Skip to main content

Advertisement

Springer Nature Link
Log in

Efficient compression technique for reducing transmitted EEG data without loss in IoMT networks based on fog computing

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The rapid development in medical devices and communication technologies led to the emergence of the Internet of Medical Things (IoMT), resulting in several new applications that connect to healthcare IT systems through online computer networks. A vast quantity of data produced by these applications will be received at the edge gateway periodically to transmit them to the remote cloud for further handling. However, sending this huge data to the cloud across the IoT network will place a significant burden on the IoT network. The long processing delays and exchanged data have a considerable influence on the real-time IoT applications response time. The responsiveness time of these applications will be decreased. Therefore, the IoT applications exploit the advantages of fog computing, which serves as a middle layer between the platform of cloud and IoT devices to minimize the transmitted data and enhance the response time. In this paper, we propose an efficient compression technique (ECoT) for reducing transmitted Electroencephalography (EEG) data without loss on the IoMT Networks based on Fog Computing. The ECoT combines three efficient data reduction techniques: DBSCAN clustering, Delta encoding, and Huffman encoding, to decrease the volume of data in the Fog node then sending it to the platform of cloud. First, the DBSCAN clusters the EEG data into clusters. Then, the Delta encoding is applied to the indices of EEG data in each cluster. Finally, the Huffman encoding encodes the vector of differences for each cluster. The encoded data from clusters is combined into a file to be sent to the platform of cloud. The results show that the ECoT technique introduced improved results in terms of compression ratio, sent data, compression power, and compression and decompression times compared with other methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 12

Similar content being viewed by others

Data availability

The data that support the findings of this study are openly available in the EEG data recordings of the Bonn University dataset at reference number [10].

References

  1. Al-Turjman F, Nawaz MH, Ulusar UD (2020) Intelligence in the internet of medical things era: a systematic review of current and future trends. Comput Commun 150:644–660

    Article  Google Scholar 

  2. Sara KI, Ali KI (2021) New fog computing enabled lossless EEG data compression scheme in iot networks. J Ambient Intell Hum Comput 1–14

  3. Papageorgiou A, Cheng B, Kovacs E (2015) Real-time data reduction at the network edge of internet-of-things systems. In: 2015 11th International Conference on Network and Service Management (CNSM), pp 284–291

  4. La QD, Ngo MV, Dinh TQ, Quek TQS, Shin H (2019) Enabling intelligence in fog computing to achieve energy and latency reduction. Digit Commun Netw 5(1):3–9

    Article  Google Scholar 

  5. Idrees AK, Idrees SK, Couturier R, Ali-Yahiya T (2022) An edge-fog computing enabled lossless EEG data compression with epileptic seizure detection in iomt networks. IEEE Intern Things J

  6. Feng G, Jiang G, Li Z, Wang X (2016) Prognostic value of electroencephalography (EEG) for brain injury after cardiopulmonary resuscitation. Neurol Sci 37(6):843–849

    Article  Google Scholar 

  7. Boylan GB, Kharoshankaya L, Wusthoff CJ (2015) Seizures and hypothermia: importance of electroencephalographic monitoring and considerations for treatment. Semin Fetal Neonatal Med 20:103–108

    Article  Google Scholar 

  8. Jaber AS, Idrees AK (2021) Energy-saving multisensor data sampling and fusion with decision-making for monitoring health risk using wbsns. Softw Pract Exp 51(2):271–293

    Article  Google Scholar 

  9. Abdellatif AA, Emam A, Chiasserini C-F, Mohamed A, Jaoua A, Ward R (2019) Edge-based compression and classification for smart healthcare systems: concept, implementation and evaluation. Exp Syst Appl 117:1–14

    Article  Google Scholar 

  10. Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E 64(6):061907

    Article  Google Scholar 

  11. Srinivasan K, Dauwels J, Ramasubba RM (2011) A two-dimensional approach for lossless EEG compression. Biomed Signal Process Control 6(4):387–394

    Article  Google Scholar 

  12. Srinivasan K, Ramasubba M (2010) Efficient preprocessing technique for real-time lossless EEG compression. Electron Lett 46(1):26–27

    Article  Google Scholar 

  13. Al-Nassrawy KK, Al-Shammary D, Idrees AK (2020) High performance fractal compression for EEG health network traffic. Proc Comput Sci 167:1240–1249

    Article  Google Scholar 

  14. Hejrati B, Fathi A, Abdali-Mohammadi F (2017) Efficient lossless multi-channel EEG compression based on channel clustering. Biomed Signal Process Control 31:295–300

    Article  Google Scholar 

  15. Karimu RY, Azadi S (2016) Lossless EEG compression using the dct and the Huffman coding. J Sci Ind Res 75:615–620

    Google Scholar 

  16. Maazouz M, Tchoktck KS, Bengherbia B, Toubal A, Batel N, Bahri N (2015) A dct-based algorithm for multi-channel near-lossless EEG compression. In: 2015 4th International Conference on Electrical Engineering (ICEE), pp 1–5

