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A Multi-layer Deep Learning Model for ECG-Based Arrhythmia Classification

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 646))

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Abstract

Electrocardiogram (ECG) is consistently used as a measure to medical monitoring technology that records the cardiac activity to identify the cardiovascular diseases (CVDs) which are the foremost reason of death globally these days. Regrettably, seeking medical experts to analyse big amount ECG signal consumes an inordinate number of medical resources. As a result, machine learning based approaches for identifying ECG characteristics have gradually gained popularity. However, these traditional approaches have some disadvantages, such as the need for manual feature recognition, complex representations, and a prolonged training period. In this article, we present a method for identifying the five classes of heart-beat categories in the MIT-BIH Arrhythmia dataset using A Multi-layer Deep Learning Model (MLDLM) in compliance with the AAMI EC57 standard. The proposed MLDLM technique was tested using MIT-BIH Arrhythmia Dataset which has 1,09,446 ECG heart-beats and sample frequency 125 Hz. This initial dataset contains the classes N, S, V, F, and Q. The PTB Diagnostic ECG Dataset is the second dataset, which is divided into two categories. The results show that the suggested approach is capable of making predictions with average accuracies of 98.75 on MIT-BIH Arrhythmia dataset and 98.87 on MI classification.

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Acknowledgement

This research has been financially supported by The Analytical Center for the Government of the Russian Federation (Agreement No. \(70-2021-00143\) dd. 01.11.2021, IGK 000000D730321P5Q0002). Authors acknowledge the technical support and review feedback from AILSIA symposium held in conjunction with the 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022).

Author information

Authors and Affiliations

  1. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, UK, India

    Khushboo Jain

  2. Ramanujan College, Delhi University, Delhi, India

    Arun Agarwal & Ashima Jain

  3. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, 98071, USA

    Ajith Abraham

  4. Center for Artificial Intelligence Innopolis University, Innopolis, Russia

    Ajith Abraham

Authors
  1. Khushboo Jain
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  2. Arun Agarwal
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  3. Ashima Jain
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  4. Ajith Abraham
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Corresponding author

Correspondence to Khushboo Jain .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Jain, K., Agarwal, A., Jain, A., Abraham, A. (2023). A Multi-layer Deep Learning Model for ECG-Based Arrhythmia Classification. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-27440-4_5

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