Matching Items (11)
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
This study focused on the connection between the EnvZ/OmpR two-component regulatory system and the iron homeostasis system in Escherichia coli, specifically how a mutant form of EnvZ11/OmpR is able to reduce the expression of fepA::lacZ, a reporter gene fusion in E. coli. FepA is one of several outer membrane siderophore receptors that allow extracellular siderophores bound to iron to enter the cells to power various biological processes. Previous studies have shown that in E. coli cells that expressed a mutant allele of envZ, called envZ11, which led to altered expression of various iron genes including down regulation of fepA::lacZ. The wild type EnvZ/OmpR system is not considered to regulate iron genes, but because these envz11 strains had downregulated fepA::lacZ, this study was undertaken to understand the connection and mechanisms of this downregulation. A large number of Lac+ revertants were obtained from the B32-2483 strain (envz11 and fepA::lacZ) and 7 Lac+ revertants that had reversion mutations not directly correcting the envZ11 allele were further characterized. With P1 phage transduction genetic mapping that involved moving a kanamycin resistance marker linked to fepA::lacZ, two Lac+ revertants were found to have their reversion mutations in the fepA promoter region, while the other five revertants had their mutations mapping outside the fepA region. These two revertants underwent DNA sequencing and found to carry two different single base pair mutations in two different locations of the fepA promoter region. Each one is in the Fur repressor binding region, but one also may have affected the Shine-Dalgarno region involved in translation initiation. All 7 reveratants underwent beta-galactosidase assays to measure fepA::lacZ expression. The two revertants that had mutations in the fepA promoter region had significantly increased fepA activity, with the revertant with the Shine-Dalgarno mutation having the most elevated fepA expression. The other 5 revertants that did not map in the fepA region had fepA expression elevated to the same level as that found in the wild type EnvZ/OmpR background. The data suggest that the negative effect of envZ11 can be overcome by multiple mechanisms, including directly correcting the envZ11 allele or changing the fepA promoter region.
ContributorsKalinkin, Victor Arkady (Co-author) / Misra, Rajeev (Co-author, Thesis director) / Mason, Hugh (Committee member) / Foy, Joseph (Committee member) / Biomedical Informatics Program (Contributor) / School of Life Sciences (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Translating research has been a goal of the Department of Health and Human Services since 1999. Through two years of iteration and interview with our community members, we have collected insights into the barriers to accomplishing this goal. Liberating Science is a think-tank of researchers and scientists who seek to create a more transparent process to accelerate innovation starting with behavioral health research.
ContributorsRaghani, Pooja Sioux (Author) / Hekler, Eric (Thesis director) / Buman, Matthew (Committee member) / Pruthi, Virgilia Kaur (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / Biomedical Informatics Program (Contributor)
Created2014-05
Description
The proliferation of interconnected and networked medical devices has resulted in the development of innovative Medical Cyber-Physical Systems (MCPS). MCPS are life-critical, distributed systems that are utilized to monitor and control healthcare organizations in order to provide a more coordinated, cohesive care-continuum focused on the whole patient resulting in better outcomes, and a happier, healthier patient. Medical Cyber Physical (MCPS) systems are life-critical, networked systems used to monitor and control healthcare and medical devices in order to provide more coordinated and cohesive care for the patient. Cyber-securing MCPS is difficult due to their complex and interconnected nature, and this project sets about analyzing current security requirements for MCPS using an ontology and exploration techniques, and developing a risk assessment and monitoring framework to better secure such systems.
ContributorsLamp, Josephine Ann (Author) / Ahn, Gail-Joon (Thesis director) / Rubio-Medrano, Carlos (Committee member) / School of Film, Dance and Theatre (Contributor) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Title: A Mobile Health Application for Tracking Patients' Health Record Abstract Background: Mobile Health (mHealth) has recently been adopted and used in rural communities in developing countries to improve the quality of healthcare in those areas. Some organizations use mHealth application to track pregnancy and provide routine checkups for pregnant women. Other organizations use mHelath application to provide treatment and counseling services to HIV/AIDs patients, and others are using it to provide treatment and other health care services to the general populations in rural communities. One organization that is using mobile health to bring primary care for the first time in some of the rural communities of Liberia is Last Mile Health. Since 2015, the organization has trained community health assistants (CHAs) to use a mobile health platform called Data Collection Tools (DCTs). The CHAs use the DCT to collect health data, diagnose and treat patients, provide counseling and educational services to their communities, and for referring patients for further care. While it is true that the DCT has many great features, it currently has many limitations such as data storage, data processing, and many others. Objectives: The goals of this study was to 1. Explore some of the mobile health initiatives in developing countries and outline some of the important features of those initiatives. 2. Design a mobile health application (a new version of the Last Mile Health's DCT) that incorporates some of those features that were outlined in objective 1. Method: A comprehensive literature search in PubMed and Arizona State University (ASU) Library databases was conducted to retrieve publications between 2014 and 2017 that contained phrases like "mHealth design", "mHealth implementation" or "mHealth validation". For a publication to refer to mHealth, the publication had to contain the term "mHealth," or contains both the term "health" and one of the following terms: mobile phone, cellular phone, mobile device, text message device, mobile technology, mobile telemedicine, mobile monitoring device, interactive voice response device, or disease management device. Results: The search yielded a total of 1407 publications. Of those, 11 publications met the inclusion criteria and were therefore included in the study. All of the features described in the selected articles were important to the Last Mile Health, but due to issues such as internet accessibility and cellular coverage, only five of those features were selected to be incorporated in the new version of the Last Mile's mobile health system. Using a software called Configure.it, the new version of the Last Mile's mobile health system was built. This new system incorporated features such as user logs, QR code, reminder, simple API, and other features that were identified in the study. The new system also helps to address problems such as data storage and processing that are currently faced by the Last Mile Health organization.
