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Evaluation of Digital Compressed Sensing for Real-Time Wireless ECG System with Bluetooth low Energy

  • Mobile Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In this paper, a wearable and wireless ECG system is firstly designed with Bluetooth Low Energy (BLE). It can detect 3-lead ECG signals and is completely wireless. Secondly the digital Compressed Sensing (CS) is implemented to increase the energy efficiency of wireless ECG sensor. Different sparsifying basis, various compression ratio (CR) and several reconstruction algorithms are simulated and discussed. Finally the reconstruction is done by the android application (App) on smartphone to display the signal in real time. The power efficiency is measured and compared with the system without CS. The optimum satisfying basis built by 3-level decomposed db4 wavelet coefficients, 1-bit Bernoulli random matrix and the most suitable reconstruction algorithm are selected by the simulations and applied on the sensor node and App. The signal is successfully reconstructed and displayed on the App of smartphone. Battery life of sensor node is extended from 55 h to 67 h. The presented wireless ECG system with CS can significantly extend the battery life by 22 %. With the compact characteristic and long term working time, the system provides a feasible solution for the long term homecare utilization.

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References

  1. Wang, Y., Yu, K., Wang, D., Zhao, C., Wang, L. and Wang, P., Multi-Model Diagnosis Method for Lung Cancer based on MOS-SAW Breath Detecting e-Nose, in Proceedings of The 14th International Symposium on Olfaction and Electronic Nose, 2011.

  2. Wang, Y., Hu, Y., Wang, D., Yu, K., Wang, L., Zou, Y., Zhao, C., Zhang, X., Wang, P., and Ying, K., The analysis of volatile organic compounds biomarkers for lung cancer in exhaled breath, tissues and cell lines. Cancer Biomarkers 11:129–137, 2012.

    PubMed  Google Scholar 

  3. Cao, H., Li, H., Stocco, L., and Leung, V. C. M., Wireless three-pad ECG system: challenges, design, and evaluations. Commun. Netw., J. 13:113–124, 2011.

    Article  Google Scholar 

  4. Rattfaelt, L., Bjoerefors, F., Nilsson, D., Wang, X., Norberg, P., and Ask, P., Properties of screen printed electrocardiography smartware electrodes investigated in an electro-chemical cell. Biomed. Eng. OnLine 12(64):1–11, 2013.

    Google Scholar 

  5. Jeon, T., Kim, B., Jeon, M., and Lee, B.-G., Implementation of a portable device for real-time ECG signal analysis. Biomed. Eng. OnLine 13(160):1–13, 2014.

    Google Scholar 

  6. Havlik, J., Lhotska, L., Parak, J., Dvorak, J., Horcik, Z., and Pokorny, M., A modular system for rapid development of telemedical devices. J. Univ. Comput. Sci. 19(9):1242–1256, 2013.

    Google Scholar 

  7. Altini, M., Polito, S., Penders, J., Kim, H., Van Helleputte, N., Kim, S., Yazicioglu, F., An ECG patch combining a customized ultra-low-power ECG SoC with bluetooth low energy for long term ambulatory monitoring, In: Proceedings of the 2nd Conference on Wireless Health, pp. 1–2, 2011.

  8. Munshi, M.C., Xu, X., Zou, X., Soetiono, E., Teo, C.S., Lian, Y., Wireless ECG plaster for body sensor network, In: Proceedings of ISSS-MDBS 2008, pp. 310–313, 2008.

  9. Masse, F., Penders, J., Serteyn, A., Bussel, M. van and Arends, J., Miniaturized wireless ECG-monitor for real-time detection of epileptic seizures, In Proceedings of Wireless Health 2010, 2010.

  10. Chi, Y. M., Ng, P., and Cauwenberghs, G., Wireless noncontact ECG and EEG biopotential sensors. ACM Trans. Embed. Comput. Syst. 12(4):103–110, 2013.

    Article  Google Scholar 

  11. Dixon, A. M. R., Allstot, E. G., Gangopadhyay, D., and Allstot, D. J., Compressed sensing system considerations for ECG and EMG wireless biosensors. Biomed. Circ. Syst. IEEE Trans. 6(2):156–166, 2012.

