Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/474019
Title: Cardiac Ailments Classification of Electrocardiogram Signals Using Machine Learning and Deep Learning Algorithms
Researcher: LANKA ALEKHYA
Guide(s): P. RAJESH KUMAR
Keywords: Engineering
Engineering and Technology
Engineering Electrical and Electronic
University: Andhra University
Completed Date: 2022
Abstract: quotIn recent scenarios, the diagnosis of various cardiovascular newlinediseases is predominantly done by the proper investigation of newlineElectrocardiogram (ECG) signal. Typical heart diseases can be identified newlineby any modifications in the characteristic waves of ECG signal. In order newlineto identify and diagnose the Cardiac Ailments the characteristic waves newlineof ECG signal such as P, QRS complex and T waves are ought to be newlinedetected. With the advancements in technology and sciences, especially newlinethe use of wearable devices identifies ECG of particular person and with newlineproper software installed in it can automatically classify the ECG of newlinecardiac ailments which helps to diagnose these diseases earlier. newlineArrhythmia, Congestive Heart Failure and Atrial Fibrillation are some of newlinethe life-threatening cardiac ailments that may lead to sudden deaths in newlinemany cases. The work used these cardiovascular diseases along with newlinean unaltered ECG signal Normal Sinus Rhythm is taken from MIT-BIH newlinephysionet database to classify and detect the altered ECG signal.quot newline newline
Pagination: 199 pg
URI: http://hdl.handle.net/10603/474019
Appears in Departments:Department of Electronics & Communication Engineering

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01_title.pdfAttached File82.09 kBAdobe PDFView/Open
02_prelim pages.pdf491.75 kBAdobe PDFView/Open
03_content.pdf55.2 kBAdobe PDFView/Open
04_abstract.pdf108.27 kBAdobe PDFView/Open
05_chapter 1.pdf204.54 kBAdobe PDFView/Open
06_chapter 2.pdf76.54 kBAdobe PDFView/Open
07_chapter 3.pdf536.28 kBAdobe PDFView/Open
08_chapter 4.pdf446.29 kBAdobe PDFView/Open
09_chapter 5.pdf364.37 kBAdobe PDFView/Open
10_chapter 6.pdf544.44 kBAdobe PDFView/Open
11_chapter 7.pdf556.03 kBAdobe PDFView/Open
12_annexures.pdf164.37 kBAdobe PDFView/Open
80_recommendation.pdf135.21 kBAdobe PDFView/Open
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