Advanced Methods and Implementation Tools for Cardiac Signal Analysis

  • Safa MejhoudiEmail author
  • Rachid Latif
  • Abdelhafid Elouardi
  • Wissam Jenkal
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)


The heart is considered as a muscular pump that propels the blood toward all the cells of the human body, this hollow muscle has an internal electrical activity that allows it to contract automatically. Measuring this activity, called ECG signal, is used to diagnose the heart disorders. So, in this chapter, we survey the current state-of-the-art methods of ECG processing which contain several steps such as preprocessing or denoising, feature extraction and then arrhythmias detection; and the technological solutions for real-time implementation on embedded architectures as CPU, GPU, or FPGA. Finally, we discuss drawbacks and limitations of the presented methods with concluding remarks and future challenges.


ECG signal Feature extraction Algorithm Real-time processing Embedded architectures 



This work is partially supported by the National Centre of Scientific and Technical Research of Morocco (CNRST). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.


  1. Aboutabikh, K., & Aboukerdah, N. (2015). Design and implementation of a multiband digital filter using FPGA to extract the ECG signal in the presence of different interference signals. Computers in Biology and Medicine, 62, 1–13.CrossRefGoogle Scholar
  2. Alberdi, A., Aztiria, A., & Basarab, A. (2016). Towards an automatic early stress recognition system for office environments based on multimodal measurements: a review. Journal of Biomedical Informatics, 59(2016), 49–75.CrossRefGoogle Scholar
  3. Balda, R., Diller, G., Deardorff, E., Doue, J., & Hsieh, P. (1977). The HP ECG analysis program. In Trends in Computer Processed Electrocardiograms (pp. 197–205).Google Scholar
  4. Bhaskar, P. C., & Uplane, M. D. (2016). High frequency electromyogram noise removal from electrocardiogram using FIR low pass filter based on FPGA. Procedia Technology, 25, 497–504.CrossRefGoogle Scholar
  5. Blanco-Velasco, M., Weng, B., & Barner, K. E. (2008). ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in Biology and Medicine, 38, 1–13. Scholar
  6. Borries, R. F., Pierluissi, H. J., & Nazeran, H. (2005). Wavelet transform based ECG baseline drift removal for body surface potential mapping. In Proceedings of the 27th Annual Conference on Engineering in Medicine and Biology, Shanghai (pp. 3891–3894).Google Scholar
  7. Cao, X., & Li, Z. (2010). Denoising of ECG signal based on a comprehensive framework. International Conference on Multimedia Technology (ICMT), 1(4), 29–31.Google Scholar
  8. Chouhan, S., & Mehta, S. S. (2007). Total removal of baseline drift from ECG signal. In Presented at International Conference on Computing: Theory and Applications, ICCTA’07.Google Scholar
  9. Clifford, G. D., Azuaje, F., & McSharry, P. E. (2006). Advanced methods and tools for ECG data analysis. Artech House Publishers.Google Scholar
  10. Cuomo, S., De Michele, P., Galletti, A., & Marcellino, L. (2016). A GPU parallel algorithm for ECG signal denoising based on the NLM method. In Proceedings of the IEEE 30th International Conference on Advanced Information Networking and Applications Workshops, WAINA (pp. 35–39).
  11. El Hassan, E. L. M., & Karim, M. (2014). An FPGA-based implementation of a pre-processing stage for ECG signal analysis using DWT. 978-1-4799-4647-1/14/$31.00 ©2014. IEEE.Google Scholar
  12. Elhaj, F. A., Naomie, S., Arief, R. H., Tan, T. S., & Taqwa, A. (2016). Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Computer Methods and Programs in Biomedicine, 127, 52–63.CrossRefGoogle Scholar
  13. Gautam, A., & Lee, H. J. (April–June 2010). ECG signal de-noising with asynchronous averaging and filtering algorithm. International Journal of Healthcare Information Systems and Informatics, 5(2), 30–36.CrossRefGoogle Scholar
  14. Giorgio, A. (2016). A new FPGA-based medical device for the real time prevention of the risk of arrhythmias. International Journal of Applied Engineering Research, 11(8), 6013–6017. ISSN 0973–4562.Google Scholar
  15. Gupta, V., Chaurasia, V., & Shandilya, M. (2015). Random-valued impulse noise removal using adaptive dual threshold median filter. Journal of Visual Communication and Image Representation, 26, 296–304.CrossRefGoogle Scholar
