Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Science and Its Applications

ICCSA 2019: Computational Science and Its Applications – ICCSA 2019 pp 349–358Cite as

Analysis of Noise Perturbation on Neural Network Based ECG Classification

  • Conference paper
  • First Online:

  • 1927 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11623))

Abstract

The heart diseases are diagnosed by analysing the ECG signals. However, during the acquisition process, the ECG signals are affected by different noises. Therefore, it is crucial to realize a pretreatment of the ECG signals before extracting the features. This article aims to study the effects of the Empirical Mode Decomposition filtering and Butterworth filtering on arrhythmia classification based on the convolutional neural network. Five classes of arrhythmia are concerned, including the sino-auricular node dysfunction, the supra-ventricular tachycardia, the ventricular tachycardia, the auricular flutter and the auricular Fibrillation. The proposed approach is evaluated with the MIT-BIH Arrhythmia database.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Abdou, A.D., Ngom, N.F., Niang, O.: Electrocardiograms patterns analysis using artificial neural network and non-linear regression. In: Nabi, L.G., Eric, B., Watson, B. (eds.) CARI 2018 - Colloque africain sur la recherche en informatique et mathématiques appliquées, Stellenbosch, South Africa, pp. 25–36, October 2018

    Google Scholar 

  2. Abdou, A.-D., Ngom, N.F., Niang, O.: Classification and prediction of arrhythmias from electrocardiograms patterns based on empirical mode decomposition and neural network. In: Mendy, G., Ouya, S., Dioum, I., Thiaré, O. (eds.) AFRICOMM 2018. LNICST, vol. 275, pp. 174–184. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16042-5_17

    Chapter  Google Scholar 

  3. Abdou, A.D., Ngom, N.F., Sidibé, S., Niang, O., Thioune, A., Ndiaye, C.H.T.C.: Neural networks for biomedical signals classification based on empirical mode decomposition and principal component analysis. In: M. F. Kebe, C., Gueye, A., Ndiaye, A. (eds.) InterSol/CNRIA -2017. LNICST, vol. 204, pp. 267–278. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-72965-7_25

    Chapter  Google Scholar 

  4. Pourbabaee, B., Howe-Patterson, M., Reiher, E., Frederic, B.: Deep convolutional neural network for ECG-based human identification. Can. Med. Biol. Eng. Soc. 41 (2018)

    Google Scholar 

  5. Al-Bander, B., Al-Nuaimy, W., Williams, B.M., Zheng, Y.: Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc. Biomed. Signal Process. Control 40, 91–101 (2018)

    Article  Google Scholar 

  6. Jin, F., Sugavaneswaran, L., Krishnan, S., Chauhan, V.S.: Quantification of fragmented QRS complex using intrinsic time-scale decomposition. Biomed. Signal Process. Control 31, 513–523 (2017)

    Article  Google Scholar 

  7. Karhe, R.R., Bhagyashri, B.: Arrhythmia detection using one dimensional convolutional neural network. Int. J. Eng. Technol. 5(8) (2018)

    Google Scholar 

  8. Kim, Y.: Convolutional neural networks for sentence classification. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)

    Google Scholar 

  9. Martin, L., Johan, J., Per, T., Amy, L., Mats, L.: Computer aided detection of ureteral stones in thin slice computed tomography volumes using convolutional neural networks. Comput. Biol. Med. 97, 153–160 (2018)

    Article  Google Scholar 

  10. Hadjem, M., Salem, O., Naït-Abdesselam, F.: An ECG monitoring system for prediction of cardiac anomalies using WBAN. In: IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 441–446 (2014)

    Google Scholar 

  11. Nazarahari, M., Namin, S.G., Markazi, A.H.D., Anaraki, A.K.: A multi-wavelet optimization approach using similarity measures for electrocardiogram signal classification. Biomed. Signal Process. Control 20, 142–151 (2015)

    Article  Google Scholar 

  12. Mitra, M., Chakrabarty, S., Mia, Md.S., Rahman, A.: Identification of arrhythmia in electrocardiogram (ECG) using statistical tools and non-linear analysis. Int. J. Bioinform. Biomed. Eng. 4(2) (2018)

    Google Scholar 

  13. Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. 20, 45–50 (2001)

    Article  Google Scholar 

  14. Niang, O., Thioune, A., Delechelle, E., Lemoine, J.: Spectral intrinsic decomposition method for adaptative signal representation. ISRN Signal Process. 9, 3 (2012)

    MATH  Google Scholar 

  15. Padmavathi, S., Ramanujam, E.: Naïve Bayes classifier for ECG abnormalities using multivariate maximal time series Motif. Procedia Comput. Sci. 47, 222–228 (2015)

    Article  Google Scholar 

  16. Pal, S., Mitra, M.: Empirical mode decomposition based on ECG enhencement and QRS detection. Comput. Biol. Med. 42, 83–92 (2012)

    Article  Google Scholar 

  17. Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M.: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci. 415–416, 190–198 (2017)

    Article  Google Scholar 

  18. Acharya, U.R., Fujita, H., Lih, O.S., Hagiwara, Y., Tan, J.H., Adam, M.: Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf. Sci. 405, 81–90 (2017)

    Article  Google Scholar 

  19. Rodriguez, R., Mexicano, A., Bila, J., Cervantes, S., Ponce, R.: Feature extraction of electrocardiogram signals by aplying adaptative threshold and principal component analysis. J. Appl. Res. Technol. 13, 261–269 (2015)

    Article  Google Scholar 

  20. Slimane, Z.H., Nait Ali, A.: QRS complex detection using empirical mode decomposition. Digital Signal Process. 20, 1221–1228 (2010)

    Article  Google Scholar 

  21. Subbiah, S., Suresh, S.: Biomedical arrhythmia heart diseases classification based on artificial neural network and machine learning approach. Int. J. Eng. Technol. 7, 10–14 (2018)

    Google Scholar 

  22. Xiang, Y., Lin, Z., Meng, J.: Automatic QRS complex detection using two level convolutional neural network. BioMed. Eng. OnLine 17, 13 (2018)

    Article  Google Scholar 

  23. Zhang, H.: An improved QRS wave group detection algorithm and matlab implementation. Phys. Procedia 25, 1010–1016 (2012)

    Article  Google Scholar 

  24. Ziat, A.Y.: Apprentissage de représentation pour la prédiction et la classification de séries temporelles. Réseau de neurones. PhD thesis, Université Pierre et Marie Curie - Paris VI (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université de Thies, Thiès, Senegal

    Abdoul-Dalibou Abdou

  2. Ecole Polytechnique de Thies, Thiès, Senegal

    Ndeye Fatou Ngom & Oumar Niang

  3. Laboratoire Traitement de l’Information et Systémes Intelligents, Thiès, Senegal

    Abdoul-Dalibou Abdou, Ndeye Fatou Ngom & Oumar Niang

  4. Université Gaston berger, Saint-Louis, Senegal

    Mohamed Cheikh Ould Guera

Authors
  1. Abdoul-Dalibou Abdou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ndeye Fatou Ngom
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oumar Niang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed Cheikh Ould Guera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdoul-Dalibou Abdou .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg , Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abdou, AD., Ngom, N.F., Niang, O., Guera, M.C.O. (2019). Analysis of Noise Perturbation on Neural Network Based ECG Classification. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11623. Springer, Cham. https://doi.org/10.1007/978-3-030-24308-1_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24308-1_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24307-4

  • Online ISBN: 978-3-030-24308-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation