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R-Peaks Detection Using Shannon Energy for HRV Analysis

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Advances in Signal Processing and Communication

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 526))

Abstract

In this paper, a system for R-peak detection in ECG signal is presented that applicable in different heart rate variability (HRV) applications based on S-transform and Shannon energy. The presented technique and system are efficient in R-peak detection as per results illustrated up to 99.80% of sensitivity and positive predictivity. Here, Shannon energy envelope computes sharp peaks that help in the allocation of peak position in ECG signal. The presented technique is evaluated on 27 records of MIT-BIH arrhythmia database of ECG signals. A tool named as SpandanV.1 (Cardiac Rhythm Variability, version 1) also demonstrated for R-peak detection with HRV analysis.

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References

  1. Finegold, J.A., Asaria, P., Francis, D.P.: Mortality from ischaemic heart disease by country, regin, and age: statistics from world orgranisation and unites nations. Int. J. Cardiol. 168, 934–945 (2012)

    Article  Google Scholar 

  2. Acharya, U.R., Joseph, K.P., Kannathal, N., Lim, ChM, Suri, J.S.: Heart rate variability: a review. Med. Biol. Eng. Comput. 44, 1031–1051 (2006)

    Article  Google Scholar 

  3. Chen, X., Yang, R., Ge, L., Zhang, L., Lv, R.: Heart rate variability analysis during hypnosis using wavelet transformation. Biomed. Signal Process. Control 31, 1–5 (2017)

    Article  Google Scholar 

  4. John, A.A., Subramanian, A.P., Jaganathan, S.K., Sethuraman, B.: Evaluation of cardiac signals using discrete wavelet transform with MATLAB graphical user interface. Indian Heart J. 67, 549–551 (2015)

    Article  Google Scholar 

  5. Arzeno, N., Deng, Zhi-De., Poon, C.: Analysis of first-derivative based QRS detection algorithm. IEEE Trans. Biomed. Eng. 55(2), 478–484 (2008)

    Article  Google Scholar 

  6. Afonso, V.X., Tompkins, W.J., Nguyen, T.Q., Luo, S.: ECG beat detection using filter banks. IEEE Trans. Biomed. Eng. 46(2), 192–202 (1999)

    Article  Google Scholar 

  7. Okada, M.: A digital filter for the QRS complex detection. IEEE Trans. Biomed. Eng. 26(12), 700–703 (1999)

    Article  Google Scholar 

  8. Abidullaev, B., Seo, H.: A new QRS detection method using wavelets and artificial neural networks. J. Med. Syst. 35(4), 683–691 (2011)

    Article  Google Scholar 

  9. Zidelmal, Z., Amirou, A., Adnane, M., Belouchrani, Adel.: QRS detection based on wavelet coefficients. Comput. Method. Program. Biomed. 107, 490–496 (2012)

    Article  Google Scholar 

  10. Ruchita, G., Sharma, A.K.: Detection of QRS complex of ECG recording based on wavelet transform using Matlab. Int. J. Eng. Syst. 2(7), 3038–3034 (2010)

    Google Scholar 

  11. Chen, S.W., Chen, C.H., Chan, H.L.: A real-time QRS method based on moving-averaging incorporating with wavelet denoising. Comput. Method. Program. Biomed. 82(3), 187–195 (2006)

    Article  Google Scholar 

  12. Moukadem, A., Dieterlen, A., Hueber, N., Brandt, C.: A robust heart sounds segmentation module based on S-Transform. Biomed. Signal Process. Control 8(13), 273–281 (2013)

    Article  Google Scholar 

  13. Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)

    Article  Google Scholar 

  14. Köhler, B.U., Hennig, C., Orglmeister, R.: The principles of software QRS detection. IEEE Eng. Med. Biol. Mag. 21(3), 42–57 (2002)

    Article  Google Scholar 

  15. Kumar, R., Kumar, A., Singh, G.K.: Electrocardiogram signal compression based on 2D- transforms: a research overview. J. Med. Imaging Health Inf. 6(2), 285–296 (2016)

    Article  Google Scholar 

  16. Benitez, D., Gaydecki, P.A., Zaidi, A., Fitzpatrick, A.P.: The use of the Hilbert transform in ECG signal analysis. Comput. Biol. Med. 31(5), 399–406 (2001)

    Article  Google Scholar 

  17. Mark, R., Moody, G.: MIT-BIH arrhythmia database. http://www.physionet.org/physiobank/database/mitdb/

  18. Stockwell, R.G., Mansinha, L., Lowe, R.P.: Localisation of the complex spectrum: the S-Transform. IEEE Trans. Signal Process. 44(4), 998–1001 (1996)

    Article  Google Scholar 

  19. Zidelmal, Z., Amirou, A., Ould-Abdeslam, D., Moukadem, A., Dieterlen, A.: QRS detection using S-transform and Shannon energy. Comput. Method Prog. Biomed. 116, 1–9 (2014)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, 482005, Madhya Pradesh, India

    Om Navin, Gautam Kumar, Nirmal Kumar & Anil Kumar

  2. Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, 201309, Uttar Pradesh, India

    Kuldeep Baderia

  3. Department of Electronics and Communication Engineering, Madanapalle Institute of Technology & Science, Madanapalle, India

    Ranjeet Kumar

Authors
  1. Om Navin
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  2. Gautam Kumar
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  3. Nirmal Kumar
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  4. Kuldeep Baderia
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  5. Ranjeet Kumar
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  6. Anil Kumar
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Corresponding author

Correspondence to Kuldeep Baderia .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Reno, NV, USA

    Banmali S. Rawat

  2. Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior, Gwalior, Madhya Pradesh, India

    Aditya Trivedi

  3. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Manhas

  4. Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Vikram Karwal

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Navin, O., Kumar, G., Kumar, N., Baderia, K., Kumar, R., Kumar, A. (2019). R-Peaks Detection Using Shannon Energy for HRV Analysis. In: Rawat, B., Trivedi, A., Manhas, S., Karwal, V. (eds) Advances in Signal Processing and Communication . Lecture Notes in Electrical Engineering, vol 526. Springer, Singapore. https://doi.org/10.1007/978-981-13-2553-3_39

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  • DOI: https://doi.org/10.1007/978-981-13-2553-3_39

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2552-6

  • Online ISBN: 978-981-13-2553-3

  • eBook Packages: EngineeringEngineering (R0)

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