    Article  Google Scholar 

  12. Candes, E. J., and Wakin, M. B., An introduction to compressive sampling. Signal Process. Mag. IEEE 25(2):21–30, 2008.

    Article  Google Scholar 

  13. Gangopadhyay, D., Allstot, E. G., Dixon, A. M. R., Natarajan, K., Gupta, S., and Allstot, D. J., Compressed sensing analog front-end for bio-sensor applications. Solid-State Circ., IEEE J. 49(2):426–438, 2014.

    Article  Google Scholar 

  14. Allstot, E.G., Chen, A.Y., Dixon, A.M.R., Gangopadhyay, D., Mitsuda, H., Allstot, D.J., Compressed sensing of ECG bio-signals using one-bit measurement matrices, In: New Circuits and Systems Conference (NEWCAS), 2011 I.E. 9th International, pp. 213–216, 2011.

  15. Wang, Y., Wunderlich, R., Heinen, S., Design and evaluation of a novel wireless reconstructed 3-lead ECG monitoring system, In: Biomedical Circuits and Systems Conference (BioCAS), 2013 IEEE, pp. 362–365, 2013.

  16. Wang, Y., Doleschel, S., Wunderlich, R., and Heinen, S., A wearable wireless ECG monitoring system with dynamic transmission power control for long-term homecare. J. Med. Syst. 39(3):1–10, 2015.

    Article  Google Scholar 

  17. Wang., Y., Wunderlich., R. and Heinen., S., A low noise wearable wireless ECG system with body motion cancellation for long term homecare, In Proceedings of IEEE Healthcom 2013 Conference, 2013.

  18. Candes, E. J., Romberg, J. K., and Tao, T., Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8):1207–1223, 2006.

    Article  Google Scholar 

  19. Donoho, D. L., Compressed sensing. IEEE Trans. Inf. Theory 52(4):1289–1306, 2006.

    Article  Google Scholar 

  20. Zou, W., and Pan, X., Compressed-sensing-based fluorescence molecular tomographic image reconstruction with grouped sources. Biomed. Eng. OnLine 13(119):1–15, 2014.

    Google Scholar 

  21. Mallat, S., A wavelet tour of signal processing: the sparse way. Academic Press, Boston, 2009.

    Google Scholar 

  22. Baraniuk, R. G., Cevher, V., Duarte, M. F., and Hegde, C., Model-based compressive sensing. IEEE Trans. Inf. Theory 56(4):1982–2001, 2010.

    Article  Google Scholar 

  23. Mallat, S. G., and Zhang, Z., Matching pursuits with time-frequency dictionaries. Signal Process. IEEE Trans. 41(12):3397–3415, 1993.

    Article  Google Scholar 

  24. Pope, G., Compressive sensing: A summary of reconstruction algorithms, Master’s thesis, Eidgenössische Technische Hochschule, Zürich, Department of Computer Science, 2009.

  25. Tropp, J. A., and Gilbert, A. C., Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12):4655–4666, 2007.

    Article  Google Scholar 

  26. Needell, D., and Tropp, J. A., Cosamp: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal. 26(3):301–321, 2009.

    Article  Google Scholar 

  27. Blumensath, T., and Davies, M. E., Iterative hard thresholding for compressed sensing. Appl. Comput. Harmon. Anal. 27(3):265–274, 2009.

    Article  Google Scholar 

  28. Blumensath, T., Accelerated iterative hard thresholding. Signal Process. 92(3):752–756, 2012.

    Article  Google Scholar 

  29. van den Berg, E., Convex optimization for generalized sparse recovery, PhD thesis, The University of British Columbia, Department of Computer Science, 2009.

  30. Li, X., and Luo, S., A compressed sensing-based iterative algorithm for ct reconstruction and its possible application to phase contrast imaging. Biomed. Eng. OnLine 10(73):1–14, 2011.

    Article  Google Scholar 

  31. Burns, A., Doheny, E.P., Greene, B.R., Foran, T., Leahy, D., O’Donovan, K., McGrath, M.J., ShimmerTM: an extensible platform for physiological signal capture, In: Proceedings of Annual International Conference of IEEE EMBC 2010, pp. 3759–3762, 2010.

