Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/434916
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dc.coverage.spatialInvestigations of asthma and ecg morphology segment analysis using gmr sensor and neural network
dc.date.accessioned2023-01-02T09:12:59Z-
dc.date.available2023-01-02T09:12:59Z-
dc.identifier.urihttp://hdl.handle.net/10603/434916-
dc.description.abstractRecent researches have been focused on major respiratory problems in medical newlinefield. One such problem addressed in the current research work is asthma. Asthma is newlinecharacterised as a persistent airway inflammatory illness. Chronic inflammation is newlineinterlinked with airway hyper responsiveness, which directs to unceasing indications such newlineas gasping, dyspnea (breathlessness), muscle aches and sneezing (increased airwaynarrowing newlinestimulation such as allergic reactions and workout). Episodes of signs are newlineusually associated with widespread, but complex, restriction of ventilation inside the lungs newlinethat is potentially transient either naturally or with adequate care for asthma. newlineThe main objective of the present research work is to propose an efficient algorithm newlineknown as Rational-Dilation Wavelet Transforms (RADWT) algorithm to diagnose the newlineasthma disease. In this proposed work, GMR sensor has been utilized to monitor the newlineparameters of asthma along with algorithm .The methodology or the process obtains the newlinevalues of filter bank using RADWT algorithm with mathematical evaluations by hardware newlineimplementation. The results provided by the proposed algorithm have been increased up to newline85 percent in prediction of asthma disease as compared with existing researches. newline
dc.format.extentxii,118p.
dc.languageEnglish
dc.relationp.102-117
dc.rightsuniversity
dc.titleInvestigations of asthma and ecg morphology segment analysis using gmr sensor and neural network
dc.title.alternative
dc.creator.researcherNithya Selvakumari S
dc.subject.keywordEngineering and Technology
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordChronic inflammation
dc.subject.keywordarrhythmia disease
dc.subject.keywordneural network
dc.description.note
dc.contributor.guideGowri Shankar A
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2021
dc.date.awarded2021
dc.format.dimensions21 cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File42.57 kBAdobe PDFView/Open
02_prelim pages.pdf3.2 MBAdobe PDFView/Open
03_content.pdf18.69 kBAdobe PDFView/Open
04_abstract.pdf21.05 kBAdobe PDFView/Open
05_chapter 1.pdf1.02 MBAdobe PDFView/Open
06_chapter 2.pdf119.19 kBAdobe PDFView/Open
07_chapter 3.pdf1.52 MBAdobe PDFView/Open
08_chapter 4.pdf386.64 kBAdobe PDFView/Open
09_chapter 5.pdf2.1 MBAdobe PDFView/Open
10_chapter 6.pdf26.63 kBAdobe PDFView/Open
11_annexures.pdf135.5 kBAdobe PDFView/Open
80_recommendation.pdf54.74 kBAdobe PDFView/Open


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