An investigation of velopharyngeal closure with linear regression / by Anish Sana.
| Author/creator | Sana, Anish author. |
| Other author | Tabrizi, M. H. N., degree supervisor. |
| Other author | East Carolina University. Department of Computer Science. |
| Format | Theses and dissertations |
| Publication | [Greenville, N.C.] : [East Carolina University], 2016. |
| Description | 80 pages : illustrations, charts |
| Supplemental Content | Access via ScholarShip |
| Subjects |
| Summary | Cleft lip and palate is a common birth defect in the United States. Children diagnosed with this abnormality face difficulties during feeding, hearing and speech. Surgical methods exist to repair the cleft lip and palate but often require subsequent surgeries as children are unable to gain full speech capabilities as they tend to develop hypernasal speech due to velopharyngeal inadequacy. Investigating velopharyngeal closure can help speech pathologists, surgeons and related professionals understand the effect of velopharyngeal anatomy on velopharyngeal function. In order to accomplish this, several studies have used two dimensional and three dimensional modeling to visualize the velum. Very few attempts have been made to track the velum and plot its movement against time. Image segmentation has been used widely for various purposes. However, its proficiency in tracking the velum is questionable at the moment. Two image segmentation methods, EdgeTrak and the Hidden Markov Model, are reviewed in this report. EdgeTrak, a software developed at the Video/Image Modeling and Synthesis Laboratory, has been proven to track the surface of a human tongue during speech production. An attempt was made to similarly track the velum during speech production using EdgeTrak but the results were disappointing. Also, synchronized audio mapping using the Hidden Markov Model was only partially successful. This report describes the challenges image segmentation faces with regards to tracking the velum. To tackle the ineffectiveness of image segmentation for studying the velopharyngeal system, this study introduces a novel method to investigate the effects of muscles in the velopharyngeal system on closure force using a machine learning algorithm called multiple linear regression. Velopharyngeal muscle data from ten adults and ten children was acquired to train the algorithm. A mechanical representation of the velopharyngeal system was used to calculate the closure force and angle values for the training set which was validated using linearity where closure force increases linearly with increase in muscle activation levels. The algorithm was programmed in MATLAB which used the training set data to predict closure force values and their direction for any set of anatomical parameters. It was observed that the cross sectional area of the velum had a major influence on closure force challenging previous claims that the levator veli palatini was responsible for this. It was also found that the levator veli palatini muscle had a greater influence on closure force direction than other anatomical parameters suggesting that it acts a supporting structure. |
| General note | Presented to the faculty of the Department of Computer Science and to faculty of the Department of Communication Sciences & Disorders. |
| General note | Advisor: Nasseh Tabrizi. |
| General note | Title from PDF t.p. (viewed February 29, 2016). |
| Dissertation note | M.S. East Carolina University 2016. |
| Bibliography note | Includes bibliographical references. |
| Technical details | System requirements: Adobe Reader. |
| Technical details | Mode of access: World Wide Web. |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |