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http://hdl.handle.net/10603/434916
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DC Field | Value | Language |
---|---|---|
dc.coverage.spatial | Investigations of asthma and ecg morphology segment analysis using gmr sensor and neural network | |
dc.date.accessioned | 2023-01-02T09:12:59Z | - |
dc.date.available | 2023-01-02T09:12:59Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/434916 | - |
dc.description.abstract | Recent 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.extent | xii,118p. | |
dc.language | English | |
dc.relation | p.102-117 | |
dc.rights | university | |
dc.title | Investigations of asthma and ecg morphology segment analysis using gmr sensor and neural network | |
dc.title.alternative | ||
dc.creator.researcher | Nithya Selvakumari S | |
dc.subject.keyword | Engineering and Technology | |
dc.subject.keyword | Computer Science | |
dc.subject.keyword | Computer Science Information Systems | |
dc.subject.keyword | Chronic inflammation | |
dc.subject.keyword | arrhythmia disease | |
dc.subject.keyword | neural network | |
dc.description.note | ||
dc.contributor.guide | Gowri Shankar A | |
dc.publisher.place | Chennai | |
dc.publisher.university | Anna University | |
dc.publisher.institution | Faculty of Information and Communication Engineering | |
dc.date.registered | ||
dc.date.completed | 2021 | |
dc.date.awarded | 2021 | |
dc.format.dimensions | 21 cm | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 42.57 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 3.2 MB | Adobe PDF | View/Open | |
03_content.pdf | 18.69 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 21.05 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 1.02 MB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 119.19 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 1.52 MB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 386.64 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 2.1 MB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 26.63 kB | Adobe PDF | View/Open | |
11_annexures.pdf | 135.5 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 54.74 kB | Adobe PDF | View/Open |
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