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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    ECG disease detection & feature extraction by wavelet transformation

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    16101122.pdf (787.5Kb)
    Date
    2016-03
    Publisher
    BRAC University
    Author
    Imam, Saif
    Tabassum, Tasbiha
    ZarinIrtiza
    Metadata
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    URI
    http://hdl.handle.net/10361/5417
    Abstract
    ECG is the most common and basic test to run on patients to check any kind of anomalies in the heart. In the ECG result 10 to 20 minutes long continuous data of a patient’s heart is down sampled and printed as a 1D graph. We have develop a program which will take the continuous dataset from the ECG machine and analyses the data and extracts various features of the ECG wave. At first we decompose the data using Wavelet decomposition. Then the data is reconstructed in 4 levels which removes the noise from the signal. In the same time we detect major components of the ECG wave which is P wave, QRS complex and T wave. Then we calculate ST deviation, heart rate and extract other features such as location and amplitude of each waves in order to detect anomalies. Finally our output provides the heart status (healthy, if any disease found, if any major or minor risk) in a language that the patient can understand and also some detailed wave properties in medical term for the doctors.
    Keywords
    Computer science and engineering; Wavelet transformation
     
    Description
    This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (page 22-23).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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