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Classification of ECG Heartbeat Using Deep Convolutional Neural Network

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Machine Learning for Intelligent Decision Science

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

The report of World Health Organization (WHO) specifies that the diagnosis and treatment of cardiovascular diseases are challenging tasks. To study the electrical conductivity of the heart, Electrocardiogram (ECG) which is an inexpensive diagnostic tool, is used. Classification is the most well-known topic for arrhythmia detection related to cardiovascular disease. Many algorithms have been evolved for the classification of heartbeat arrhythmia in the previous few decades using the CAD system. In this paper, we have developed a new deep CNN (11-layer) model for automatically classifying ECG heartbeats into five different groups according to the ANSI-AAMI standard (1998) without using feature extraction and selection techniques. The experiment is performed on publicly available Physionet MIT-BIH database and evaluated results are then compared with the existing works mentioned in the literature. To handle the problem of minority classes as well as the class imbalance problem, the database has been oversampled artificially using SMOTE technique. The augmented ECG database was employed for training the model while the testing was performed on the unseen dataset. On evaluation of the results from the experiment, we found that the proposed CNN model performed better in comparison to the experiments mentioned in other papers in terms of accuracy, sensitivity, and specificity. abstract environment.

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Authors and Affiliations

  1. Department of Information Technology, NIT Raipur, Chhattisgarh, India

    Saroj Kumar Pandey & Rekh Ram Janghel

  2. CSVTU Bhilai, Durg, India

    Kshitiz Varma

Authors
  1. Saroj Kumar Pandey
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  2. Rekh Ram Janghel
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  3. Kshitiz Varma
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Corresponding author

Correspondence to Saroj Kumar Pandey .

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Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India

    Jitendra Kumar Rout

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India

    Minakhi Rout

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India

    Himansu Das

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Pandey, S.K., Janghel, R.R., Varma, K. (2020). Classification of ECG Heartbeat Using Deep Convolutional Neural Network. In: Rout, J., Rout, M., Das, H. (eds) Machine Learning for Intelligent Decision Science. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3689-2_2

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