BIROn - Birkbeck Institutional Research Online

    Deep learning-based arrhythmia detection using RR-Interval framed electrocardiograms

    Kim, S.-K. and Yeun, C.Y. and Yoo, Paul and Lo, N.-W. and Damiani, E. (2022) Deep learning-based arrhythmia detection using RR-Interval framed electrocardiograms. In: UNSPECIFIED (ed.) The 8th International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems. Springer. (In Press)

    [img] Text
    RRFrameECG_R02b_AMG.pdf - Author's Accepted Manuscript
    Restricted to Repository staff only

    Download (974kB) | Request a copy

    Abstract

    Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders. We devel-oped a deep learning model for the detection of arrhythmia in which time-sliced ECG data representing the distance between successive R-peaks are used as the input for a convolutional neural network (CNN). The main ob-jective is developing the compact deep learning based detect system which minimally uses the dataset but delivers the confident accuracy rate of the Arrhythmia detection. This compact system can be implemented in weara-ble devices or real-time monitoring equipment because the feature extrac-tion step is not required for complex ECG waveforms, only the R-peak data is needed. The 10 hidden layers of the CNN detect arrhythmias using a nov-el RR-interval framing (RRIF) approach. Two testing processes were imple-mented, the first during the training and validation of the CNN algorithm and the second using different datasets for testing under realistic condi-tions. The results of both tests indicated that the Compact Arrhythmia De-tection System (CADS) matched the performance of conventional systems for the detection of arrhythmia in two consecutive test runs.

    Metadata

    Item Type: Book Section
    Additional Information: Series ISSN: 2367-3370 - https://www.springer.com/series/15179
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Paul Yoo
    Date Deposited: 15 Nov 2022 11:13
    Last Modified: 09 Aug 2023 12:54
    URI: https://eprints.bbk.ac.uk/id/eprint/49799

    Statistics

    Activity Overview
    6 month trend
    1Download
    6 month trend
    79Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item