Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/566999
Title: | Design and implementation of intelligent learning algorithms for classification of cardiovascular disease risk factors for obstructive sleep apnea patients |
Researcher: | Priyadharshini R |
Guide(s): | Paulraj M |
Keywords: | Computer Science Computer Science Information Systems Engineering and Technology |
University: | Anna University |
Completed Date: | 2024 |
Abstract: | newline Sleep is essential for the good physical and mental health of a person. Insufficient quality sleep may lead to serious health issues like mental health disorders, obesity, diabetes, immunodeficiency problems, and, most importantly, cardiovascular diseases (CVD). Due to lifestyle changes, mental stress, not eating on time, and other environmental conditions, a sleep disorder called sleep apnea may arise. A potentially serious sleep problem called sleep apnea is characterized by frequent pauses in breathing while a person is asleep. Obstructive sleep apnea (OSA) occurs when the muscles in the back of the throat relax and close off the airway, reducing the amount of oxygen that can reach the blood. As a result, breathing stops, which the brain detects, and the airway is then reopened. This cycle happens nearly 5 to 30 times per hour of sleep due to OSA. newlineThis research considered OSA and its consequences on CVD and associated risk factors. Loud snoring, morning headaches, insomnia, excessive daytime drowsiness (hypersomnia), abnormalities in brain processes, night sweats, and other symptoms are frequent in middle-aged and older persons who have OSA. OSA patients are affected by diseases like overweight, diabetes, heart attack, stroke, hypertension, etc., out of which they have double the risk of CV deaths if untreated. Multiple pathways are activated while OSA patients are sleeping as a result of brain activity, changes in intrathoracic pressure, reoxygenation, intermittent episodes of hypoxemia, like sympathetic activation, hypercoagulability, inflammation, oxidative stress, and metabolic dysregulation that leads to CVD outcomes like heart failure, stroke, systemic hypertension, arrhythmia, myocardial ischemia and infraction and even sudden death. OSA is a highly prevalent disease and frequently coexists with CVD. Around 22% of males and 17% of females in newlineiv newlinethe general population, respectively, have OSA. Research revealed, an important risk factor for the development of stroke is OSA, which is strongly |
Pagination: | xxi,140p. |
URI: | http://hdl.handle.net/10603/566999 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 199.31 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 1.37 MB | Adobe PDF | View/Open | |
03_content.pdf | 74.11 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 70.1 kB | Adobe PDF | View/Open | |
05_chapter1.pdf | 258.74 kB | Adobe PDF | View/Open | |
06_chapter2.pdf | 523.23 kB | Adobe PDF | View/Open | |
07_chapter3.pdf | 1.15 MB | Adobe PDF | View/Open | |
08_chapter4.pdf | 917.63 kB | Adobe PDF | View/Open | |
09_chapter5.pdf | 756.06 kB | Adobe PDF | View/Open | |
10_annexures.pdf | 195.34 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 127.88 kB | Adobe PDF | View/Open |
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