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Electrocardiogram (ECG) Circuit Design and Using the Random Forest to ECG Arrhythmia Classification

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Advances in Engineering Research and Application (ICERA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 602))

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Abstract

The paper presents a proposed hardware device for ECG signals measurement with an integrated remote system for signal automatic analysis to help doctors monitor health, diagnose cardiovascular diseases. This measuring device can transmit the ECG signals online to the server, The server is equipped with a software for analyzing ECG signals and classifying them to detect the arrhythmias using a RF (Random Forest) network. The Hermite basis functions were used to generate the feature vectors together with 2 time-based features: the last R-R period and the average of the last 10 R-R periods. The proposed solution was tested with ECG signals taken from databases MIT-BIH (Massachusetts Institute of Technology, Boston’s Beth Israel Hospital). Seven types of ECG beats were classified with an error of 2.28%. The proposed Random Forest algorithm works very fast, which makes it suitable for the requirement of quick classification of some cardiovascular diseases.

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References

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Author information

Authors and Affiliations

  1. Hanoi University of Industry, Hanoi, Vietnam

    Van Nam Pham

  2. Hanoi University of Science and Technology, Hanoi, Vietnam

    Hoai Linh Tran

Authors
  1. Van Nam Pham
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  2. Hoai Linh Tran
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Corresponding author

Correspondence to Van Nam Pham .

Editor information

Editors and Affiliations

  1. Faculty of Electronic Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam

    Duy Cuong Nguyen

  2. Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam

    Ngoc Pi Vu

  3. Vietnam Association for Science Editing, Hanoi University of Science and Technology, Hanoi, Vietnam

    Banh Tien Long

  4. Institute for Automation and Systems Engineering, Ilmenau University of Technology (IUT), Ilmenau, Germany

    Horst Puta

  5. Department of Computer Science and Automation, Ilmenau University of Technology (IUT), Ilmenau, Germany

    Kai-Uwe Sattler

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Pham, V.N., Tran, H.L. (2023). Electrocardiogram (ECG) Circuit Design and Using the Random Forest to ECG Arrhythmia Classification. In: Nguyen, D.C., Vu, N.P., Long, B.T., Puta, H., Sattler, KU. (eds) Advances in Engineering Research and Application. ICERA 2022. Lecture Notes in Networks and Systems, vol 602. Springer, Cham. https://doi.org/10.1007/978-3-031-22200-9_54

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  • DOI: https://doi.org/10.1007/978-3-031-22200-9_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22199-6

  • Online ISBN: 978-3-031-22200-9

  • eBook Packages: EngineeringEngineering (R0)

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