ContributorsKarway, George K. (Author) / Scotch, Matthew (Thesis director) / Kaufman, David (Committee member) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Methane (CH4) is very important in the environment as it is a greenhouse gas and important for the degradation of organic matter. During the last 200 years the atmospheric concentration of CH4 has tripled. Methanogens are methane-producing microbes from the Archaea domain that complete the final step in breaking down organic matter to generate methane through a process called methanogenesis. They contribute to about 74% of the CH4 present on the Earth's atmosphere, producing 1 billion tons of methane annually. The purpose of this work is to generate a preliminary metabolic reconstruction model of two methanogens: Methanoregula boonei 6A8 and Methanosphaerula palustris E1-9c. M. boonei and M. palustris are part of the Methanomicrobiales order and perform hydrogenotrophic methanogenesis, which means that they reduce CO2 to CH4 by using H2 as their major electron donor. Metabolic models are frameworks for understanding a cell as a system and they provide the means to assess the changes in gene regulation in response in various environmental and physiological constraints. The Pathway-Tools software v16 was used to generate these draft models. The models were manually curated using literature searches, the KEGG database and homology methods with the Methanosarcina acetivorans strain, the closest methanogen strain with a nearly complete metabolic reconstruction. These preliminary models attempt to complete the pathways required for amino acid biosynthesis, methanogenesis, and major cofactors related to methanogenesis. The M. boonei reconstruction currently includes 99 pathways and has 82% of its reactions completed, while the M. palustris reconstruction includes 102 pathways and has 89% of its reactions completed.
ContributorsMahendra, Divya (Author) / Cadillo-Quiroz, Hinsby (Thesis director) / Wang, Xuan (Committee member) / Stout, Valerie (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / School of Life Sciences (Contributor) / Biomedical Informatics Program (Contributor)
Created2014-05
Description
Phylogenetic analyses that were conducted in the past didn't have the ability or functionality to inform and implement useful public health decisions while using clustering. Models can be constructed to conduct any further analyses for the result of meaningful data to be used in the future of public health informatics. A phylogenetic tree is considered one of the best ways for researchers to visualize and analyze the evolutionary history of a certain virus. The focus of this study was to research HIV phylodynamic and phylogenetic methods. This involved identifying the fast growing HIV transmission clusters and rates for certain risk groups in the US. In order to achieve these results an HIV database was required to retrieve real-time data for implementation, alignment software for multiple sequence alignment, Bayesian analysis software for the development and manipulation of models, and graphical tools for visualizing the output from the models created. This study began by conducting a literature review on HIV phylogeographies and phylodynamics. Sequence data was then obtained from a sequence database to be run in a multiple alignment software. The sequence that was obtained was unaligned which is why the alignment was required. Once the alignment was performed, the same file was loaded into a Bayesian analysis software for model creation of a phylogenetic tree. When the model was created, the tree was edited in a tree visualization software for the user to easily interpret. From this study the output of the tree resulted the way it did, due to a distant homology or the mixing of certain parameters. For a further continuation of this study, it would be interesting to use the same aligned sequence and use different model parameter selections for the initial creation of the model to see how the output changes. This is because one small change for the model parameter could greatly affect the output of the phylogenetic tree.
ContributorsNandan, Meghana (Author) / Scotch, Matthew (Thesis director) / Liu, Li (Committee member) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Integrating behavioral and physical health is the key to value-based care. Little is known about data sharing preferences and consent practices for individuals with behavioral health conditions. This study focuses on identifying behavioral health provider perceptions about patient data sharing practices, preferences and perceived impact on care resulting from enhanced patient control of record types during consent for data sharing.
ContributorsHiestand, Megan (Author) / Grando, Adela (Thesis director) / Murcko, Anita (Committee member) / Sharp, Richard (Committee member) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Prescription opioid abuse has become a serious national problem. To respond to the opioid epidemic, states have created prescription drug monitoring programs (PDMPs) to monitor and reduce opioids use. We conducted a systematic literature review to better understand metrics used to quantify the effect that PDMPs have had on reducing opioid abuse, and solutions and challenges related to the integration of PDMPs with EHRs. Lessons learned can help guide federal and state-based efforts to better respond to the current opioid crisis.
ContributorsPonnapalli, Aditya Somayajulu (Author) / Murcko, Anita (Thesis director) / Grando, Adela (Committee member) / Wertheim, Pete (Committee member) / Biomedical Informatics Program (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Concept maps are teaching tools used to encourage students to utilize active learning strategies and to take responsibility for their own learning. The purpose of this two-semester study is to evaluate the use of concept maps in a junior-level Biomaterials classroom. The maps are assessed based on students' attitude, achievement, and persistence. No significant correlation was determined between concept map score and achievement (correlation coefficient = 0.1739 in the first semester, 0.2208 in the first set of the second semester, and 0.0829 in the second set of the second semester), though further studies should be completed to support the effects of concept mapping. Statistically significant increases in student attitude regarding concept mapping cost, interest, and utility between the two semesters were found (p = 0.013, p = 0.105, p = 0.002, p = 0.083 overall). Persistence was moderately high throughout the entire study (98% in the first semester and 100% in the second semester).
ContributorsHolm, Mikayle Ashlyn (Author) / Ankeny, Casey (Thesis director) / Graham, Kaely (Committee member) / Harrington Bioengineering Program (Contributor) / Biomedical Informatics Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05