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Design of a Biorthogonal Wavelet Transform Based R-Peak Detection and Data Compression Scheme for Implantable Cardiac Pacemaker Systems

  • Mobile & Wireless Health
  • Published:
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Abstract

Bradycardia can be modulated using the cardiac pacemaker, an implantable medical device which sets and balances the patient’s cardiac health. The device has been widely used to detect and monitor the patient’s heart rate. The data collected hence has the highest authenticity assurance and is convenient for further electric stimulation. In the pacemaker, ECG detector is one of the most important element. The device is available in its new digital form, which is more efficient and accurate in performance with the added advantage of economical power consumption platform. In this work, a joint algorithm based on biorthogonal wavelet transform and run-length encoding (RLE) is proposed for QRS complex detection of the ECG signal and compressing the detected ECG data. Biorthogonal wavelet transform of the input ECG signal is first calculated using a modified demand based filter bank architecture which consists of a series combination of three lowpass filters with a highpass filter. Lowpass and highpass filters are realized using a linear phase structure which reduces the hardware cost of the proposed design approximately by 50%. Then, the location of the R-peak is found by comparing the denoised ECG signal with the threshold value. The proposed R-peak detector achieves the highest sensitivity and positive predictivity of 99.75 and 99.98 respectively with the MIT-BIH arrhythmia database. Also, the proposed R-peak detector achieves a comparatively low data error rate (DER) of 0.002. The use of RLE for the compression of detected ECG data achieves a higher compression ratio (CR) of 17.1. To justify the effectiveness of the proposed algorithm, the results have been compared with the existing methods, like Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.

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References

  1. Aging, In World Health Organization. http://www.who.int/topics/ageing/en/, 2016.

  2. Banerjee, A., and Gupta, S., Analysis of smart mobile applications for healthcare under dynamic context changes. IEEE Trans. Mob. Comput. 14(5):904–919, 2015.

    Article  Google Scholar 

  3. Zhang, Y., and et al., Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. Accepted by IEEE Systems Journal.

  4. Zhang, Y., Chen, M., Huang, D. et al., iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization. Fut. Gener. Comput. Syst 66:30–35, 2017.

    Article  Google Scholar 

  5. Yang, Z., Zhou, Q., Lei, L., Zheng, K., and Xiang, W., An IoT-cloud based wearable ECG monitoring system for smart healthcare. J. Med. Syst., 1–11, 2016.

  6. Zou, Y., Han, J., Xuang, S., Huang, S., Weng, X., Fang, D., and Zeng, X., An energy-efficient design for ECG recording and R-peak detection based on wavelet transform. IEEE Trans. Circ. Syst. -II Exp. Briefs 62(2):119–124, 2015.

    Google Scholar 

  7. Liu, X., Zheng, Y., Phyu, M. W., Zhao, B., Je, M., and Yuan, X., Multiple functional ECG signals is processing for wearable applications of long-term cardiac monitoring. IEEE Trans. Biomed. Eng. 58(2):380–389, 2011.

    Article  PubMed  Google Scholar 

  8. Buxi, D., et al., Wireless 3-lead ECG system with on-board digital signal processing for ambulatory monitoring, in Proc. IEEE bio CAS, 308–31, 2012.

  9. Deepu, C., and Lian, Y., A joint QRS detection and data compression scheme for wearable sensors. IEEE Trans. Biomed. Eng. 62(1):165–175, 2015.

    Article  PubMed  CAS  Google Scholar 

  10. Merah, M., Abdelmalik, T. A., Larbi, B. H., R-peak detection based on stationary wavelet transform, Comput. Meth. Prog. Biomed, 1-12, 2015.

  11. Kumar, A., Komaragiri, R., and Kumar, M., From pacemaker to wearable: Techniques for ECG detection systems. Journal of Medical Systems 42(2):34, 2018.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  13. Hu, Y. H., Tompkins, W. J., Urrusti, J. L., and Afonso, V. X., Applications of artificial neural networks for ECG signal detection and classification. J. Electrocardiol. 26:66–73, 1994.

    Google Scholar 

  14. Min, Y. J., Kim, H. K., Kang, Y. R., Kim, G. S., Park, J., and Kim, S. W., Design of Wavelet-Based ECG detector for implantable cardiac pacemakers. IEEE Trans. on Biomedical Circuits and Systems 7(4):426–436, 2013.

    Article  PubMed  Google Scholar 

  15. Bhavtosh, D. B. and Kumar, Y., High performance QRS complex detector for wearable ECG system using multi scaled product with booth multiplier and soft threshold algorithm, IEEE ICSC-15, 204–209, 2015.

  16. Kumar, A., Berwal, D., Kumar, Y., Design of high-performance ECG detector for implantable cardiac pacemaker systems using biorthogonal wavelet transform (DOI: 10.1007/s00034-018-0754-3), 2018.

  17. Abibullaev, B., and Seo, H. D., A new QRS detection method using wavelets and artificial neural networks. Journal of medical systems 35(4):683–691, 2011.

    Article  PubMed  Google Scholar 

  18. Sumathi, S., Beaulah, H. L., and Vanithamani, R., A wavelet transform based feature extraction and classification of cardiac disorder. Journal of medical systems 38(9):98, 2014.

