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Efficient Extreme Learning Machine (ELM) Based Algorithm for Electrocardiogram (ECG) Heartbeat Classification

  • Khurram KhalilEmail author
  • Umer Asgher
  • Yasar Ayaz
  • Riaz Ahmad
  • Salman Nazir
  • Sara Ali
  • Sofia Scataglini
  • Noriyuki Oka
Conference paper
  • 5 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1201)

Abstract

Electrocardiogram (ECG) estimates the electric signals activity of the human heart and is extensively used for sensing heart aberrations due to ease of use and non-invasive application on human body. Human heart is a one of the vital organs of human body. In an industrial environment, heart impairments and abnormalities are attributed to the different causes including work overload, occupational and workplace stress. Cardiovascular Disease (CD) of heart refers the conditions involving different heart’s frequency deviations and are mostly ascribed to the workplace stress, fatigue and strain. Early detection of deviated heartbeats may prevent premature morbidity and unhealthy rhythms under occupational stress. The Electrocardiography (ECG) is one of the widely used diagnostic test tools that cardiologists use to diagnose heart anomalies, impairments and diseases. Various approaches have been proposed to correctly classify the ECG signals. In this study, a fast ECG classification method based on Extreme Learning Machines (ELM) algorithm is proposed to classify the frequency rhythms in heartbeat. The MIT-BIH Arrhythmia Database having recordings of 47 subjects is used in this study. Proposed ELM method is evaluated and analyzed by dividing diagnostics datasets in 60:40 train-test split ratio and findings are compared with similar studies. Results confirm the feasibility of newly proposed ELM method both in terms of classification accuracy 97.55%, speed and computational power.

Keywords

Cardiovascular Disease Electrocardiography (ECG) Extreme Learning Machines (ELM) Heartbeat classification Myocardial infraction 

Notes

Acknowledgment

We acknowledge School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Pakistan and European Union (EU)’s Horizon 2020, Research and Innovation Staff Exchange Evaluations (RISE) under grant agreement No 823904 - ENHANCE project (MSCA-RISE 823904) for technical support and funding.

