Matching Items (20)

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Research and Design of an Epilepsy Clinic in Developing Countries Based on Sustainability and Ambient Environment

Description

Worldwide there are over 50 million people suffering from epilepsy, eighty percent (80%) of whom live in low to middle income countries. Of that eighty percent (80%) of people suffering

Worldwide there are over 50 million people suffering from epilepsy, eighty percent (80%) of whom live in low to middle income countries. Of that eighty percent (80%) of people suffering from this disease, seventy-five percent (75%) do not receive treatment. The current design and treatment methods of epilepsy have many limitations in these specific countries. These limitations include: lack of education about the disease leading to stigmas surrounding it, inability to afford treatment options, and the absence of healthcare practitioners who specialize in the treatment of neurological illnesses. Additionally, the healthcare system worldwide is a large contributor to climate change calling for a need to implement sustainable practices in both the treatment of patients and creation of healthcare centers. This thesis has been developed in order to theorize the design of a clinic that can be beneficial to epileptics in developing countries and to the environment. Through the methodology of case studies and research on existing strategies implemented in specific hospitals, we were able to focus on three main aspects that should be taken into consideration for an epilepsy clinic: the ambient environment, sustainability, and target demographic - developing countries. The idea ambient environment, it was found, plays a large role in the healing process through reduction of stress on patients. From there the most important features specific to epilepsy were able to be considered and synthesized for the best possible theoretical design of a clinic focused on the treatment and diagnosis of epilepsy in a developing country.

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Agent

Created

Date Created
  • 2019-05

Seizure Discharge Education for Pediatric Patients: An Online Platform

Description

There is a shortage of online resources for children who have epilepsy. Most of the current online resources are designed for populations with a higher health literacy. This creative project

There is a shortage of online resources for children who have epilepsy. Most of the current online resources are designed for populations with a higher health literacy. This creative project addresses this shortage by offering education for children with epilepsy that they are able to access and utilize online to understand their disease in greater depth. Comprehending discharge information after hospitalization can be difficult for children and families, which is why providing an accessible resource that also can be utilized at home increases understanding about the disorder and ability to manage the disorder. Basic information on epilepsy, safety tips for daily living, medication explanation, first aid information, and interactive resources are included on the website and are all geared toward children. A website developer, Sylvestri Customization™, assisted with creating the website utilizing template, search engine optimization and strategies for website sustainability. The website was created after completing a thorough review of current research literature and reviewing multiple, similar hospital educational websites while also consulting with healthcare professionals to ensure the information was evidence-based. While the website provides supplemental education via an online platform for children with epilepsy to explore, there is a need for future research to test the acceptability and efficacy of the website.

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Agent

Created

Date Created
  • 2016-12

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Evaluation of EpiFinder App: An Epilepsy Diagnostic Tool

Description

Epilepsy is a complex neurological disease that affects one in twenty-six people. Despite this prevalence, it is very difficult to diagnose. EpiFinder, Inc. has created an app to better diagnose

Epilepsy is a complex neurological disease that affects one in twenty-six people. Despite this prevalence, it is very difficult to diagnose. EpiFinder, Inc. has created an app to better diagnose epilepsy through the use of an epilepsy focused ontology and a heuristic algorithm. Throughout this project, efforts were made to improve the user interface and robustness of the EpiFinder app in order to ease usability and increase diagnostic accuracy. A general workflow of the app was created to aid new users with navigation of the app’s screens. Additionally, numerous diagnostic guidelines provided by the International League Against Epilepsy as well as de-identified case studies were annotated using the Knowtator plug-in in Protégé 3.3.1, where new terms not currently represented in the seizure and epilepsy syndrome ontology (ESSO) were identified for future integration into the ontology. This will help to increase the confidence level of the differential diagnosis reached. A basic evaluation of the user interface was done to provide feedback for the developers for future iterations of the app. Significant efforts were also made for better incorporation of the app into a physician’s typical workflow. For instance, an ontology of a basic review of systems of a medical history was built in Protégé 4.2 for later integration with the ESSO, which will help to increase efficiency and familiarity of the app for physician users. Finally, feedback regarding utility of the app was gathered from an epilepsy support group. These points will be taken into consideration for development of patient-based features in future versions of the EpiFinder app. It is the hope that these various improvements of the app will contribute to a more efficient, more accurate diagnosis of epilepsy patients, resulting in more appropriate treatments and an overall increased quality of life.

