Efficient Extreme Learning Machine (ELM) Based Algorithm for Electrocardiogram (ECG) Heartbeat Classification
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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.
KeywordsCardiovascular Disease Electrocardiography (ECG) Extreme Learning Machines (ELM) Heartbeat classification Myocardial infraction
手机体育投注平台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.
- 1.Quick, J.C., Henderson, D.F.: Occupational stress: preventing suffering, enhancing wellbeing. Int. J. Environ. Res. Public Health 13, 459 (2016)
- 2.Sulsky, L, Smith, C.S.: Work Stress. Belmont (Calif.): Thomson/Wadsworth (2005)
- 3.World Health Organization (WHO).
- 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.
- 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)
- 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)
- 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)
- 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 2018
- 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)
- 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)
- 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)
- 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)
- 13.Karpagachelvi, S., Arthanari, M., Sivakumar, M.: Classification of ECG signals using extreme learning machine. Comput. Inf. Sci. 4(1), 42 (2011)
- 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).
- 15.Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
- 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)
- 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)
- 18.Li, T., Zhou, M.: ECG classification using wavelet packet entropy and random forests. Entropy 18(8), 285 (2016)
- 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)
- 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)
- 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)
- 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 978-92-4-156437-3
- 23.Cybenko, G.: Approximations by superpositions of sigmoidal functions. Math. Control Signals Syst. 2(4), 303–314 (1989).
- 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)
- 25.Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybernet. 2(2), 107–122 (2011)
- 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.