Skip to main content
SpringerLink
Log in

IoT and cloud computing-based automated epileptic seizure detection using optimized Siamese convolutional sparse autoencoder network

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Epilepsy, a highly prevalent neurological disorder, profoundly impacts the lives of patients with periodic and unexpected seizures that lead to serious injury or death. This research work introduces a Siamese Convolutional Fire Hawk Sparse Autoencoder Network (SCFHSAN) to detect different states of the epileptic seizures. Initially, the EEG signals are gathered from two datasets including TUH EEG and UoB datasets. Then, artifacts removal is performed by multi-resolutional analysis and adaptive filtering technique. After that, dandelion tunable Q-wavelet transform is used to decompose signals into frequency sub-bands. Following that, the feature extraction and feature selection are processed using several techniques. Finally, Siamese convolutional sparse autoencoder network is proposed for epileptic seizure detection and fire hawk optimization algorithm is employed to optimize the weight parameter of the network. The results indicate that the introduced approach achieves an accuracy 99.95% and 98.78% on UoB and TUH EEG datasets, respectively. Analysis determines that the introduced SCFHSAN scheme would help neuro-experts in diagnosing epileptic behavior by analyzing seizure details from various brain regions using multichannel EEG signals.

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 is not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Singh, K., Malhotra, J.: IOT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification. J. Ambient Intell. Hum. Comput. 14, 15497–15512 (2019)

    Article  Google Scholar 

  2. Hassan, A.R., Subasi, A., Zhang, Y.: Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise. Knowl. Based Syst. 191, 105333 (2020)

    Article  Google Scholar 

  3. Aayesha, Qureshi, M.B., Afzaal, M., Qureshi, M.S., Fayaz, M.: Machine learning-based EEG signals classification model for epileptic seizure detection. Multimed. Tools Appl. 80, 17849–17877 (2021)

    Article  Google Scholar 

  4. Hossain, M.S., Amin, S.U., Alsulaiman, M., Muhammad, G.: Applying deep learning for epilepsy seizure detection and brain mapping visualization. ACM Trans. Multimed. Comput. 15, 1–17 (2019)

    Article  Google Scholar 

  5. Akyol, K.: Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection. Expert Syst. Appl. 148, 113239 (2020)

    Article  Google Scholar 

  6. Baghersalimi, S., Teijeiro, T., Atienza, D., Aminifar, A.: Personalized real-time federated learning for epileptic seizure detection. IEEE. J. Biomed. Health Inf. 26, 898–909 (2021)

    Article  Google Scholar 

  7. Chen, Z., Lu, G., Xie, Z., Shang, W.: A unified framework and method for EEG-based early epileptic seizure detection and epilepsy diagnosis. IEEE Access 8, 20080–20092 (2020)

    Article  Google Scholar 

  8. Tian, X., Deng, Z., Ying, W., Choi, K.-S., Wu, D., Qin, B., Wang, J., Shen, H., Wang, S.: Deep multi-view feature learning for EEG-based epileptic seizure detection. IEEE Trans. Neural Syst. Rehabilit. Eng. 27, 1962–1972 (2019)

    Article  Google Scholar 

  9. Radman, M., Moradi, M., Chaibakhsh, A., Kordestani, M., Saif, M.: Multi-feature fusion approach for epileptic seizure detection from EEG signals. IEEE Sens. J. 21, 3533–3543 (2020)

    Article  Google Scholar 

  10. Thara, D.K., PremaSudha, B.G., Xiong, F.: Epileptic seizure detection and prediction using stacked bidirectional long short term memory. Pattern Recognit. Lett. 128, 529–535 (2019)

    Article  Google Scholar 

  11. Geng, M., Zhou, W., Liu, G., Li, C., Zhang, Y.: Epileptic seizure detection based on stockwell transform and bidirectional long short-term memory. IEEE Trans. Neur. Syst. Rehabilit. 28, 573–580 (2020)