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Agent

Created

Date Created
  • 2016-12

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Dynamical EEG Analysis in the Diagnosis and Treatment of Epilepsy

Description

In this study, the entrainment of brain dynamics in epilepsy was investigated in a thorough, systematic way. In the first part of the study, diagnosis of epilepsy, elements from the

In this study, the entrainment of brain dynamics in epilepsy was investigated in a thorough, systematic way. In the first part of the study, diagnosis of epilepsy, elements from the theory of chaos were used to measure the brain dynamics over time from EEGs (electroencephalograms) recorded in humans with either epileptic or non-epileptic seizures. In the second part of the study, treatment of epilepsy, data from rats undergoing VNS (vagus nerve stimulation) treatment were analyzed in the same way. The results suggest that a) the differential diagnosis in humans with epileptic and non-epileptic seizures can be significantly improved by analysis of brain dynamics, and b) the Vagus Nerve Stimulation may be working by controlling the entrainment level of brain dynamics.

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Agent

Created

Date Created
  • 2013-05

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Gender Differences in Optimal Cut Scores on the Personality Assessment Inventory for Diagnosing Epileptic and Psychogenic Non-epileptic Seizures

Description

Abstract
Diagnosing psychogenic non-epileptic seizures (PNES) requires admission to an epilepsy monitoring unit, which is a lengthy and expensive process. Despite the cost of and time commitment to this

Abstract
Diagnosing psychogenic non-epileptic seizures (PNES) requires admission to an epilepsy monitoring unit, which is a lengthy and expensive process. Despite the cost of and time commitment to this inpatient evaluation, a definitive diagnosis at the end isn’t always guaranteed. Therefore, predictor variables such as demographic information and psychological testing scores can help improve the accuracy of diagnosing PNES or epilepsy at the end of a patient’s EMU admission. Locke et al. have demonstrated that the SOM scale and SOM-C subscale on the Personality Assessment Inventory (PAI) are the best indicators for predicting PNES diagnosis, with an optimal cut score of T≥70 on both of these scales. The aim of the current study was to determine whether evaluating male and female performance separately on these relevant PAI scales improves the accuracy of diagnosing PNES. The results support the hypothesis, such that male optimal cut scores on the SOM and SOM C scales are T=80 and T=75, respectively, and female optimal cut scores on the SOM and SOM C scales are T=71 and T=72, respectively. Utilizing the results of this study can help clinicians diagnose patients with PNES or epilepsy at the end of EMU evaluation with more certainty.

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Agent

Created

Date Created
  • 2015-05

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Development of Automatic Control Software for a Patient Monitoring Camera System

Description

Electroencephalogram (EEG) used simultaneously with video monitoring can record detailed patient physiology during a seizure to aid diagnosis. However, current patient monitoring systems typically require a patient to stay in

Electroencephalogram (EEG) used simultaneously with video monitoring can record detailed patient physiology during a seizure to aid diagnosis. However, current patient monitoring systems typically require a patient to stay in view of a fixed camera limiting their freedom of movement. The goal of this project is to design an automatic patient monitoring system with software to track patient movement in order to increase a patient's mobility. This report discusses the impact of an automatic patient monitoring system and the design steps used to create and test a functional prototype.

Contributors

Agent

Created

Date Created
  • 2014-05

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Economic Outcomes and Feasibility of Successful Early Stage Epilepsy Screening

Description

Epilepsy is a medical disorder that is difficult to diagnose given the current available protocols and procedures. This project looks at the potential economic impact of a new digital screening

Epilepsy is a medical disorder that is difficult to diagnose given the current available protocols and procedures. This project looks at the potential economic impact of a new digital screening technology developed by EpiFinder, Inc. Utilizing a thorough literature review, this thesis generated a concept based clinical utility function comprised of the essential functional aspects of a seizure assessment. EpiFinder’s digital screening tool was then inserted into the clinical utility objective function based on its capabilities. In order to evaluate the potential impact of this model, hospital discharge data from Phoenix Children’s Hospital was assessed for costs relating to procedures performed. This was estimated using average charges for Medicare Part B in 2018. Patients were categorized based on the severity of their seizure presentation into groups of well-controlled, intermediate-controlled, and uncontrolled seizures. Due to a limited data set for well-controlled seizure patients, only intermediate-controlled and uncontrolled groups were compared through the clinical utility model. There was an average cost savings of $227.92 for the uncontrolled group with digital screening and $131.94 for the intermediate-controlled group. The findings of this feasibility study for the economic impact of digital screening suggest further work to refine the model and improve the quality of cost estimates. Clinical utility of seizure assessment procedures and protocols should be quantified through claims data and field specialists opinions to broaden the scope of digital screening’s impact across the continuum of care for epilepsy patients. Comparisons of clinical utility and the creation of an objective function to assess new medical technologies is becoming a common practice for analyzing new medical technologies entering the market. This is the first such attempt in regards to adding a digital screening tool into the current seizure assessment protocols.

