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Detection of Normal and Abnormal ECG Signal Using ANN

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Advances in Intelligent Informatics, Smart Technology and Natural Language Processing (iSAI-NLP 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 807))

Abstract

The normality & abnormality of the heart is normally monitored by ECG. Several algorithms are proposed to classify ECG signals. In this paper, discrete wavelet transform is used for extracting some statistical features and Multilayer perceptron (MLP) neural network with Back-propagation performs the classification task. Two types of ECG signals (normal and abnormal) can be detected in this work from each database. The records from MIT-BIH Arrhythmias and Apnea ECG database from physionet have been used for training and testing our neural network based classifier. 90% healthy and 100% abnormal are detected in MIT-BIH Arrhythmias database with the overall accuracy of 94.44%. In Apnea-ECG database, 96% normal and 95.6% abnormal ECG signals are detected and achieves 95.7% classification rate.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, NIT Manipur, Manipur, Imphal, India

    Sourav Mondal & Prakash Choudhary

Authors
  1. Sourav Mondal
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  2. Prakash Choudhary
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Corresponding author

Correspondence to Sourav Mondal .

Editor information

Editors and Affiliations

  1. Sirindhorn International Institute of Technology (SIIT), Thammasat University, Bangkadi Muang Pathumthani, Thailand

    Thanaruk Theeramunkong

  2. Faculty of Science and Technology, Department of Computer Science, Thammasat University, Pathumthani, Thailand

    Rachada Kongkachandra

  3. Department of Information Technology Management, Faculty of Information Technology , King Mongkut’s University of Technology North Bangkok (KMUTNB), Bangkok, Thailand

    Mahasak Ketcham

  4. Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakorn Pathom, Thailand

    Narit Hnoohom

  5. Faculty of Science and Technology, Department of Computer Science, Thammasat University , Pathum Thani, Thailand

    Pokpong Songmuang

  6. National Science and Technology Development Agency, Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, Pathum Thani, Thailand

    Thepchai Supnithi

  7. Prince of Songkla University , Phuket, Thailand

    Kiyota Hashimoto

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Mondal, S., Choudhary, P. (2019). Detection of Normal and Abnormal ECG Signal Using ANN. In: Theeramunkong, T., et al. Advances in Intelligent Informatics, Smart Technology and Natural Language Processing. iSAI-NLP 2017. Advances in Intelligent Systems and Computing, vol 807. Springer, Cham. https://doi.org/10.1007/978-3-319-94703-7_3

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