  16. Hashim, M. A., Hau, Y. W., & Baktheri, R. (2016). Efficient QRS complex detection algorithm implementation on soc-based embedded system. JurnalTeknologi (Sciences & Engineering), 78(7–5), 49–58.Google Scholar
  17. Jenkal, W., Latif, R., Toumanari, A., Dliou, A., & El B’charri, O. (2015a). An efficient method of ECG signals denoising based on an adaptive algorithm using mean filter and an adaptive dual threshold filter. International Review on Computers and Software (IRECOS), 10(11), 1089–1095.CrossRefGoogle Scholar
  18. Jenkal, W., Latif, R., Toumanari, A., Dliou, A., El B’charri, O., & Maoulainine F. M. R. (2015b). Efficient method of QRS complex extraction using a multilevel algorithm and an adaptive thresholding technique. In Third World Conference on Complex Systems (WCCS) (pp. 1–5). IEEE.Google Scholar
  19. Jenkal, W., Latif, R., Toumanari, A., Dliou, A., El B’charri, O., &Maoulainine, F. M. R. (2016). An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Biocybernetics and Biomedical Engineering, 36(3), 499–508.CrossRefGoogle Scholar
  20. Jenkal, W., Latif, R., Toumanari, A., Elouardi, A., Hatim, A., & El Bcharri, O. (2018). Real-time hardware architecture of the adaptive dual threshold filter based ECG signal denoising. Journal of Theoretical and Applied Information Technology.Google Scholar
  21. Kabir, M. A., & Shahnaz, C. (2012). Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed Signal Process Control, 7, 481–489. Scholar
  22. Kumari, L. V. R., Padma, Y., Balaji, S. N., & Viswada, K. (2015). FPGA based arrhythmia detection. Procedia Computer Science, 57, 970–979.CrossRefGoogle Scholar
  23. Kumm, M., Möller, K., & Zipf, P. (2013). Reconfigurable FIR filter using distributed arithmetic on FPGAs. In IEEE International Symposium on Circuits and Systems (ISCAS 2013).
  24. Laguna, P., Jane, R., & Caminal, P. (1992). Adaptive filtering of ECG baseline wander. In Presented at Engineering in Medicine and Biology Society. Proceedings of the Annual International Conference of the IEEE (Vol. 14).Google Scholar
  25. Lim, H. W., Mohd Sani, M. S. A., Hashim, A., & Hau, Y. W. (2015). Throb: System-on-Chip based arrhythmia screener with self-interpretation. International Journal of Electrical and Electronic Systems Research, (Special issue: Innovate Malaysia Design Conference), 8, 30–36.Google Scholar
  26. Mallat, S. (2009). A wavelet tour of signal processing. Academic Press.Google Scholar
  27. Muthuswamy, J. (2003). Biomedical signal analysis. In Standard handbook of biomedical engineering and design. Tempe, Arizona: McGraw-Hill.Google Scholar
  28. Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, 32, 230–236.CrossRefGoogle Scholar
  29. Rudin, L. I., Osher, S., & Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 60(1), 259–268.MathSciNetCrossRefGoogle Scholar
  30. Shailesh, K., Damodar, P., & Sahu, P. K. (2018). Denoising of electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique. Biocybernetics and Biomedical Engineering, 38, 297–312.CrossRefGoogle Scholar
  31. Sharma, T., & Sharma, K. K. (2017). QRS complex detection in ECG signals using locally adaptive weighted total variation denoising. Computers in Biology and Medicine, 87, 187–199.CrossRefGoogle Scholar
  32. Sudhakar, V., Murthy, N. S., & Anjaneyulu, L. (2012). Area efficient pipelined architecture for realization of FIR filter using distributed arithmetic. In International Conference on Industrial and Intelligent Information (ICIII 2012) IPCSIT (Vol. 31). Singapore: IACSIT Press.Google Scholar
  33. Tracey, B. H., & Miller, E. L. (2012). Nonlocal means denoising of ECG signals. IEEE Transactions on Biomedical Engineering, 59(9), 2383–2386.CrossRefGoogle Scholar
  34. Wang, H., Azuaje, F., & Black, N. (2002). Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks. IEEE Transactions on Nanobioscience, 1(4), 146–166.CrossRefGoogle Scholar
  35. Wenfeng, S., Daming, W., Weimin, X., Xin, Z., & Shizhong, Y. (2010). Parallelized computation for computer simulation of electrocardiograms using personal computers with multi-core CPU and general-purpose GPU. Computer Methods and Programs in Biomedicine, 100, 87–96.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Safa Mejhoudi
    • 1
    Email author
  • Rachid Latif
    • 1
  • Abdelhafid Elouardi
    • 2
  • Wissam Jenkal
    • 1
  1. 1.Laboratory of Systems Engineering and Information Technology (LiSTi)ENSA, Ibn Zohr UniversityAgadirMorocco
  2. 2.SATIE, Digiteo LabsParis-Sud University, Paris Saclay UniversityOrsayFrance

Personalised recommendations