  32. Gaxiola-Sosa, J.E., Mohsin, N., Palliyali, A.J., Tafreshi, R., Entesari, K., A portable 12-lead ECG wireless medical system for continuous cardiac-activity monitoring, In: Proceedings of MECBME 2014, pp. 123–126, 2014.

  33. Gao, H., Duan, X., Guo, X., Huang, A., Jiao, B., Design and tests of a smartphones-based multi-lead ECG monitoring system, In: Proceedings of Annual International Conference of IEEE EMBC 2013, pp. 2267–2270, 2013.

  34. Tan, T.-H., Chang, C.-S., Huang, Y.-F., Chen, Y.-F., and Lee, C., Development of a portable linux-based ECG measurement and monitoring system. J. Med. Syst. 35(4):559–569, 2011.

    Article  PubMed  Google Scholar 

  35. Winokur, E. S., Delano, M. K., and Sodini, C. G., A wearable cardiac monitor for long-term data acquisition and analysis. IEEE Trans. Biomed. Eng. 60(1):189–192, 2013.

    Article  PubMed  Google Scholar 

  36. Gomez-Clapers, J., and Casanella, R., A fast and easy-to-use ECG acquisition and heart rate monitoring system using a wireless steering wheel. Sensors J. IEEE 12(3):610–616, 2012.

    Article  Google Scholar 

  37. Fensli, R., Dale, J., O’Reilly, P., O’Donoghue, J., Sammon, D., and Gundersen, T., Towards improved healthcare performance: examining technological possibilities and patient satisfaction with wireless body area networks. J. Med. Syst. 34(4):767–775, 2010.

    Article  PubMed  Google Scholar 

  38. Polania, L. F., Carrillo, R. E., Blanco-Velasco, M., and Barner, K. E., Exploiting prior knowledge in compressed sensing wireless ECG systems. Biomed. Health Inf. IEEE J. 19(2):508–519, 2015.

    Article  Google Scholar 

  39. Mamaghanian, H., Khaled, N., Atienza, D., and Vandergheynst, P., Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. Biomed. Eng. IEEE Trans. 58(9):2456–2466, 2011.

    Article  Google Scholar 

  40. Liu, B., Zhang, Z., Xu, G., Fan, H., and Fu, Q., Energy efficient telemonitoring of physiological signals via compressed sensing: A fast algorithm and power consumption evaluation. Biomed. Signal Process. Control 11:80–88, 2014.

    Article  CAS  Google Scholar 

  41. Fauvel, S., and Ward, R. K., An energy efficient compressed sensing framework for the compression of electroencephalogram signals. Sensors 14(1):1474–1496, 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Zhang, Z., Jung, T.-P., Makeig, S., and Rao, B. D., Compressed sensing for energy-efficient wireless Telemonitoring of noninvasive fetal ECG via block sparse bayesian learning. Biomed. Eng. IEEE Trans. 60(2):300–309, 2013.

    Article  CAS  Google Scholar 

  43. Pant, J. K., and Krishnan, S., Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning. Biomed. Circ. Syst. IEEE Trans. 8(2):293–302, 2014.

    Article  Google Scholar 

  44. Cho, G. Y., Lee, S. J., and Lee, T. R., An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems. J. Med. Syst. 39(161):1–8, 2015.

    Google Scholar 

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Authors and Affiliations

  1. Chair of Integrated Analog Circuits and RF Systems, RWTH Aachen University, D-52062, Aachen, Germany

    Yishan Wang, Sammy Doleschel, Ralf Wunderlich & Stefan Heinen

Authors
  1. Yishan Wang
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  2. Sammy Doleschel
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  3. Ralf Wunderlich
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  4. Stefan Heinen
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Correspondence to Yishan Wang.

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This article is part of the Topical Collection on Mobile Systems

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Wang, Y., Doleschel, S., Wunderlich, R. et al. Evaluation of Digital Compressed Sensing for Real-Time Wireless ECG System with Bluetooth low Energy. J Med Syst 40, 170 (2016). https://doi.org/10.1007/s10916-016-0526-1

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  • DOI: https://doi.org/10.1007/s10916-016-0526-1

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