Skip to main content
SpringerLink
Log in

Design and Implementation of a Compressed Sensing Encoder: Application to EMG and ECG Wireless Biosensors

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Among the existing applications of wireless body sensor networks (WBSNs), a wearable health monitoring system (WHMS) is the most important. In a typical WHMS, miniature wireless biosensors, attached to or implanted in the human body, collect bio-signals such as the electrocardiogram (ECG), blood pressure or electromyogram (EMG) to provide real time and continuous health monitoring. In this paper, we present a compressed sensing (CS)-based approach to compress and recover the sensed bio-signals from the wireless biosensors of a WBSN. The CS encoding process has a low computational complexity and is suitable for use in power-constrained systems such as WHMS. We propose a simple deterministic measurement matrix, which is easy to implement in hardware. We design a digital CS encoder implementing the proposed measurement matrix and use it to compress the bio-signals in EMG and ECG wireless biosensors. The simulations and experimental results have shown that the EMG and ECG signals are compressed and recovered without perceptible loss if the compression ratios are, respectively, less than or equal to 75 and 87.5%. The obtained results have also confirmed the simplicity of the proposed measurement matrix since the CS encoder does not affect the memory usage or the processing time of the microcontrollers embedded in the wireless biosensors. Additionally, the CS encoder decreases by up to 75 and 87.5% the energy consumption of the transceivers for the EMG and ECG wireless biosensors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. M. Akhlaq, T. Sheltami, RTSP: an accurate and energy-efficient protocol for clock synchronization in WSNs. IEEE Trans. Instrum. Meas. 62(3), 578–589 (2013). doi:10.1109/TIM.2012.2232472

    Article  Google Scholar 

  2. A. Amini, F. Marvasti, Deterministic construction of binary, bipolar, and ternary compressed sensing matrices. IEEE Trans. Inf. Theory 57(4), 2360–2370 (2011). doi:10.1109/TIT.2011.2111670

    Article  MathSciNet  Google Scholar 

  3. A. Bandeira, E. Dobriban, D. Mixon, W. Sawin, Certifying the restricted isometry property is hard. IEEE Trans. Inf. Theory 59(6), 3448–3450 (2013). doi:10.1109/TIT.2013.2248414

    Article  MathSciNet  Google Scholar 

  4. R. Baraniuk, Compressive sensing (lecture notes). IEEE Signal Process. Mag. 24(4), 118–121 (2007). doi:10.1109/MSP.2007.4286571

    Article  Google Scholar 

  5. M. Ben-Romdhane, C. Rebai, P. Desgreys, A. Ghazel, P. Loumeau, Flexible baseband analog front-end for NUS based multistandard receiver. In: Joint IEEE North-East Workshop on Circuits and Systems and TAISA Conference, 2009. NEWCAS-TAISA ’09, pp 1–4, ). (2009). doi:10.1109/NEWCAS.2009.5290417

  6. E. Candes, T. Tao, Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. Inf. Theory 52(12), 5406–5425 (2006). doi:10.1109/TIT.2006.885507

    Article  MathSciNet  MATH  Google Scholar 

  7. E.J. Cands, The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique. 346(9), 589–592 (2008). http://www.sciencedirect.com/science/article/pii/S1631073X08000964

  8. F. Chen, A. Chandrakasan, V. Stojanovic, Design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors. IEEE J. Solid-State Circuits 47(3), 744–756 (2012a). doi:10.1109/JSSC.2011.2179451

    Article  Google Scholar 

  9. F. Chen, A. Chandrakasan, V. Stojanovic, Design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors. IEEE J. Solid-State Circuits 47(3), 744–756 (2012b). doi:10.1109/JSSC.2011.2179451

    Article  Google Scholar 

  10. YS. Chen, HY. Lin, HC. Chiu, HP. Ma, A compressive sensing framework for electromyogram and electroencephalogram. In: Medical Measurements and Applications (MeMeA), 2014 IEEE International Symposium on, pp 1–6, (2014). doi:10.1109/MeMeA.2014.6860096

  11. I. Ciocoiu, Foveated compressed sensing. Circuits, Systems, and Signal Processing pp 1–15, (2014). doi:10.1007/s00034-014-9878-2

  12. D. Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006). doi:10.1109/TIT.2006.871582

    Article  MathSciNet  MATH  Google Scholar 

  13. ZM. Du, FY. Ye, H. Shi, GP. Zhu, A fast recovery method of 2d geometric compressed sensing signal. Circuits, Systems, and Signal Processing pp 1–14, (2014) doi:10.1007/s00034-014-9913-3

  14. D. Gangopadhyay, E. Allstot, A. Dixon, K. Natarajan, S. Gupta, D. Allstot, Compressed sensing analog front-end for bio-sensor applications. IEEE J. Solid-State Circuits 49(2), 426–438 (2014). doi:10.1109/JSSC.2013.2284673