    Article  Google Scholar 

  12. Sun, C., Cui, H., Zhou, W., Nie, W., Wang, X., Yuan, Q.: Epileptic seizure detection with EEG textural features and imbalanced classification based on EasyEnsemble learning. Int. J. Neural Syst. 29, 1950021 (2019)

    Article  Google Scholar 

  13. Jiang, Y., Chen, W., Li, M.: Symplectic geometry decomposition-based features for automatic epileptic seizure detection. Comput. Biol. Med. 116, 103549 (2020)

    Article  Google Scholar 

  14. Forooghifar, F., Aminifar, A., Cammoun, L., Wisniewski, I., Ciumas, C., Ryvlin, P., Atienza, D.: A self-aware epilepsy monitoring system for real-time epileptic seizure detection. Mob. Netw. Appl. 27, 677–690 (2019)

    Article  Google Scholar 

  15. Djoufack Nkengfack, L.C., Tchiotsop, D., Atangana, R., Louis-Door, V., Wolf, D.: EEG signals analysis for epileptic seizures detection using polynomial transforms, linear discriminant analysis and support vector machines. Biomed. Signal Process. 62, 102141 (2020)

    Article  Google Scholar 

  16. Bhandari, V., Manjaiah, D.H.: Improved ensemble learning model with optimal feature selection for automated epileptic seizure detection. Comput. Method Biomech. Biomed. Eng. 11, 135–165 (2023)

    Google Scholar 

  17. Chakrabarti, S., Swetapadma, A., Ranjan, A., Pattnaik, P.K.: Time domain implementation of pediatric epileptic seizure detection system for enhancing the performance of detection and easy monitoring of pediatric patients. Biomed. Signal Process. Control 59, 101930 (2020)

    Article  Google Scholar 

  18. Bouaziz, B., Chaari, L., Batatia, H., Quintero-Rincón, A.: Epileptic seizure detection using a convolutional neural network. In: Chaari, L. (ed.) Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine, pp. 79–86. Springer International Publishing, Berlin (2019)

    Chapter  Google Scholar 

  19. Nkengfack, L.C.D., Tchiotsop, D., Atangana, R., Louis-Door, V., Wolf, D.: Classification of EEG signals for epileptic seizures detection and eye states identification using Jacobi polynomial transforms-based measures of complexity and least-square support vector machine. Inf. Med. Unlocked 23, 100536 (2021)

    Article  Google Scholar 

  20. Bari, M.F., Fattah, S.A.: Epileptic seizure detection in EEG signals using normalized IMFs in CEEMDAN domain and quadratic discriminant classifier. Biomed. Signal Process. 58, 101833 (2020)

    Article  Google Scholar 

  21. Yedurkar, D.P., Metkar, S., Al-Turjman, F., Yardi, N., Stephan, T.: An IOT based novel hybrid seizure detection approach for epileptic monitoring. IEEE Trans. Ind. Inform. (2023). https://doi.org/10.1109/TII.2023.3274913

    Article  Google Scholar 

  22. Anter, A.M., Abd Elaziz, M., Zhang, Z.: Real-time epileptic seizure recognition using Bayesian genetic whale optimizer and adaptive machine learning. Future Gener. Comput. Syst. 127, 426–434 (2022)

    Article  Google Scholar 

  23. Idrees, A.K., Idrees, S.K., Couturier, R., Ali-Yahiya, T.: An edge-fog computing-enabled lossless EEG data compression with epileptic seizure detection in IOMT Networks. IEEE Internet Things 9, 13327–13337 (2022)

    Article  Google Scholar 

  24. Yedurkar, D.P., Metkar, S.P., Al-Turjman, F., Stephan, T., Kolhar, M., Altrjman, C.: A novel approach for multichannel epileptic seizure classification based on internet of things framework using critical spectral verge feature derived from flower pollination algorithm. Sensors 22, 9302 (2022)

