Matching Items (29)

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Partnership Dance and Its Positive Effects on Patients With Parkinson's Disease

Description

While growing up, I was placed into dance classes, dance started out as a hobby, but as I grew up it became a way for me to escape from the struggles life itself brings. While I was taking a dance

While growing up, I was placed into dance classes, dance started out as a hobby, but as I grew up it became a way for me to escape from the struggles life itself brings. While I was taking a dance culture class at Arizona State University, I stumbled across research that revealed that dance does not just help people like myself, but it also has the ability to help those with more difficult life-altering situations like Parkinson’s Disease. With having about 970 million adults aged 65 years old and up (United Nations), around 10-million of these individuals have Parkinson’s Disease (PD) (Parkinson’s New Today). With these large numbers, Parkinson’s is the second leading neurodegenerative disease worldwide (Parkinson’s News Today) behind Alzheimer’s. Parkinson’s is a motor system disorder that affects the production of dopamine in one’s brain (Harvard). With the current treatment of PD being medication as well as surgical therapy based on the severity of each patient (Parkinson’s Foundation), there is one form of treatment that has been tested but not certified, partnership dancing. The way that partnership dance benefits those with Parkinson’s Disease is by using many areas of the brain to facilitate dopamine production. The four main areas used are the motor cortex, the somatosensory, the basal ganglia, and lastly the cerebellum (Harvard). With the vast amount of existing research, as well as the information gained through secondary research, I feel as though there needs to be a study to open the development of partnership dance as a therapy modality for those with many of the forms of degenerative mental diseases. Although unable to put on this research, I have outlined what this study could look like to be continued in the hopes of having partnership dance become a certified form of therapy for those with Parkinson’s Disease.

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Date Created
2020-05

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EMG-Interfaced Device for the Detection and Alleviation of Freezing of Gait in Individuals with Parkinson's Disease

Description

Parkinson's disease is a neurodegenerative disorder in the central nervous system that affects a host of daily activities and involves a variety of symptoms; these include tremors, slurred speech, and rigid muscles. It is the second most common movement disorder

Parkinson's disease is a neurodegenerative disorder in the central nervous system that affects a host of daily activities and involves a variety of symptoms; these include tremors, slurred speech, and rigid muscles. It is the second most common movement disorder globally. In Stage 3 of Parkinson's, afflicted individuals begin to develop an abnormal gait pattern known as freezing of gait (FoG), which is characterized by decreased step length, shuffling, and eventually complete loss of movement; they are unable to move, and often results in a fall. Surface electromyography (sEMG) is a diagnostic tool to measure electrical activity in the muscles to assess overall muscle function. Most conventional EMG systems, however, are bulky, tethered to a single location, expensive, and primarily used in a lab or clinical setting. This project explores an affordable, open-source, and portable platform called Open Brain-Computer Interface (OpenBCI). The purpose of the proposed device is to detect gait patterns by leveraging the surface electromyography (EMG) signals from the OpenBCI and to help a patient overcome an episode using haptic feedback mechanisms. Previously designed devices with similar intended purposes utilize accelerometry as a method of detection as well as audio and visual feedback mechanisms in their design.

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2016-05

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Medication on Time: The Difference Between Shaking, And Shaking It Off

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,

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.

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2016-12

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SHAKE IT OFF: ESTABLISHING A TEEN SUPPORT GROUP AT THE MUHAMMAD ALI PARKINSON'S CENTER

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

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.

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Date Created
2015-05

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Utilizing Neural Networks to Predict Freezing of Gait in Parkinson's Patients

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

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.

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Date Created
2016-05

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The effects of deep brain stimulation amplitude on motor performance in Parkinson's disease

Description

The efficacy of deep brain stimulation (DBS) in Parkinson's disease (PD) has been convincingly demonstrated in studies that compare motor performance with and without stimulation, but characterization of performance at intermediate stimulation amplitudes has been limited. This study investigated the

