ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Papandreou-Suppappola, Antonia
Description
Parkinson's disease is a neurodegenerative condition diagnosed on patients with
clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated
number of patients living with Parkinson's disease around the world is seven
to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor
signs of Parkinson's disease patients. It is an advanced surgical technique that is used
when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation.
This work proposes a behavior recognition model for patients with Parkinson's
disease. In particular, an adaptive learning method is proposed to classify behavioral
tasks of Parkinson's disease patients using local field potential and electrocorticography
signals that are collected during DBS implantation surgeries. Unique patterns
exhibited between these signals in a matched feature space would lead to distinction
between motor and language behavioral tasks. Unique features are first extracted
from deep brain signals in the time-frequency space using the matching pursuit decomposition
algorithm. The Dirichlet process Gaussian mixture model uses the extracted
features to cluster the different behavioral signal patterns, without training or
any prior information. The performance of the method is then compared with other
machine learning methods and the advantages of each method is discussed under
different conditions.
clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated
number of patients living with Parkinson's disease around the world is seven
to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor
signs of Parkinson's disease patients. It is an advanced surgical technique that is used
when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation.
This work proposes a behavior recognition model for patients with Parkinson's
disease. In particular, an adaptive learning method is proposed to classify behavioral
tasks of Parkinson's disease patients using local field potential and electrocorticography
signals that are collected during DBS implantation surgeries. Unique patterns
exhibited between these signals in a matched feature space would lead to distinction
between motor and language behavioral tasks. Unique features are first extracted
from deep brain signals in the time-frequency space using the matching pursuit decomposition
algorithm. The Dirichlet process Gaussian mixture model uses the extracted
features to cluster the different behavioral signal patterns, without training or
any prior information. The performance of the method is then compared with other
machine learning methods and the advantages of each method is discussed under
different conditions.
ContributorsDutta, Arindam (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Holbert, Keith E. (Committee member) / Bliss, Daniel W. (Committee member) / Arizona State University (Publisher)
Created2015
Description
The work presented in this manuscript has the overarching theme of radiation. The two forms of radiation of interest are neutrons, i.e. nuclear, and electric fields. The ability to detect such forms of radiation have significant security implications that could also be extended to very practical industrial applications. The goal is therefore to detect, and even image, such radiation sources.
The method to do so revolved around the concept of building large-area sensor arrays. By covering a large area, we can increase the probability of detection and gather more data to build a more complete and clearer view of the environment. Large-area circuitry can be achieved cost-effectively by leveraging the thin-film transistor process of the display industry. With production of displays increasing with the explosion of mobile devices and continued growth in sales of flat panel monitors and television, the cost to build a unit continues to decrease.
Using a thin-film process also allows for flexible electronics, which could be taken advantage of in-house at the Flexible Electronics and Display Center. Flexible electronics implies new form factors and applications that would not otherwise be possible with their single crystal counterparts. To be able to effectively use thin-film technology, novel ways of overcoming the drawbacks of the thin-film process, namely the lower performance scale.
The two deliverable devices that underwent development are a preamplifier used in an active pixel sensor for neutron detection and a passive electric field imaging array. This thesis will cover the theory and process behind realizing these devices.
The method to do so revolved around the concept of building large-area sensor arrays. By covering a large area, we can increase the probability of detection and gather more data to build a more complete and clearer view of the environment. Large-area circuitry can be achieved cost-effectively by leveraging the thin-film transistor process of the display industry. With production of displays increasing with the explosion of mobile devices and continued growth in sales of flat panel monitors and television, the cost to build a unit continues to decrease.
Using a thin-film process also allows for flexible electronics, which could be taken advantage of in-house at the Flexible Electronics and Display Center. Flexible electronics implies new form factors and applications that would not otherwise be possible with their single crystal counterparts. To be able to effectively use thin-film technology, novel ways of overcoming the drawbacks of the thin-film process, namely the lower performance scale.
The two deliverable devices that underwent development are a preamplifier used in an active pixel sensor for neutron detection and a passive electric field imaging array. This thesis will cover the theory and process behind realizing these devices.
ContributorsChung, Hugh E (Author) / Allee, David R. (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2015