• Login
    View Item 
    •   Home
    • Computer Science
    • Faculty Research and Publications
    • Articles
    • View Item
    •   Home
    • Computer Science
    • Faculty Research and Publications
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Effat University RepositoryCommunitiesPublication DateAuthorsTitlesSubjectsPublisherJournalTypeDepartmentThis CollectionPublication DateAuthorsTitlesSubjectsPublisherJournalTypeDepartmentProfilesView

    My Account

    Login

    Statistics

    Display statistics

    A novel Discrete Wavelet-Concatenated Mesh Tree and ternary chess pattern based ECG signal recognition method

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Author
    Tuncer, Turker
    Dogan, Sengul
    Plawiak, Pawel
    Subasi, Abdulhamit cc
    Subject
    ECG signal Recognition
    Machine learning
    Pattern Recognition
    Ternary Chess Pattern
    Wavelet Mesh Tree
    Date
    2022-02-01
    
    Metadata
    Show full item record
    Abstract
    Electrocardiogram (ECG) signals have been widely used to diagnose heart arrhythmias. In order to detect these arrhythmias using ECG signals, many machine learning methods have been presented. In this article, a novel Discrete Wavelet Concatenated Mesh Tree (DW-CMT) and ternary chess pattern (TCP) based ECG signal recognition method is presented. The proposed ECG signal recognition method consists of 4 main steps: pre-processing using DW-CMT, feature extraction using TCP, feature selection, and classification. In the pre-processing step, 15 sub-bands of an ECG signals are generated. By using TCP, features are extracted from the sub-bands of the ECG signal. The extracted features are concatenated in the feature concatenation phase. In order to select distinctive features, the neighborhood component analysis (NCA) based feature selection method is used and the 128 most distinctive features are selected. In order to demonstrate the strength of the extracted and selected features, conventional classifiers which are linear discriminant analysis (LDA), k-nearest neighbor (k-NN), support vector machine (SVM) are used. To test the success of the proposed method, the MIT-BIH dataset and St. Petersburg dataset were used. The 96.60% maximum classification accuracy is achieved for the MIT-BIH dataset using k-NN and 97.80% accuracy is achieved using SVM for St. Petersburg ECG dataset. The obtained results clearly prove the success of the proposed method.
    Department
    Computer Science
    Publisher
    Elsevier
    Journal title
    Biomedical Signal Processing and Control
    DOI
    https://doi.org/10.1016/j.bspc.2021.103331
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1016/j.bspc.2021.103331
    Scopus Count
    Collections
    Articles

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.