The efficacy of deep brain stimulation (DBS) in Parkinson's disease (PD) has been convincingly demonstrated in studies that compare motor performance with and without stimulation, but characterization of performance at intermediate stimulation amplitudes has been limited. This study investigated the effects of changing DBS amplitude in order to assess dose-response characteristics, inter-subject variability, consistency of effect across outcome measures, and day-to-day variability. Eight subjects with PD and bilateral DBS systems were evaluated at their clinically determined stimulation (CDS) and at three reduced amplitude conditions: approximately 70%, 30%, and 0% of the CDS (MOD, LOW, and OFF, respectively). Overall symptom severity and performance on a battery of motor tasks - gait, postural control, single-joint flexion-extension, postural tremor, and tapping - were assessed at each condition using the motor section of the Unified Parkinson's Disease Rating Scale (UPDRS-III) and quantitative measures. Data were analyzed to determine whether subjects demonstrated a threshold response (one decrement in stimulation resulted in ≥ 70% of the maximum change) or a graded response to reduced stimulation. Day-to-day variability was assessed using the CDS data from the three testing sessions. Although the cohort as a whole demonstrated a graded response on several measures, there was high variability across subjects, with subsets exhibiting graded, threshold, or minimal responses. Some subjects experienced greater variability in their CDS performance across the three days than the change induced by reducing stimulation. For several tasks, a subset of subjects exhibited improved performance at one or more of the reduced conditions. Reducing stimulation did not affect all subjects equally, nor did it uniformly affect each subject's performance across tasks. These results indicate that altered recruitment of neural structures can differentially affect motor capabilities and demonstrate the need for clinical consideration of the effects on multiple symptoms across several days when selecting DBS parameters.

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Date Created
2013

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Topological Descriptors for Parkinson's Disease Classification and Regression Analysis

Description

At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to

At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.

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2020-05

Electromagnetic Field Strength Analysis with Deep Brain Stimulation in Parkinson's Patients

Description

Deep Brain Stimulation (DBS) is a stimulating therapy currently used to treat the motor disabilities that occur as a result of Parkinson’s disease (PD). Previous literature has proven the DBS to be an effective treatment in the effects of PD

Deep Brain Stimulation (DBS) is a stimulating therapy currently used to treat the motor disabilities that occur as a result of Parkinson’s disease (PD). Previous literature has proven the DBS to be an effective treatment in the effects of PD but the mechanism to validating this phenomenon is poorly understood. In this study, an evaluation of the DBS mechanism was analyzed in patients who received both contralateral and ipsilateral stimulation by the DBS electrode in relation to the recording microelectrode. I hypothesize that the data recorded from the neural tissue of the Parkinson’s patients will exhibit increased electromagnetic field (EMF) fall-off as spatial distance increases among the DBS lead and the microelectrode within the subthalamic nucleus (STN) as a result of the interaction between the EMF exuded by DBS and the neural tissue. Results depicted that EMF fall-off values increased with distance, observable upon comparing ipsilateral and contralateral patient data. The resulting analysis supported this phenomenon evidenced by the production of greater peak voltage amplitudes in ipsilateral patient stimulation with respect to time when compared to contralateral patient stimulation. The understanding of EMF strength and the associated trends among this data are vital to the progression and continued development of the DBS field relative to future research.

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Date Created
2020-12

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Detecting Oligomeric Forms of Alpha-Synuclein in Cell and Mouse Tissue

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

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.

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Date Created
2013-05

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A study on home based Parkinson's disease monitoring and evaluation: design, development, and evaluation

Description

Parkinson's disease, the most prevalent movement disorder of the central nervous system, is a chronic condition that affects more than 1000,000 U.S. residents and about 3% of the population over the age of 65. The characteristic symptoms include tremors, bradykinesia,

Parkinson's disease, the most prevalent movement disorder of the central nervous system, is a chronic condition that affects more than 1000,000 U.S. residents and about 3% of the population over the age of 65. The characteristic symptoms include tremors, bradykinesia, rigidity and impaired postural stability. Current therapy based on augmentation or replacement of dopamine is designed to improve patients' motor performance but often leads to levodopa-induced complications, such as dyskinesia and motor fluctuation. With the disease progress, clinicians must closely monitor patients' progress in order to identify any complications or decline in motor function as soon as possible in PD management. Unfortunately, current clinical assessment for Parkinson's is subjective and mostly influenced by brief observations during patient visits. Thus improvement or decline in patients' motor function in between visits is extremely difficult to assess. This may hamper clinicians while making informed decisions about the course of therapy for Parkinson's patients and could negatively impact clinical care. In this study we explored new approaches for PD assessment that aim to provide home-based PD assessment and monitoring. By extending the disease assessment to home, the healthcare burden on patients and their family can be reduced, and the disease progress can be more closely monitored by physicians. To achieve these aims, two novel approaches have been designed, developed and validated. The first approach is a questionnaire based self-evaluation metric, which estimate the PD severity through using self-evaluation score on pre-designed questions. Based on the results of the first approach, a smart phone based approach was invented. The approach takes advantage of the mobile computing technology and clinical decision support approach to evaluate the motor performance of patient daily activity and provide the longitudinal disease assessment and monitoring. Both approaches have been validated on recruited PD patients at the movement disorder program of Barrow Neurological Clinic (BNC) at St Joseph's Hospital and Medical Center. The results of validation tests showed favorable accuracy on detecting and assessing critical symptoms of PD, and shed light on promising future of implementing mobile platform based PD evaluation and monitoring tools to facilitate PD management.

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2013