana, Haroon (Co-author) / Montesano, Mark (Thesis director) / Abbaszadegan, Hamed (Committee member) / Barrett, The Honors College (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Art (Contributor) / School of Life Sciences (Contributor) / School for the Science of Health Care Delivery (Contributor)
Created2015-05
Description
I conducted a research study with the intent to test an educational tool (a PowerPoint presentation) to evaluate its effectiveness at educating a group of nursing students (ASU college juniors) about Parkinson's disease (PD), Parkinson's medication and medication administration guidelines, and the necessity of getting patients with PD their medication on time. This research was based on the fact that a majority of patients with PD do not get their medication on time in a healthcare environment, and that structured interventions will increase awareness and knowledge of the specific needs of the PD patient. Upon analyzing the results of a survey given before and after the presentation, this educational tool was effective, but more research is needed to justify its implementation into education.
ContributorsHodges, Marie Bernadette (Author) / LuPone, Kathy (Thesis director) / Ash, Deborah (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
The following paper discusses the potential for Designed Ankyrin Repeat Proteins (DARPin) use as a diagnostic tool for neurodegenerative diseases in particular Alzheimer's disease (AD) and Parkinson's disease (PD). The two structures investigated for AD and PD were ADC7 and PDC1. Plasmid transformation was performed in order to grow the DARPin in E. coli for simple expression. Following growth and purification the proteins were validated using SDS-PAGE, Western Blot, BCA and indirect sandwich ELISA using transgenic mouse brain tissue. Targeted functionality of the DARPin structure was utilized during characterization methods to ensure the efficacy of the protein as a diagnostic for the respective disease targets. Both the ADC7 and PDC1 demonstrated improved binding with transgenic mice compared to wild type with a maximum 1.8 and 1.7 relative ratio, respectively. Additionally, both of the proteins demonstrated exclusive binding to their disease target and did not provide false positive results.
ContributorsTindell, John (Co-author) / Card, Emma (Co-author) / Sierks, Michael (Thesis director) / Nannenga, Brent (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
There has been tremendous technological advancement in the past two decades. Faster computers and improved sensing devices have broadened the research scope in computer vision. With these developments, the task of assessing the quality of human actions, is considered an important problem that needs to be tackled. Movement quality assessment finds wide range of application in motor control, health-care, rehabilitation and physical therapy. Home-based interactive physical therapy requires the ability to monitor, inform and assess the quality of everyday movements. Obtaining labeled data from trained therapists/experts is the main limitation, since it is both expensive and time consuming.
Motivated by recent studies in motor control and therapy, in this thesis an existing computational framework is used to assess balance impairment and disease severity in people suffering from Parkinson's disease. The framework uses high-dimensional shape descriptors of the reconstructed phase space, of the subjects' center of pressure (CoP) tracings while performing dynamical postural shifts. The performance of the framework is evaluated using a dataset collected from 43 healthy and 17 Parkinson's disease impaired subjects, and outperforms other methods, such as dynamical shift indices and use of chaotic invariants, in assessment of balance impairment.
In this thesis, an unsupervised method is also proposed that measures movement quality assessment of simple actions like sit-to-stand and dynamic posture shifts by modeling the deviation of a given movement from an ideal movement path in the configuration space, i.e. the quality of movement is directly related to similarity to the ideal trajectory, between the start and end pose. The S^1xS^1 configuration space was used to model the interaction of two joint angles in sit-to-stand actions, and the R^2 space was used to model the subject's CoP while performing dynamic posture shifts for application in movement quality estimation.
Motivated by recent studies in motor control and therapy, in this thesis an existing computational framework is used to assess balance impairment and disease severity in people suffering from Parkinson's disease. The framework uses high-dimensional shape descriptors of the reconstructed phase space, of the subjects' center of pressure (CoP) tracings while performing dynamical postural shifts. The performance of the framework is evaluated using a dataset collected from 43 healthy and 17 Parkinson's disease impaired subjects, and outperforms other methods, such as dynamical shift indices and use of chaotic invariants, in assessment of balance impairment.
