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Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques

Physical and Engineering Sciences in Medicine volume 43, pages 623–634 (2020)Cite this article

Abstract

An approach is proposed for the detection of chronic heart disorders from the electrocardiogram (ECG) signals. It utilizes an intelligent event-driven ECG signal acquisition system to achieve a real-time compression and effective signal processing and transmission. The experimental results show that grace of event-driven nature an overall 2.6 times compression and bandwidth utilization gain is attained by the suggested solution compared to the counter classical methods. It results in a significant reduction in the complexity and execution time of the post denoising, features extraction and classification processes. The overall system precision is studied in terms of the classification accuracy, the F-measure, the area under the ROC curve (AUC) and the Kappa statistics. The best classification accuracy of 94.07% is attained. It confirms that the designed event-driven solution realizes a computationally efficient automatic diagnosis of the cardiac arrhythmia while achieving a high precision decision support for cloud-based mobile health monitoring.

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References

  1. Mozaffarian D et al. (2016) Heart disease and stroke statistics—2016 update. Circulation

  2. Alickovic E, Subasi A (2015) Effect of multiscale PCA de-noising in ECG beat classification for diagnosis of cardiovascular diseases. Circuit Syst Signal Process 34(2):513–533

    Google Scholar 

  3. Rai HM, Trivedi A, Shukla S (2013) ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier. Measurement 46(9):3238–3246

    Google Scholar 

  4. Phukpattaranont P (2015) QRS detection algorithm based on the quadratic filter. Expert Syst Appl 42(11):4867–4877

    Google Scholar 

  5. Hesar HD, Mohebbi M (2016) ECG denoising using marginalized particle extended kalman filter with an automatic particle weighting strategy. IEEE J Biomed Health Inform 21(3):635–644

    PubMed  Google Scholar 

  6. Rodríguez R, Mexicano A, Bila J, Cervantes S, Ponce R (2015) Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. J Appl Res Technol 13(2):261–269

    Google Scholar 

  7. da Luz EJS, Schwartz WR, Cámara-Chávez G, Menotti D (2016) ECG-based heartbeat classification for arrhythmia detection: a survey. Comput. Methods Programs Biomed. 127:144–164

    PubMed  Google Scholar 

  8. Li P et al (2016) High-performance personalized heartbeat classification model for long-term ECG signal. IEEE Trans Biomed Eng 64(1):78–86

    PubMed  Google Scholar 

  9. Zhang X, Lian Y (2014) A 300-mV 220-nW event-driven ADC with real-time QRS detection for wearable ECG sensors. IEEE Trans Biomed Circuits Syst 8(6):834–843

    PubMed  Google Scholar 

  10. de Ruvo E et al (2016) A prospective comparison of remote monitoring systems in implantable cardiac defibrillators: potential effects of frequency of transmissions. J Interv Card Electrophysiol 45(1):81–90

    PubMed  Google Scholar 

  11. Rezaii TY, Beheshti S, Shamsi M, Eftekharifar S (2018) ECG signal compression and denoising via optimum sparsity order selection in compressed sensing framework. Biomed Signal Process Control 41:161–171

    Google Scholar 

  12. Shaw L, Rahman D, Routray A (2018) Highly efficient compression algorithms for multichannel EEG. IEEE Trans Neural Syst Rehabil Eng 26(5):957–968

    PubMed  Google Scholar 

  13. Niederhauser T, Haeberlin A, Jesacher B, Fischer A, Tanner H (2017) “Model-based delineation of non-uniformly sampled ECG signals”, presented at the. Computing in Cardiology (CinC) 2017:1–4

    Google Scholar 

  14. Qaisar SM, Subasi A (2018) An adaptive rate ECG acquisition and analysis for efficient diagnosis of the cardiovascular diseases. presented at the 2018 IEEE 3rd international conference on signal and image processing (ICSIP), pp. 177–181

  15. Budiman ES (2017) Multi-rate analyte sensor data collection with sample rate configurable signal processing

  16. Qaisar SM, Fesquet L, Renaudin M (2014) Adaptive rate filtering a computationally efficient signal processing approach. Signal Process 94:620–630

    Google Scholar 

  17. Qaisar SM et al. (2017) Time-domain characterization of a wireless ECG system event driven A/D converter. Presented at the 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6.

  18. Qaisar SM, Yahiaoui R, Gharbi T (2013) An efficient signal acquisition with an adaptive rate A/D conversion. Presented at the 2013 IEEE international conference on circuits and systems (ICCAS), pp. 124–129.