  17. Rajasekar P, Pushpalatha M (2020) Huffman quantization approach for optimized EEG signal compression with transformation technique. Soft Comput 24(19):14545–14559

    Article  Google Scholar 

  18. Dao PT, Li XJ, Do HN (2015) Lossy compression techniques for EEG signals. In: 2015 International Conference on Advanced Technologies for Communications (ATC), pp 154–159

  19. Titus G, Sudhakar MS (2020) A simple but efficient EEG data compression algorithm for neuromorphic applications. IETE J Res 66(3):303–314

    Article  Google Scholar 

  20. Birvinskas D, Jusas V, Martisius I, Damasevicius R (2015) Fast DCT algorithms for EEG data compression in embedded systems. Comput Sci Inf Syst 12(1):49–62

    Article  Google Scholar 

  21. Alsenwi M, Ismail T, Mostafa H (2016) Performance analysis of hybrid lossy/lossless compression techniques for EEG data. In: 2016 28th International Conference on Microelectronics (ICM), pp 1–4

  22. Alsenwi M, Saeed M, Ismail T, Mostafa H, Gabran S (2017) Hybrid compression technique with data segmentation for electroencephalography data. In: 2017 29th International Conference on Microelectronics (ICM), pp 1–4

  23. Campobello G, Gugliandolo G, Donato N (2021) A simple and efficient near-lossless compression algorithm for multichannel EEG systems. In: 2021 29th European Signal Processing Conference (EUSIPCO), pp 1150–1154

  24. Das S, Kyal C (2021) Efficient multichannel EEG compression by optimal tensor truncation. Biomed Signal Process Control 68:102749

    Article  Google Scholar 

  25. Fragkou Aikaterini, Kakarountas Athanasios, Kokkinos Vasileios (2022) Low power EEG data encoding for brain neurostimulation implants. Information 13(4):194

    Article  Google Scholar 

  26. Chakraborty P, Chandrapragasam T (2022) Extended applications of compressed sensing algorithm in biomedical signal and image compression. J Inst Eng India Ser B 103(1):83–91

    Article  Google Scholar 

  27. Kunabeva R, Vinutha LB, Manjunatha P (2022) In-node adaptive compressive sensing technique for EEG signal in wban. Data Intell Cogn Inf 705–719

  28. Narendra SK, Kavitha C, Ramesh G, Subimal D (2019) A health monitoring system for vital signs using IoT. Intern Things 5:116–129

    Article  Google Scholar 

  29. Gia TN, Jiang M, Rahmani A-M, Westerlund T, Liljeberg P, Tenhunen H (2015) Fog computing in healthcare internet of things: a case study on ECG feature extraction. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp 356–363

  30. Sauravjoyti S, Bhattacharyya Dhruba K (2010) An effective technique for clustering incremental gene expression data. IJCSI Int J Comput Sci Issues 7(3):31–41

    Google Scholar 

  31. Smith SW et al (2009) The scientist and engineer’s guide to digital signal processing, 1999

  32. Jade F, David JMN, Regina B, Fernanda C, Victor S, De Aguiar L (2021) Ontology-based data integration for the internet of things in a scientific software ecosystem. Int J Comput Appl Technol 67(2–3):252–262

    Google Scholar 

  33. Singh D, Thakur A, Singh M, Sandhu A (2021) Iot implementation strategies amid covid-19 pandemic. Int J Comput Appl Technol 65(4):389–398

    Article  Google Scholar 

  34. Yu T, Wang X, Shami A (2017) A novel fog computing enabled temporal data reduction scheme in iot systems. In: GLOBECOM 2017-2017 IEEE Global Communications Conference, pp 1–5

  35. Han D, Agrawal A, Liao W-K, Choudhary A (2016) A novel scalable dbscan algorithm with spark. In: 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp 1393–1402

  36. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220

    Article  Google Scholar 

  37. Hadi HA, George LE, Hassan EK (2021) Lossless EEG data compression using delta modulation and two types of enhanced adaptive shift coders. In: International Conference on New Trends in Information and Communications Technology Applications, Springer, pp 87–98

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Information Security, College of Information Technology, University of Babylon, Hillah, Iraq

    Ali Kadhum Idrees & Marwa Saieed Khlief

Authors
  1. Ali Kadhum Idrees
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Marwa Saieed Khlief
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

AKI developed the model and performed experiments. MSK wrote the some part of the manuscript. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Ali Kadhum Idrees.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest. They have no competing interests to declare that are relevant to the content of this article.

Consent for publication

Not applicable.

Consent to participate

Not applicable.

Ethical approval

This work does not contain any studies with human participants or animals performed by any of the authors.

Human or animal rights

Not applicable.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Idrees, A.K., Khlief, M.S. Efficient compression technique for reducing transmitted EEG data without loss in IoMT networks based on fog computing. J Supercomput 79, 9047–9072 (2023). https://doi.org/10.1007/s11227-022-05027-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-05027-9

Keywords