In this thesis, an unsupervised method is also proposed that measures movement quality assessment of simple actions like sit-to-stand and dynamic posture shifts by modeling the deviation of a given movement from an ideal movement path in the configuration space, i.e. the quality of movement is directly related to similarity to the ideal trajectory, between the start and end pose. The S^1xS^1 configuration space was used to model the interaction of two joint angles in sit-to-stand actions, and the R^2 space was used to model the subject's CoP while performing dynamic posture shifts for application in movement quality estimation.
ContributorsSom, Anirudh (Author) / Turaga, Pavan (Thesis advisor) / Krishnamurthi, Narayanan (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2016
Description
Biological and biomedical measurements, when adequately analyzed and processed, can be used to impart quantitative diagnosis during primary health care consultation to improve patient adherence to recommended treatments. For example, analyzing neural recordings from neurostimulators implanted in patients with neurological disorders can be used by a physician to adjust detrimental stimulation parameters to improve treatment. As another example, biosequences, such as sequences from peptide microarrays obtained from a biological sample, can potentially provide pre-symptomatic diagnosis for infectious diseases when processed to associate antibodies to specific pathogens or infectious agents. This work proposes advanced statistical signal processing and machine learning methodologies to assess neurostimulation from neural recordings and to extract diagnostic information from biosequences.
For locating specific cognitive and behavioral information in different regions of the brain, neural recordings are processed using sequential Bayesian filtering methods to detect and estimate both the number of neural sources and their corresponding parameters. Time-frequency based feature selection algorithms are combined with adaptive machine learning approaches to suppress physiological and non-physiological artifacts present in neural recordings. Adaptive processing and unsupervised clustering methods applied to neural recordings are also used to suppress neurostimulation artifacts and classify between various behavior tasks to assess the level of neurostimulation in patients.
For pathogen detection and identification, random peptide sequences and their properties are first uniquely mapped to highly-localized signals and their corresponding parameters in the time-frequency plane. Time-frequency signal processing methods are then applied to estimate antigenic determinants or epitope candidates for detecting and identifying potential pathogens.
For locating specific cognitive and behavioral information in different regions of the brain, neural recordings are processed using sequential Bayesian filtering methods to detect and estimate both the number of neural sources and their corresponding parameters. Time-frequency based feature selection algorithms are combined with adaptive machine learning approaches to suppress physiological and non-physiological artifacts present in neural recordings. Adaptive processing and unsupervised clustering methods applied to neural recordings are also used to suppress neurostimulation artifacts and classify between various behavior tasks to assess the level of neurostimulation in patients.
For pathogen detection and identification, random peptide sequences and their properties are first uniquely mapped to highly-localized signals and their corresponding parameters in the time-frequency plane. Time-frequency signal processing methods are then applied to estimate antigenic determinants or epitope candidates for detecting and identifying potential pathogens.
ContributorsMaurer, Alexander Joseph (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2016
Description
Misfolding and aggregation of alpha-synuclein (a-syn) has been strongly correlated with the pathogenesis of Parkinson's disease (PD). Reagents such as single chain antibody fragments (scFv) that can interact with specific aggregate forms of a-syn can be very useful to study how different aggregate forms affect cells. Here we utilize two scFvs, D5 and 10H, that recognize two distinct oligomeric forms of a-syn to characterize the presence of different a-syn aggregates in animal models of PD.
ContributorsAlam, Now Bahar (Author) / Sierks, Michael (Thesis director) / Pauken, Christine (Committee member) / Williams, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Harrington Bioengineering Program (Contributor)
Created2013-05
Description
The artificial neural network is a form of machine learning that is highly effective at recognizing patterns in large, noise-filled datasets. Possessing these attributes uniquely qualifies the neural network as a mathematical basis for adaptability in personal biomedical devices. The purpose of this study was to determine the viability of neural networks in predicting Freezing of Gait (FoG), a symptom of Parkinson's disease in which the patient's legs are suddenly rendered unable to move. More specifically, a class of neural networks known as layered recurrent networks (LRNs) was applied to an open- source FoG experimental dataset donated to the Machine Learning Repository of the University of California at Irvine. The independent variables in this experiment \u2014 the subject being tested, neural network architecture, and sampling of the majority classes \u2014 were each varied and compared against the performance of the neural network in predicting future FoG events. It was determined that single-layered recurrent networks are a viable method of predicting FoG events given the volume of the training data available, though results varied significantly between different patients. For the three patients tested, shank acceleration data was used to train networks with peak precision/recall values of 41.88%/47.12%, 89.05%/29.60%, and 57.19%/27.39% respectively. These values were obtained for networks optimized using detection theory rather than optimized for desired values of precision and recall. Furthermore, due to the nature of the experiments performed in this study, these values are representative of the lower-bound performance of layered recurrent networks trained to detect gait freezing. As such, these values may be improved through a variety of measures.
