Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/352888
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dc.coverage.spatialAutomatic detection of sleep apnea Disease from ppg signal using Optimization echniques
dc.date.accessioned2021-12-27T06:50:56Z-
dc.date.available2021-12-27T06:50:56Z-
dc.identifier.urihttp://hdl.handle.net/10603/352888-
dc.description.abstractQuite recently, the rate of people suffering from respiratory diseases has greatly increased no matter the age Generally, while breathing, the respiratory sounds are formed in the large airways and the combination of air velocity and instability tends to produce vibrations in the airway pipes. Later, these vibrations pass through the lung tissue and thoracic wall which an be used to observe sound using a stethoscope, micro phone or any other sensors. Typically, human respiratory signal is categorised into three types such as Sleep Apnea (SA), Motion artifacts and standard respiration. Among the three types, Sleep Apnea (SA) occurs during sleep because of monotonous syndrome at upper airway tubes. These abnormalities result in frequent awakening and oxygen de-saturation. When affected by Sleep Apnea (SA), the person pauses his breath by holding the air up to a minute and then releases the air. The breath suppression happens throughout their sleep. Illnesses like high blood pressure, heart attack, obesity and diabetes may cause the person to suffer from Sleep Apnea (SA). In order to detect Sleep Apnea (SA), several methods have been implemented. Here, four novel methods are proposed to detect Sleep Apnea (SA) precisely. Initially, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for classification of respiratory signals using cepstral coefficients. It is a novel and efficient method which classify normal and abnormal breathing sounds. As known, computerized methods of respiratory sound analysis will provide a quantitative basis for abnormal breathing sound recognition. In this work, the breathing sound signals are analysed using Mel Frequency Cepstral Coefficients (MFCC) and classified by Adaptive Neuro-Fuzzy Inference System Classifier newline
dc.format.extentxxii, 165p.
dc.languageEnglish
dc.relationP142-164
dc.rightsuniversity
dc.titleAutomatic detection of sleep apnea Disease from ppg signal using Optimization echniques
dc.title.alternative
dc.creator.researcherKins burk sunil, N
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering
dc.subject.keywordEngineering Electrical and Electronic
dc.subject.keywordsleep apnea
dc.subject.keywordOptimization
dc.subject.keywordppg signal
dc.description.note
dc.contributor.guideGanesan, R and Sankaragomathi, B
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Electrical Engineering
dc.date.registered
dc.date.completed2020
dc.date.awarded2020
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Electrical Engineering

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01_title.pdfAttached File30.48 kBAdobe PDFView/Open
02_certificates.pdf575.41 kBAdobe PDFView/Open
03_vivaproceedings.pdf1.24 MBAdobe PDFView/Open
04_bonafidecertificate.pdf1.21 MBAdobe PDFView/Open
05_abstracts.pdf13.06 kBAdobe PDFView/Open
06_acknowledgements.pdf1.19 MBAdobe PDFView/Open
07_contents.pdf15.64 kBAdobe PDFView/Open
08_listoftables.pdf8.29 kBAdobe PDFView/Open
09_listoffigures.pdf10.86 kBAdobe PDFView/Open
10_listofabbreviations.pdf19.47 kBAdobe PDFView/Open
11_chapter1.pdf129.98 kBAdobe PDFView/Open
12_chapter2.pdf583.26 kBAdobe PDFView/Open
13_chapter3.pdf283.99 kBAdobe PDFView/Open
14_chapter4.pdf589.37 kBAdobe PDFView/Open
15_chapter5.pdf718.19 kBAdobe PDFView/Open
16_chapter6.pdf1.8 MBAdobe PDFView/Open
17_conclusion.pdf28.12 kBAdobe PDFView/Open
18_references.pdf4.5 MBAdobe PDFView/Open
19_listofpublications.pdf12.13 kBAdobe PDFView/Open
80_recommendation.pdf56.08 kBAdobe PDFView/Open


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