Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/522113
Title: Vocal cord paralysis detection at earlier stage using deep learning algorithms
Researcher: Sakthivel S
Guide(s): Prabhu V
Keywords: High-Speed Video-Endoscopy
Phonatory Process
Vocal Cord Paralysis
University: Anna University
Completed Date: 2023
Abstract: The phonatory process means voicing. In the phonatory process, air newlinefrom lungs goes through glottis and gives a pressure drop over the larynx. When newlinedrop is large, vocal fold starts the oscillation. The high-speed video-endoscopy newline(HSV) is used for the study of phonatory processes, which is linked to speech. newlinePrecise identification of vocal fold boundaries at the time of vibration is used newlinefor diagnosis of speech disorder. HSV captures the image of vocal fold along newlinewith audio data, used for voice physiology and pathophysiology analysis. The newlinevocal fold is thin muscle at the back of human throat, moves to produce voice. newlineHSV is a unique laryngeal imaging technology that captures intracycle vocal newlinefold vibrations at a higher frame rate without the need of any auditory inputs. newlineHSV is effective for identification of the vibrational characteristics of the vocal newlinefolds with an increased temporal resolution during phonation. newlineClinically, vocal fold vibratory characteristics during speech is newlineretrieved through image and signal processing algorithms, extracts vocal fold newlinevibration from HSV data. Traditionally, vocal cord disorders such as laryngitis, newlinevocal nodules, vocal polyps, and vocal cord paralysis are diagnosed through newlineHSV data. Vocal cord paralysis is diagnosed through visual interpretation newlinethrough Endoscope/CT/MRI/laryngeal electromyography. However, vocal newlinecord paralysis never detect with vocal cord s muscle health. In this thesis, the newlinevocal cord paralysis i.e., vocal cords muscle health detection through vocal fold newlinecracking, stretching, tightening and shortening during vibrations. The deep newlinelearning-based diagnosis of vocal fold abnormalities are proposed in this thesis. newline
Pagination: xiv,144p.
URI: http://hdl.handle.net/10603/522113
Appears in Departments:Faculty of Information and Communication Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File246.07 kBAdobe PDFView/Open
02_prelim_pages.pdf2.23 MBAdobe PDFView/Open
03_contents.pdf76.81 kBAdobe PDFView/Open
04_abstracts.pdf85.81 kBAdobe PDFView/Open
05_chapter1.pdf172.55 kBAdobe PDFView/Open
06_chapter2.pdf261.78 kBAdobe PDFView/Open
07_chapter3.pdf1.43 MBAdobe PDFView/Open
08_chapter4.pdf496.68 kBAdobe PDFView/Open
09_chapter5.pdf1.98 MBAdobe PDFView/Open
10_annexures.pdf133.24 kBAdobe PDFView/Open
80_recommendation.pdf60.92 kBAdobe PDFView/Open
Show full item record


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: