Skip to main content
SpringerLink
Log in

FPGA Design of an Efficient EEG Signal Transmission Through 5G Wireless Network Using Optimized Pilot Based Channel Estimation: A Telemedicine Application

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Electroencephalogram (EEG) signifies a neurophysiologic measurement, which perceives the electrical activity of brain via making a record of EEG signal from the electrodes positioned on the scalp. With the progression of wired and wireless technologies both m-healthcare and e-healthcare turn into an essential fragment of biomedical science. Mixing of EEG signal with some other biological signal is referred as artifacts. Removal of artifacts postures an abundant challenge in the medical field. In this paper, Hybrid multi resolution discrete wavelet transform based delayed error normalized least mean square is proposed to eradicate motion artifact from the recorded EEG signal. After filtering process Encryption and Encoding take place with chaos encryption and Turbo encoder. Encryption is the process of scrambling the plain EEG signal in to Chipper format. Telemedicine system can be used to transmit medical data transmission and it requires an optimal channel estimation method (ESSA) to reduce BERs. The main aim of ESSA algorithm is to optimally place the pilot symbols and in-order to enable the automatic estimation of state of the channel. Channel estimation is facilitated through GFDM-IM modulation approach and the estimation can be done through SVD-LMMSE module. The proposed optimal based channel estimation is simulated under Xilinx platform with Verilog coding. Then, the performance of the proposed method will be analysed in terms of BER, area and frequency.

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

Data Availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Code Availability

Custom code.

References

  1. Ahmed, S. T., Sandhya, M., & Sankar, S. (2019). An optimized RTSRV machine learning algorithm for biomedical signal transmission and regeneration for telemedicine environment. Procedia Computer Science, 152, 140–149.

    Article  Google Scholar 

  2. Aggarwal, G., Dai, X., Binns, R., & Saatchi, R. (2019). Real-time wireless healthcare system for angular transmission of EEG signal using VL-OCC. Procedia Computer Science, 152, 28–35.

    Article  Google Scholar 

  3. Tamilarasi, K., & Jawahar, A. (2020). Medical data security for healthcare applications using hybrid lightweight encryption and swarm optimization algorithm. Wireless Personal Communications, 114(3), 1865.

    Article  Google Scholar 

  4. Shahzadi, R., Anwar, S. M., Qamar, F., Ali, M., Rodrigues, J. J. P. C., & Alnowami, M. (2019). Secure EEG signal transmission for remote health monitoring using optical chaos. IEEE Access, 7, 57769–57778.

    Article  Google Scholar 

  5. Maddirala, A. K., & Shaik, R. A. (2016). Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis. Biomedical Signal Processing and Control, 30, 79–85.

    Article  Google Scholar 

  6. Maddirala, A. K., & Shaik, R. A. (2016). Removal of EOG artifacts from single channel EEG signals using combined singular spectrum analysis and adaptive noise canceler. IEEE Sensors Journal, 16(23), 8279–8287.

    Google Scholar 

  7. Kher, R., & Gandhi, R. (2016). Adaptive filtering based artifact removal from electroencephalogram (EEG) signals. In 2016 International conference on communication and signal processing (ICCSP), IEEE, pp. 0561–0564.

  8. Yang, B., Duan, K., & Zhang, T. (2016). Removal of EOG artifacts from EEG using a cascade of sparse autoencoder and recursive least squares adaptive filter. Neurocomputing, 214, 1053–1060.

    Article  Google Scholar 

  9. Alickovic, E., Kevric, J., & Subasi, A. (2018). Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomedical Signal Processing and Control, 39, 94–102.

    Article  Google Scholar 

  10. Mishra, A., Bhateja, V., Gupta, A., & Mishra, A. (2019). Noise removal in EEG signals using SWT–ICA combinational approach. In S. C. Satapathy, V. Bhateja, & S. Das (Eds.), Smart intelligent computing and applications (pp. 217–224). Springer.