  19. Hou Y et al (2018) “A 61-nW Level-Crossing ADC With Adaptive Sampling for Biomedical. Appl IEEE Trans Circuits Syst II Express Briefs 66(1):56–60

    Google Scholar 

  20. Marisa T et al (2017) Pseudo asynchronous level crossing ADC for ECG signal acquisition. IEEE Trans Biomed Circuits Syst 11(2):267–278

    CAS  PubMed  Google Scholar 

  21. Miskowicz M (2015) Event-based control and signal processing. CRC Press, New York

    Google Scholar 

  22. Mashhadi MB, Salarieh N, Farahani ES, Marvasti F (2017) Level crossing speech sampling and its sparsity promoting reconstruction using an iterative method with adaptive thresholding. IET Signal Process 11(6):721–726

    Google Scholar 

  23. Greitans M, Shavelis R, Fesquet L, Beyrouthy T (2011) Combined peak and level-crossing sampling scheme. Presented at the 9th International Conference on Sampling Theory and Applications SampTA 2011

  24. Moser BA, Lunglmayr M (2019) On quasi-isometry of threshold-based sampling. IEEE Trans Signal Process. 67:3238–3841

    Google Scholar 

  25. Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 20(3):45–50

    CAS  PubMed  Google Scholar 

  26. Welch TB, Wright CH, Morrow MG (2016) Real-time digital signal processing from MATLAB to C with the TMS320C6x DSPs. CRC Press, New York

    Google Scholar 

  27. Qaisar SM, Akbar M, Beyrouthy T, Al-Habib W, Asmatulah M (2016) An error measurement for resampled level crossing signal. presented at the 2016 Second International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP), pp. 1–4

  28. A. Subasi, Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach. Academic Press, 2019.

  29. Cavanagh J (2017) Computer arithmetic and Verilog HDL fundamentals. CRC Press, New York

    Google Scholar 

  30. Kim H, Yazicioglu RF, Merken P, Van Hoof C, Yoo H-J (2009) ECG signal compression and classification algorithm with quad level vector for ECG holter system. IEEE Trans Inf Technol Biomed 14(1):93–100

    PubMed  Google Scholar 

  31. Yeh Y-C, Wang W-J, Chiou CW (2010) A novel fuzzy c-means method for classifying heartbeat cases from ECG signals. Measurement 43(10):1542–1555

    Google Scholar 

  32. Melgani F, Bazi Y (2008) Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE Trans Inf Technol Biomed 12(5):667–677

    PubMed  Google Scholar 

  33. Khalaf AF, Owis MI, Yassine IA (2015) A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines. Expert Syst Appl 42(21):8361–8368

    Google Scholar 

  34. Dutta S, Chatterjee A, Munshi S (2010) Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification. Med Eng Phys 32(10):1161–1169

    PubMed  Google Scholar 

  35. Gothwal H, Kedawat S, Kumar R (2011) Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network. J Biomed Sci Eng 4(04):289

    Google Scholar 

  36. Shen C-P, Kao W-C, Yang Y-Y, Hsu M-C, Wu Y-T, Lai F (2012) Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines. Expert Syst Appl 39(9):7845–7852

    Google Scholar 

  37. Martis RJ, Acharya UR, Mandana K, Ray AK, Chakraborty C (2012) Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst Appl 39(14):11792–11800

    Google Scholar 

  38. Zadeh AE, Khazaee A, Ranaee V (2010) Classification of the electrocardiogram signals using supervised classifiers and efficient features. Comput Methods Programs Biomed 99(2):179–194

    PubMed  Google Scholar 

  39. Faezipour M, Tiwari TM, Saeed A, Nourani M, Tamil LS (2009) “Wavelet-based denoising and beat detection of ECG signal”, presented at the. IEEE/NIH Life Sci Syst Appl Workshop 2009:100–103

    Google Scholar 

  40. Özbay Y, Ceylan R, Karlik B (2011) Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier. Expert Syst Appl 38(1):1004–1010

    Google Scholar 

  41. Qaisar SM (2019) Efficient mobile systems based on adaptive rate signal processing. Comput Electr Eng 79:106462

    Google Scholar 

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Acknowledgements

Authors are thankful to anonymous reviewers for their valuable feedback.

Funding

This project is funded by the Effat University with the decision number UC#7/28Feb 2018/10.2-44 g.

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  1. College of Engineering, Effat University, Jeddah, 21478, Saudi Arabia

    Saeed Mian Qaisar & Abdulhamit Subasi

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  1. Saeed Mian Qaisar
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  2. Abdulhamit Subasi
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Correspondence to Saeed Mian Qaisar.

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Mian Qaisar, S., Subasi, A. Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques. Phys Eng Sci Med 43, 623–634 (2020). https://doi.org/10.1007/s13246-020-00863-6

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  • DOI: https://doi.org/10.1007/s13246-020-00863-6

Keywords

  • Electrocardiogram (ECG)
  • Event-driven acquisition
  • Compression
  • Adaptive-rate processing
  • Autoregressive burg
  • Features extraction
  • Machine learning
  • Cloud-based mobile health monitoring