References

  1. 1.
    Quick, J.C., Henderson, D.F.: Occupational stress: preventing suffering, enhancing wellbeing. Int. J. Environ. Res. Public Health 13, 459 (2016)CrossRefGoogle Scholar
  2. 2.
    Sulsky, L, Smith, C.S.: Work Stress. Belmont (Calif.): Thomson/Wadsworth (2005)Google Scholar
  3. 3.
  4. 4.
    Xiao, B., Xu, Y., Bi, X., et al.: Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption. Neurocomputing.  https://doi.org/10.1016/j.neucom.2018.09.101
  5. 5.
    Jiang, Z., Choi, S.: A cardiac sound characteristic waveform method for in home heart disorder monitoring with electric stethoscope. Expert Syst. Appl. 31, 286–298 (2006)CrossRefGoogle Scholar
  6. 6.
    Esmaili, A., Kachuee, M., Shabany, M.: Nonlinear cuffless blood pressure estimation of healthy subjects using pulse transit time and arrival time. IEEE Trans. Instrum. Measure. 66(12), 3299–3308 (2017)CrossRefGoogle Scholar
  7. 7.
    Dastjerdi, A.E., Kachuee, M., Shabany, M.: Non-invasive blood pressure estimation using phonocardiogram. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–4. IEEE (2017)Google Scholar
  8. 8.
    Kachuee, M., Fazeli, S., Sarrafzadeh, M.: ECG heartbeat classification: a deep transferable representation. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp. 443–444. IEEE, June 2018Google Scholar
  9. 9.
    Zhang, W., Han, J., Deng, S.: Heart sound classification based on scaled spectrogram and partial least squares regression. Biomed. Signal Process. Control 32, 20–28 (2017)CrossRefGoogle Scholar
  10. 10.
    Inan, O.T., Giovangrandi, L., Kovacs, G.T.: Robust neural network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Trans. Biomed. Eng. 53(12), 2507–2515 (2006)CrossRefGoogle Scholar
  11. 11.
    Jin, L., Dong, J.: Classification of normal and abnormal ECG records using lead convolutional neural network and rule inference. Sci. China Inf. Sci. 60(7), 078103 (2017)Google Scholar
  12. 12.
    Kim, J., Shin, H.S., Shin, K., et al.: Robust algorithm for arrhythmia classification in ECG using extreme learning machine. BioMed Eng OnLine 8, 31 (2009)Google Scholar
  13. 13.
    Karpagachelvi, S., Arthanari, M., Sivakumar, M.: Classification of ECG signals using extreme learning machine. Comput. Inf. Sci. 4(1), 42 (2011)Google Scholar
  14. 14.
    Sara, J.D., Prasad, M., Eleid, M.F., Zhang, M., Widmer, R.J., Lerman, A.: Association between work related stress and coronary heart disease: a review of prospective studies through the job strain, effortreward balance, and organizational justice models. J. Am. Heart Assoc. 7(9) (2018).  https://doi.org/10.1161/jaha.117.008073
  15. 15.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)CrossRefGoogle Scholar
  16. 16.
    Huang, G.B., Bai, Z., Kasun, L.L.C., Vong, C.M.: Local receptive fields based extreme learning machine. IEEE Comput. Intell. Mag. 10(2), 18–29 (2015)CrossRefGoogle Scholar
  17. 17.
    Martis, R.J., Acharya, U.R., Lim, C.M., Mandana, K., Ray, A.K., Chakraborty, C.: Application of higher order cumulant features for cardiac health diagnosis using ECG signals. Int. J. Neural Syst. 23(04), 1350014 (2013)CrossRefGoogle Scholar
  18. 18.
    Li, T., Zhou, M.: ECG classification using wavelet packet entropy and random forests. Entropy 18(8), 285 (2016)CrossRefGoogle Scholar
  19. 19.
    Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A., San Tan, R.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89, 389–396 (2017)Google Scholar
  20. 20.
    Sharma, L., Tripathy, R., Dandapat, S.: Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans. Biomed. Eng. 62(7), 1827–1837 (2015)CrossRefGoogle Scholar
  21. 21.
    Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet. Circulation 101(23), e215–e220 (2000)CrossRefGoogle Scholar
  22. 22.
    Mendis, S, Puska, P, Norrving, B.: Global Atlas on Cardiovascular Disease Prevention and Control (PDF). World Health Organization in Collaboration with the World Heart Federation and the World Stroke Organization, pp. 3–18 (2011). ISBN 978-92-4-156437-3Google Scholar
  23. 23.
    Cybenko, G.: Approximations by superpositions of sigmoidal functions. Math. Control Signals Syst. 2(4), 303–314 (1989).  https://doi.org/10.1007/BF02551274MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Huang, G.B.: What are extreme learning machines? Filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle. Cogn. Comput. 7(3), 263–278 (2015)CrossRefGoogle Scholar
  25. 25.
    Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybernet. 2(2), 107–122 (2011)CrossRefGoogle Scholar
  26. 26.
    Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999–2015 on CDC WONDER Online Database, released December 2016. Data are from the Multiple Cause of Death Files, 1999-2015, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. http://wonder.cdc.gov/mcd-icd10.html

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Khurram Khalil
    • 1
    Email author
  • Umer Asgher
    • 1
  • Yasar Ayaz
    • 1
  • Riaz Ahmad
    • 1
    • 2
  • Salman Nazir
    • 3
  • Sara Ali
    • 1
  • Sofia Scataglini
    • 4
  • Noriyuki Oka
    • 5
  1. 1.School of Mechanical and Manufacturing Engineering (SMME)National University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.Directorate of Quality Assurance and International CollaborationNational University of Sciences and Technology (NUST)IslamabadPakistan
  3. 3.Department of Maritime OperationsUniversity of South-Eastern Norway (USN)BorreNorway
  4. 4.Department of Product Development, Faculty of Design SciencesUniversity of AntwerpAntwerpBelgium
  5. 5.Department of RehabilitationNerima Ken-ikukai HospitalTokyoJapan

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