    Chapter  Google Scholar 

  11. Prema, P., Kesavamurthy, T., & Ramadoss, K. (2018). Ocular artifact suppression in single trial EEG using DWT-combined ANC. In M. C. Bhuvaneswari & J. Saxena (Eds.), Intelligent and efficient electrical systems (pp. 225–230). Springer.

    Chapter  Google Scholar 

  12. Patel, R., Janawadkar, M. P. R., Sengottuvel, S., Gireesan, K., & Radhakrishnan, T. S. (2016). Suppression of eye-blink associated artifact using single channel EEG data by combining cross-correlation with empirical mode decomposition. IEEE Sensors Journal, 16(18), 6947–6954.

    Article  Google Scholar 

  13. Patel, R., Sengottuvel, S., Janawadkar, M. P., Gireesan, K., Radhakrishnan, T. S., & Mariyappa, N. (2016). Ocular artifact suppression from EEG using ensemble empirical mode decomposition with principal component analysis. Computers & Electrical Engineering, 54, 78–86.

    Article  Google Scholar 

  14. Runnova, A. E., Grubov, V. V., Khramova, M. V., & Hramov, A. E. (2017). Dealing with noise and physiological artifacts in human EEG recordings: empirical mode methods. In Saratov Fall Meeting 2016: Laser Physics and Photonics XVII; and Computational Biophysics and Analysis of Biomedical Data III, International Society for Optics and Photonics, vol. 10337, p. 1033712.

  15. Basnet, A., Alsadoon, A., Prasad, P. W. C., Alsadoon, O. H., Pham, L., & Elchouemi, A. (2019). A novel secure patient data transmission through wireless body area network: Health tele-monitoring. International Journal of Communication Networks and Information Security, 11(1), 93–104.

    Google Scholar 

  16. Dey, N., Ashour, A. S., Shi, F., Fong, S. J., & Sherratt, R. S. (2017). Developing residential wireless sensor networks for ECG healthcare monitoring. IEEE Transactions on Consumer Electronics, 63(4), 442–449.

    Article  Google Scholar 

  17. Haddad, O., & Khalighi, M. A. (2019). Enabling communication technologies for medical wireless body-area networks. In 2019 Global LIFI Congress (GLC), IEEE, pp. 1–5.

  18. Zang, W., Miao, F., Gravina, R., Sun, F., Fortino, G., & Li, Y. (2020). CMDP-based intelligent transmission for wireless body area network in remote health monitoring. Neural Computing and Applications, 32(3), 829–837.

    Article  Google Scholar 

  19. Sodhro, A. H., & Shah, M. A. (2017). Role of 5G in medical health. In 2017 International conference on innovations in electrical engineering and computational technologies (ICIEECT), IEEE, pp. 1–5.

  20. Anwar, S., & Prasad, R. (2018). Framework for future telemedicine planning and infrastructure using 5G technology. Wireless Personal Communications, 100(1), 193–208.

    Article  Google Scholar 

  21. Öztürk, E., Basar, E., & Çırpan, H. A. (2016). Spatial modulation GFDM: A low complexity MIMO-GFDM system for 5G wireless networks. In 2016 IEEE international black sea conference on communications and networking (BlackSeaCom), IEEE, pp. 1–5.

  22. Roy, V., & Shukla, S. (2019). Designing efficient blind source separation methods for EEG motion artifact removal based on statistical evaluation. Wireless Personal Communications, 108(3), 1311–1327.

    Article  Google Scholar 

  23. Satheeskumaran, S., & Sabrigiriraj, M. (2014). A new LMS based noise removal and DWT based R-peak detection in ECG signal for biotelemetry applications. National Academy Science Letters, 37(4), 341–349.

    Article  Google Scholar 

  24. Venkatesan, C., & Karthigaikumar, P. (2018). An efficient noise removal technique using modified error normalized LMS algorithm. National Academy Science Letters, 41(3), 155–159.