ContributorsZia, Jonathan Sargon (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Adler, Charles (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Cannabis use is increasing both medically and recreationally. Over the last decade studies have investigated sex differences associated with Parkinson’s disease (PD) diagnosis and degenerative symptoms. Previous research has shown that cannabis use has had either a beneficial or deleterious effect on PD symptoms. This research will examine whether sex differences exist among the positive or negative effects of cannabis use in PD. In this paper, an analysis of sex-based differences between male and female cohorts categorized across 2,700 participants is completed under the Fox Insight data set. Each cohort will be compared to 14 nonmotor symptoms and 8 motor symptoms commonly associated with PD. In each cohort mean age, cannabis intake, cannabis dose, cannabis type, and PD diagnosis are analyzed within groups. Each symptom (motor and nonmotor) was analyzed between cohort responses to indicate if there was beneficial or worsening effect within cannabis. Results indicated that the designated female cohort reported both beneficial and worsening effects of cannabis use regarding both motor and nonmotor symptoms. The positive symptoms primarily consisted of individual motor functioning (e.g. dyskinesia, stiffness, back pain, etc.) while the worsening symptoms primarily consisted of nonmotor functioning (e.g. anxiety and apathy). Meanwhile, the male cohort only reported beneficial effects towards nonmotor symptoms (e.g. dystonia, muscle cramps, heart rate). These findings suggest the need for further examination of nigrostriatal pathways and hypothalamic integrity in PD, as it may provide more information into the effects of cannabis use based on sex differences.
ContributorsHooten, Madeline Loraine (Author) / Ofori, Edward (Thesis advisor) / Daniulaityte, Raminta (Committee member) / Peterson, Daniel (Committee member) / Arizona State University (Publisher)
Created2022
Description
Introduction: Lateral reactive stepping is correlated with impairment in people with Parkinson’s Disease (PwPD). Despite this, there is little known of lateral stepping strategies and performance of these strategies in reactive stepping. Objective: To characterize step strategy in people with PD, characterize changes in these stepping strategies through training, and identify performance improvements in the lateral step strategies. Methods: A total of 31 PwPd who are currently at risk for falls took part in an 18-week various background reactive stepping intervention. The stepping strategies were assessed on two baseline assessments (B1 and B2) immediately followed by a 6- session step training intervention occurring over two weeks. Step strategies were again assessed immediately after training (P1) and two months later (P2). Initial outcomes were characterized step strategies, changes in step strategies, and improvement in performance of step strategies. Results: Three step strategies were established and split into two groups (no cross and cross). Changes in step strategies did not occur significantly both before and after training. Improvement in performance of the step strategies occurred at a significant amount (p=0.05) via a decrease in use of support after training occurred for any step strategies utilized. Conclusion: Step strategies were characterized, and performance of strategies was improved upon following the 2-week training. Lateral step strategies are defined and repeated throughout reactive step training with potential for improvement.
ContributorsBosard, Cal (Author) / Peterson, Daniel (Thesis director) / Larson, David (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2023-05
Description
While REM Sleep Behavior disorder (RBD) has been linked with synucleinopathies, difficulties persist in clinically convenient diagnostic tools which can differentiate between underlying diseases. Identifying markers in the gait of RBD patients may ease the diagnostic process and indicate potential or status for developing more severe disorders. Individuals were referred to Movement Disorders Center of Arizona (MDCA) by a sleep specialist with a confirmed diagnosis of RBD, or those who were clinically indicated after questioning. All participants underwent a skin-biopsy test for α-synuclein, I-ioflupane dopamine transporter(DAT) scan, and had their gait velocity, cadence and stride dynamics assessed by an automated gait analysis system.
ContributorsWebster, Samuel (Author) / Peterson, Daniel (Thesis director) / Evidente, Virgilio (Committee member) / Barrett, The Honors College (Contributor) / Department of Physics (Contributor)
Created2023-05