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Agent

Created

Date Created
  • 2019-05

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Utilizing Structural MRI Data to Improve Epilepsy Surgery Planning

Description

In epilepsy, malformations that cause seizures often require surgery. The purpose of this research is to join forces with the Multi-Center Epilepsy Lesion Detection (MELD) project at University College London

In epilepsy, malformations that cause seizures often require surgery. The purpose of this research is to join forces with the Multi-Center Epilepsy Lesion Detection (MELD) project at University College London (UCL) in order to improve the process of detecting lesions in patients with drug-resistant epilepsy. This, in turn, will improve surgical outcomes via more structured surgical planning. It is a global effort, with more than 20 sites across 5 continents. The targeted populations for this study include patients whose epilepsy stems from Focal Cortical Dysplasia. Focal Cortical Dysplasia is an abnormality of cortical development, and causes most of the drug-resistant epilepsy. Currently, the creators of MELD have developed a set of protocols which wrap various
commands designed to streamline post-processing of MRI images. Using this partnership, the Applied Neuroscience and Technology Lab at PCH has been able to complete production of a post-processing pipeline which integrates locally sourced smoothing techniques to help identify lesions in patients with evidence of Focal Cortical Dysplasia. The end result is a system in which a patient with epilepsy may experience more successful post-surgical results due to the
combination of a lesion detection mechanism and the radiologist using their trained eye in the presurgical stages. As one of the main points of this work is the global aspect of it, Barrett thesis funding was dedicated for a trip to London in order to network with other MELD project collaborators. This was a successful trip for the project as a whole in addition to this particular thesis. The ability to troubleshoot problems with one another in a room full of subject matter
experts allowed for a high level of discussion and learning. Future work includes implementing machine learning approaches which consider all morphometry parameters simultaneously.

Contributors

Agent

Created

Date Created
  • 2019-05

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Development of a Novel Zebrafish Model for Dynamin-1 Epileptic Encephalopathy

Description

Epileptic encephalopathies (EE) are genetic or environmentally-caused conditions that cause “catastrophic” damage or degradation to the sensory, cognitive, and behavioral centers of the brain. Whole-exome sequencing identified de novo heterozygous

Epileptic encephalopathies (EE) are genetic or environmentally-caused conditions that cause “catastrophic” damage or degradation to the sensory, cognitive, and behavioral centers of the brain. Whole-exome sequencing identified de novo heterozygous missense mutations within the DNM1 gene of five pediatric patients with epileptic encephalopathies. DNM1 encodes for the dynamin-1 protein which is involved in endocytosis and synaptic recycling, and it is a member of dynamin GTPase. The zebrafish, an alternative model system for drug discovery, was utilized to develop a novel model for dynamin-1 epileptic encephalopathy through a small molecule inhibitor. The model system mimicked human epilepsy caused by DNM1 mutations and identified potential biochemical pathways involved in the production of this phenotype. The use of microinjections of mutated DNM1 verified phenotypes and was utilized to determine safe and effective antiepileptic drugs (AEDs) for treatment of this specific EE. This zebrafish dynamin-1 epileptic encephalopathy model has potential uses for drug discovery and investigation of this rare childhood disorder.

Contributors

Created

Date Created
  • 2019-05

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Directional information flow and applications

Description

In the late 1960s, Granger published a seminal study on causality in time series, using linear interdependencies and information transfer. Recent developments in the field of information theory have introduced

In the late 1960s, Granger published a seminal study on causality in time series, using linear interdependencies and information transfer. Recent developments in the field of information theory have introduced new methods to investigate the transfer of information in dynamical systems. Using concepts from Chaos and Markov theory, much of these methods have evolved to capture non-linear relations and information flow between coupled dynamical systems with applications to fields like biomedical signal processing. This thesis deals with the application of information theory to non-linear multivariate time series and develops measures of information flow to identify significant drivers and response (driven) components in networks of coupled sub-systems with variable coupling in strength and direction (uni- or bi-directional) for each connection. Transfer Entropy (TE) is used to quantify pairwise directional information. Four TE-based measures of information flow are proposed, namely TE Outflow (TEO), TE Inflow (TEI), TE Net flow (TEN), and Average TE flow (ATE). First, the reliability of the information flow measures on models, with and without noise, is evaluated. The driver and response sub-systems in these models are identified. Second, these measures are applied to electroencephalographic (EEG) data from two patients with focal epilepsy. The analysis showed dominant directions of information flow between brain sites and identified the epileptogenic focus as the system component typically with the highest value for the proposed measures (for example, ATE). Statistical tests between pre-seizure (preictal) and post-seizure (postictal) information flow also showed a breakage of the driving of the brain by the focus after seizure onset. The above findings shed light on the function of the epileptogenic focus and understanding of ictogenesis. It is expected that they will contribute to the diagnosis of epilepsy, for example by accurate identification of the epileptogenic focus from interictal periods, as well as the development of better seizure detection, prediction and control methods, for example by isolating pathologic areas of excessive information flow through electrical stimulation.

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Agent

Created

Date Created
  • 2011