    Article  Google Scholar 

  25. Venkatesan, C., Karthigaikumar, P., & Varatharajan, R. (2019). FPGA implementation of modified error normalized LMS adaptive filter for ECG noise removal. Cluster Computing, 22(5), 12233–12241.

    Article  Google Scholar 

  26. Abdellatif, A. A., Khafagy, M. G., Mohamed, A., & Chiasserini, C.-F. (2018). EEG-based transceiver design with data decomposition for healthcare IoT applications. IEEE Internet of Things Journal, 5(5), 3569–3579.

    Article  Google Scholar 

  27. Hejrati, B., Fathi, A., & Abdali-Mohammadi, F. (2017). Efficient lossless multi-channel EEG compression based on channel clustering. Biomedical Signal Processing and Control, 31, 295–300.

    Article  Google Scholar 

  28. Nguefack, L. T., Pauné, F., Kenfack, G. W., & Mbihi, J. (2020). A novel optical fiber transmission system using duty-cycle modulation and application to ECG signal: Analog design and simulation. Journal of Electrical Engineering, Electronics, Control and Computer Science, 6(3), 39–48.

    Google Scholar 

  29. Dhatchayeny, D. R., Sewaiwar, A., Tiwari, S. V., & Chung, Y. H. (2015). EEG biomedical signal transmission using visible light communication. In 2015 International conference on industrial instrumentation and control (ICIC), IEEE, pp. 243–246.

  30. Devi, M. R., Ramanjaneyulu, K., & Krishna, B. T. (2019). Performance analysis of sub interleaver for turbo coded OFDM system. Journal of Mechanics of continua and Mathematical Sciences, 14(1), 469–488.

  31. Wong, C.-C., Lai, M.-W., Lin, C.-C., Chang, H.-C., & Lee, C.-Y. (2010). Turbo decoder using contention-free interleaver and parallel architecture. IEEE Journal of Solid-State Circuits, 45(2), 422–432.

    Article  Google Scholar 

  32. Jayashri, R., Sujatha, S., & Dananjayan, P. (2017). PAPR reduction for OFDM systems using DCT and helical interleaver in modified PTS technique. In 2017 International conference on communication and signal processing (ICCSP), IEEE, pp. 0083–0086.

  33. Sohail, M. F., Ghauri, S. A., & Alam, S. (2017). Channel estimation in massive mimo systems using heuristic approach. Wireless Personal Communications, 97(4), 6483–6498.

    Article  Google Scholar 

  34. Raghunatharao, D., Prasad, T. J., & Prasad, M. N. G. (2020). Optimal pilot-based channel estimation in cognitive radio. Wireless Personal Communications, 114(4), 2801–2819.

    Article  Google Scholar 

  35. Nie, Y., Yu, X., & Yang, Z. (2019). Deterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM system. EURASIP Journal on Wireless Communications and Networking, 2019(1), 1–8.

    Article  Google Scholar 

  36. Seyman, M. N., & Taşpinar, N. (2011). Particle swarm optimization for pilot tones design in MIMO-OFDM systems. EURASIP Journal on Advances in Signal Processing, 2011(1), 10.

    Article  Google Scholar 

  37. Jain, M., Singh, V., & Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and Evolutionary Computation, 44, 148–175.

    Article  Google Scholar 

Download references

Funding

No funding.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Malnad College of Engineering, Hassan, Karnataka, 573201, India

    K. B. Santhosh Kumar & B. R. Sujatha

Authors
  1. K. B. Santhosh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. R. Sujatha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. B. Santhosh Kumar.

Ethics declarations

Conflict of interest

Santhosh Kumar K B and B.R. Sujatha have declared that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, K.B.S., Sujatha, B.R. FPGA Design of an Efficient EEG Signal Transmission Through 5G Wireless Network Using Optimized Pilot Based Channel Estimation: A Telemedicine Application. Wireless Pers Commun 123, 3597–3621 (2022). https://doi.org/10.1007/s11277-021-09305-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09305-2

Keywords

Search

Navigation