Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal

Mohamad Mahmoud Al Rahhal, Naif Al Ajlan, Yakoub Bazi, Haikel Al Hichri, Timon Rabczuk

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    16 Citations (Scopus)

    Abstract

    In this paper, we propose an electrocardiogram (ECG) technique for the automatic detection of Premature Ventricular Contractions (PVC) based on multi-lead signals and on a deep learning architecture which is built using Stacked Denoising Autoencoders (SDAEs) networks. The proposed method consists of two main stages; feature learning and classification. In the first stage, we learn a new feature representation from data using SDAEs. Regarding the classification, we add a softmax regression layer on the top of the resulting hidden representation layer yielding a deep neural network (DNN). The proposed method fuses the results of several ECG leads (up to 12) in order to increase the detection accuracy. In the experiments, we use INCART database to test the proposed DNN multi-lead method. The obtained results are 98.6%, 91.4%, and 97.7% respectively for overall accuracy (OA), average sensitivity (Se), and average positive productivity (Pp).

    Original languageEnglish
    Title of host publication2018 IEEE International Conference on Electro/Information Technology, EIT 2018
    PublisherIEEE Computer Society
    Pages169-173
    Number of pages5
    ISBN (Electronic)9781538653982
    DOIs
    Publication statusPublished - 2018 Oct 18
    Event2018 IEEE International Conference on Electro/Information Technology, EIT 2018 - Rochester, United States
    Duration: 2018 May 32018 May 5

    Publication series

    NameIEEE International Conference on Electro Information Technology
    Volume2018-May
    ISSN (Print)2154-0357
    ISSN (Electronic)2154-0373

    Other

    Other2018 IEEE International Conference on Electro/Information Technology, EIT 2018
    Country/TerritoryUnited States
    CityRochester
    Period18/5/318/5/5

    Bibliographical note

    Funding Information:
    The authors extend their appreciation to the Distinguished Scientist Fellowship Program (DSFP) at King Saud University for funding this work.

    Publisher Copyright:
    © 2018 IEEE.

    ASJC Scopus subject areas

    • Computer Science Applications
    • Information Systems
    • Control and Systems Engineering
    • Electrical and Electronic Engineering

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