    Article  Google Scholar 

  25. Malekzadeh, A., Zare, A., Yaghoobi, M., Alizadehsani, R.: Automatic diagnosis of epileptic seizures in EEG signals using fractal dimension features and convolutional autoencoder method. Big Data Cognit. Comput. 5, 78 (2021)

    Article  Google Scholar 

  26. Singh, K., Malhotra, J.: Deep Learning based smart health monitoring for automated prediction of epileptic seizures using spectral analysis of SCALP EEG. Phys. Eng. Sci. Med. 44, 1161–1173 (2021)

    Article  Google Scholar 

  27. Yedurkar, D.P., Metkar, S.P.: Multiresolution approach for artifacts removal and localization of seizure onset zone in epileptic EEG signal. Biomed. Signal Process. 57, 101794 (2020)

    Article  Google Scholar 

  28. Pal, H.S., Kumar, A., Vishwakarma, A., Ahirwal, M.K.: Electrocardiogram signal compression using tunable-Q wavelet transform and meta-heuristic optimization techniques. Biomed. Signal Process. 78, 103932 (2022)

    Article  Google Scholar 

  29. Zhao, S., Zhang, T., Ma, S., Chen, M.: Dandelion optimizer: a nature-inspired metaheuristic algorithm for engineering applications. Eng. Appl. Artif. Intell. 114, 105075 (2022)

    Article  Google Scholar 

  30. Zhang, K., Song, J., Yu, Y., Du, S.: Incomplete multi-view clustering based on weighted adaptive graph learning. In 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) (ICSP) (2022)

  31. Azizi, M., Talatahari, S., Gandomi, A.H.: Fire Hawk optimizer: a novel metaheuristic algorithm. Artif. Intell. Rev. 56, 287–363 (2023)

    Article  Google Scholar 

  32. https://www.upf.edu/web/ntsa/downloads/-/asset_publisher/xvT6E4pczrBw/content/2001-indications-of-nonlinear-deterministic-and-finite-dimensional-structures-in-time-series-of-brain-electrical-activity-dependence-on-recording-regi

  33. https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml

  34. Singh, R., Ahmed, T., Kumar Singh, A., Chanak, P., Singh, S.K.: Seizsclas: an efficient and secure internet-of-things-based EEG classifier. IEEE Internet Things 8, 6214–6221 (2020)

    Article  Google Scholar 

  35. Basri, A., Arif, M.: Classification of seizure types using random forest classifier. Adv. Sci. Technol. Res. J. 15, 167–178 (2021)

    Article  Google Scholar 

  36. Singh, K., Malhotra, J.: Smart neurocare approach for detection of epileptic seizures using deep learning based temporal analysis of EEG patterns. Multimed. Tools Appl. 81, 29555–29586 (2022)

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, 641008, India

    M. Ramkumar

  2. Department of Electronics and Communication Engineering, Karpagam Institute of Technology, Coimbatore, Tamil Nadu, 641021, India

    S. Syed Jamaesha & M. S. Gowtham

  3. Department of Computer Science and Engineering, Er Perumal Manimekalai College of Engineering, Hosur, Tamil Nadu, 635117, India

    C. Santhosh Kumar

Authors
  1. M. Ramkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Syed Jamaesha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. S. Gowtham
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. Santhosh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors have contributed equally to the work.

Corresponding author

Correspondence to M. Ramkumar.

Ethics declarations

Conflict of Interest

The authors declare that they have no potential conflict of interest.

Ethical approval

All applicable institutional and/or national guidelines for the care and use of animals were followed.

Informed consent

For this type of analysis, formal consent is not needed.

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

Ramkumar, M., Jamaesha, S.S., Gowtham, M.S. et al. IoT and cloud computing-based automated epileptic seizure detection using optimized Siamese convolutional sparse autoencoder network. SIViP 18, 3509–3525 (2024). https://doi.org/10.1007/s11760-024-03017-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-024-03017-3

Keywords

Search

Navigation