    Article  PubMed  CAS  Google Scholar 

  19. Benali, R., Reguig, F. B., and Slimane, Z. H., Automatic classification of heartbeats using wavelet neural network. Journal of medical systems 36(2):883–892, 2012.

    Article  PubMed  Google Scholar 

  20. S. M. Szilagyi and L. Szilagyi, Wavelet transform and neural-network-based adaptive filtering for QRS detection, in Proc. 22nd Annu. Int. Conf. IEEE Engineering in Medicine and Biology Soc., Chicago, 2:1267–1270, 2003.

  21. Arnavut, Z., ECG signal compression based on burrows-wheeler transformation and inversion ranks of linear prediction. IEEE Trans. Biomed. Eng. 54(3):410–418, 2007.

    Article  PubMed  Google Scholar 

  22. Miaou, S.-G., and Chao, S.-N., Wavelet-based lossy-to-lossless ECG compression in a unified vector quantization framework. IEEE Trans. Biomed. Eng. 52(3):539–543, 2005.

    Article  PubMed  Google Scholar 

  23. Chua, E., and Fang, W., Mixed bio-signal lossless data compressor for portable brain-heartmonitoring systems. IEEE Trans. Consum. Electron. 57(1):267–273, 2011.

    Article  Google Scholar 

  24. Chen, S.-L., and Wang, J.-G., VLSI implementation of low-power costefficient lossless ECG encoder design for wireless healthcare monitoring application. Electron. Lett. 49(2):91–93, 2013.

    Article  Google Scholar 

  25. Kohler, B.-U., Hennig, C., and Orglmeister, R., The principles of software QRS detection. IEEE Eng. Med. Biol. Mag. 21(1):42–57, 2002.

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

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

  28. Elgendi, M., Jonkman, M., DeBoer, F., Frequency bands effects on QRS detection. Pan, 5, 15Hz, 2010.

  29. Laguna, P., Mark, R. G., Goldberg, A., and Moody, G. B., A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput. Cardiol.:673–676, 1997.

  30. Sivannarayana, N., and Reddy, D. C., Biorthogonal wavelet transforms for ECG parameters estimation. Med. Engg. Phy. 21(3):167–174, 1999.

    Article  CAS  Google Scholar 

  31. Rodrigues, J. N., Olsson, T., Sornmo, L., and Owall, V., Digital implementation of a wavelet-based event detector for cardiac pacemakers. IEEE Trans. Circuits Syst. I, Reg. Papers 52(12):2686–2698, 2005.

    Article  Google Scholar 

  32. Mallat, S. G., A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7):674–693, 1989.

    Article  Google Scholar 

  33. Kumar, A., Tiwari, R. N., Kumar, M., Kumar, Y., A filter bank architecture based on wavelet transform for ECG signal denoising. In Signal Processing, Computing and Control (ISPCC), 2017 4th International Conference on (pp. 212–215). IEEE, 2017.

  34. Levi, I., ALbeck, A., Fish, A., and Wimer, S., A low energy and high performance DM2 adder, IEEE Trans. Circ. & Circ. I: REGULAR PAPERS, 61(11), 2014.

  35. Venkatachalam, S., and Ko, S. B., Design of power and area efficient approximate multipliers. IEEE Trans. VLSI Syst. 25(5):1782–1786, 2017.

    Article  Google Scholar 

  36. Gopeka, S. V., Murali, L., and Manigandan, T., VLSI Design of ECG QRS Complex Detection using Multiscale Mathematical Morphology, ICACCCT-14, 478–82, 2014.

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

    Article  PubMed  Google Scholar 

  38. Poli, R., Cagnoni, S., and Valli, G., Genetic design of optimum linear and nonlinear QRS detectors. IEEE Trans. Biomed. Eng. 42(11):1137–1141, 1995.

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  40. Ieong, C.-I., Mak, P.-I., Lam, C.-P., Dong, C., Vai, M.-I., Mak, P.-U., Pun, S.-H., Wan, F., and Martins, R. P., A 0.83-uW QRS detection processor using quadratic spline wavelet transform for wireless ECG acquisition in 0.35-uM CMOS. IEEE Trans. Biomed. Circuits Syst. 6(6):586–595, 2012.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Bennett University, Greater Noida, U.P, 201308, India

    Ashish Kumar, Manjeet Kumar & Rama Komaragiri

Authors
  1. Ashish Kumar
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  2. Manjeet Kumar
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  3. Rama Komaragiri
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Correspondence to Manjeet Kumar.

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Author Ashish Kumar declares that he has no conflict of interest. Author Manjeet Kumar declares that he has no conflict of interest. Author Rama Komaragiri declares that he has no conflict of interest.

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This article is part of the Topical Collection on Mobile & Wireless Health

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Kumar, A., Kumar, M. & Komaragiri, R. Design of a Biorthogonal Wavelet Transform Based R-Peak Detection and Data Compression Scheme for Implantable Cardiac Pacemaker Systems. J Med Syst 42, 102 (2018). https://doi.org/10.1007/s10916-018-0953-2

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