    Article  Google Scholar 

  15. Z. He, T. Ogawa, M. Haseyama, The simplest measurement matrix for compressed sensing of natural images. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp 4301–4304, (2010). doi:10.1109/ICIP.2010.5651800

  16. S. Imtiaz, A. Casson, E. Rodriguez-Villegas, Compression in wearable sensor nodes: Impacts of node topology. IEEE Tran. Biomed. Eng. 61(4), 1080–1090 (2014). doi:10.1109/TBME.2013.2293916

    Article  Google Scholar 

  17. H. Mamaghanian, N. Khaled, D. Atienza, P. Vandergheynst, Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE Trans. Biomed. Eng. 58(9), 2456–2466 (2011). doi:10.1109/TBME.2011.2156795

    Article  Google Scholar 

  18. NRF24L01 nRF24L01 - 2.4GHz RF - products - nordic semiconductor. (2014). http://www.nordicsemi.com/eng/Products/2.4GHz-RF/nRF24L01

  19. D. Oletic, M. Skrapec, V. Bilas, Prototype of respiratory sounds monitoring system based on compressive sampling. In: Zhang YT (ed) The International Conference on Health Informatics, no. 42 in IFMBE Proceedings, Springer International Publishing, pp 92–95, (2014). http://link.springer.com/chapter/10.1007/978-3-319-03005-0_24

  20. R. Pal, B. Gupta, N. Prasad, R. Prasad Efficient data processing in ultra low power wireless networks: Ideas from compressed sensing. In: 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies, 2009. ISABEL 2009, pp 1-2, (2009). doi:10.1109/ISABEL.2009.53736622

  21. A. Pantelopoulos, N. Bourbakis, A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40(1), 1–12 (2010). doi:10.1109/TSMCC.2009.2032660

    Article  Google Scholar 

  22. PhysioBank PhysioBank ATM, (2013). http://www.physionet.org/cgi-bin/atm/ATM

  23. T. Qiao, W. Li, B. Wu, A new algorithm based on linearized bregman iteration with generalized inverse for compressed sensing. Circuits, Systems, and Signal Processing 33(5), 1527–1539 (2014). doi:10.1007/s00034-013-9714-0

    Article  MathSciNet  Google Scholar 

  24. A. Ravelomanantsoa, H. Rabah, A. Rouane SystemC-AMS based virtual prototyping of wireless body sensor network using compressed sensing. In: 2013 25th International Conference on Microelectronics (ICM), pp 1–4 (2013). doi:10.1109/ICM.2013.6734992

  25. SystemC, SystemC-accellera systems initiative. (2012). http://www.accellera.org/downloads/standards/systemc

  26. Y. Tang, G. Lv, K. Yin, Deterministic sensing matrices based on multidimensional pseudo-random sequences. Circuits, Systems, and Signal Processing 33(5), 1597–1610 (2014). doi:10.1007/s00034-013-9701-5

    Article  Google Scholar 

  27. TEA (2014) TEA | Technologie Ergonomie Appliques. http://teaergo.com/drupal/en/tea

  28. J. Tropp, J. Laska, M. Duarte, J. Romberg, R. Baraniuk, Beyond Nyquist: Efficient sampling of sparse bandlimited signals. IEEE Trans. Inf. Theory 56(1), 520–544 (2010). doi:10.1109/TIT.2009.2034811

    Article  MathSciNet  Google Scholar 

  29. L. Zeng, X. Zhang, L. Chen, T. Cao, J. Yang, Deterministic construction of Toeplitzed structurally chaotic matrix for compressed sensing. Circuits, Systems, and Signal Processing pp 1–17, (2014) . doi:10.1007/s00034-014-9873-7

  30. H. Zörlein, F. Akram, M. Bossert, Dictionary adaptation in sparse recovery based on different types of coherence. CoRR abs/1307.3901 (2013)

Download references

Author information

Authors and Affiliations

  1. Institut Jean Lamour (IJL), UMR7198, Université de Lorraine, 54506, Vandoeuvre-Lès-Nancy, France

    Andrianiaina Ravelomanantsoa, Amar Rouane & Hassan Rabah

  2. Technopôle de Nancy-Brabois, TEA (Technology, Ergonomy, Applications), 3, rue du Bois Chêne le Loup, BP 210, Vandoeuvre-Lès-Nancy, France

    Nicolas Ferveur & Landry Collet

Authors
  1. Andrianiaina Ravelomanantsoa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amar Rouane
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hassan Rabah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nicolas Ferveur
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Landry Collet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrianiaina Ravelomanantsoa.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ravelomanantsoa, A., Rouane, A., Rabah, H. et al. Design and Implementation of a Compressed Sensing Encoder: Application to EMG and ECG Wireless Biosensors. Circuits Syst Signal Process 36, 2875–2892 (2017). https://doi.org/10.1007/s00034-016-0444-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-016-0444-y

Keywords

Search

Navigation