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Web-Based Software Tool for Electrocardiogram Annotation

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Contemporary Methods in Bioinformatics and Biomedicine and Their Applications (BioInfoMed 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 374))

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Abstract

The manual annotation of large multilead ECG databases is a challenge, especially in the context for providing meaningful visualization, easy tools for annotation and simultaneous access from multiple experts via extended Internet connectivity. The aim of this work is to present the development platform and capabilities of internet-based software tool for the purpose of user-friendly manual annotation and delineation of heartbeats in 12-lead ECG databases. The annotation software consists of: (i) server-based application, written in Python under DJango framework with SQLite database manager; and (ii) web-based front-end application, created in Node Package Manager environment under React JavaScript framework. The server-based part contains procedures for managing the ECG records (i.e. receiving and saving ECG signals), saving manually annotated data, and generation of average beats. The front-end application contains: (i) graphical user interface for visualization of 12-lead ECG signals and management of the user input/output commands; (ii) annotation module, which provides tools to mark and correct the positions and type of QRS complexes; (iii) average beat module, which provides an option to switch between leads, provides graphical markers for annotation of fiducial points and time-intervals in the averaged heartbeat waveform and sets the average beat type; and (iv) rhythm type module, which shows the rhythm type and provides a possibility to change it. The software is opened for further developments. The presented annotation tool could be potentially used for annotation of large ECG databases without limitations on the number of ECG leads and number of annotations per ECG recording. This is in line with the new trends for accumulation of large ECG databases from multiple sources, which are the thoughtful platform for development of machine learning and especially deep learning algorithms for ECG signal processing and arrhythmia classification.

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Acknowledgement

This work was supported by the Bulgarian National Science Fund under Grant Ref. No. КП-06-H42/3 “Computer aided diagnosis of cardiac arrhythmias based on machine learning and deep neural networks”.

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Authors and Affiliations

  1. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl. 105, 1113, Sofia, Bulgaria

    Todor Stoyanov

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  1. Todor Stoyanov
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Editors and Affiliations

  1. Faculty of Technical Sciences, University Prof. D-r Asen Zlatarov, Burgas, Bulgaria

    Sotir S. Sotirov

  2. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Tania Pencheva

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  4. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Krassimir T. Atanassov

  5. Intelligent Systems Laboratory, University Prof. D-r Asen Zlatarov, Burgas, Bulgaria

    Evdokia Sotirova

  6. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Galya Staneva

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Stoyanov, T. (2022). Web-Based Software Tool for Electrocardiogram Annotation. In: Sotirov, S.S., Pencheva, T., Kacprzyk, J., Atanassov, K.T., Sotirova, E., Staneva, G. (eds) Contemporary Methods in Bioinformatics and Biomedicine and Their Applications. BioInfoMed 2020. Lecture Notes in Networks and Systems, vol 374. Springer, Cham. https://doi.org/10.1007/978-3-030-96638-6_34

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  • DOI: https://doi.org/10.1007/978-3-030-96638-6_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96637-9

  • Online ISBN: 978-3-030-96638-6

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