Skip to main content

Advertisement

SpringerLink
Log in

An epileptic seizures diagnosis system using feature selection, fuzzy temporal naive Bayes and T-CNN

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Today’s hospitals make use of state-of-the-art methods such as magnetic resonance imaging (MRI) and electroencephalogram (EEG) signal predictions in order to predict the occurrence of seizures well in advance. However, this method only works in theory. Almost every standard seizure prediction approach fails to predict epileptic seizures. Accurately, and most doctors mainly focus only on the treatment of epilepsy rather than preventing it. Health experts say that successive epileptic seizures would be even more virulent and fatal to individuals. The motivation of this research is to effectively predict and manage epileptic seizures disease by analyzing EEG signals. This work proposes a new Epileptic Seizures Diagnosis System (ESDS) for diagnosing epileptic seizures effectively. The proposed ESDS consists of two components, namely feature selection and classification. First, a newly proposed Fuzzy Temporal Naïve Bayes (FT-NB) classifier and the existing Convolutional Neural Network with Temporal Features (T-CNN) are proposed for performing effective data preprocessing and classification. Second, a GridSearchCV method is used to determine the best parameter for hyperparameter tuning to obtain the best performance in the FT-NB. In addition, the T-CNN is also applied for enhancing the prediction result further, and the different machine learning (ML) algorithms are considered for performing comparative analysis with the FT-NB in terms of disease prediction. The experiments have been conducted by using the standard dataset and proved as better than other systems in terms of Precision (87.5%), Recall (90.4%), Specificity (97.4%) and Accuracy (96.7%).

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

Similar content being viewed by others

Availability of data and material

Not Applicable.

Code availability

Not Applicable.

References

  1. Abdellatif A (2015) A new method for predicting epilepsy seizure. In: 2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG), Porto, pp 1–4

  2. Almustafa KM (2020) Classification of epileptic seizure dataset using different machine learning algorithms. Inform Med Unlocked 21, Article No.100444:1–7

    Article  Google Scholar 

  3. Antoniades A, Spyrou L, Took CC, Sanei S (2016) Deep learning for epileptic intracranial EEG data. In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Vietrisul Mare, pp 1–6

  4. Anugraha A, Vinotha E, Anusha R, Giridhar S, Narasimhan K (2017) A machine learning application for epileptic seizure detection. In: 2017 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, pp 1–4

  5. Cao Y, Guo Y, Yu H, Yu X (2017) Epileptic seizure auto-detection using deep learning method. In: 2017 4th International Conference on Systems and Informatics (ICSAI), Hangzhou, pp 1076–1081

  6. Daoud H, Bayoumi MA (2019) Efficient epileptic seizure prediction based on deep learning. IEEE Trans Biomed Circuits Syst 13(5):804–813

    Article  Google Scholar 

  7. Ganapathy S, Sethukkarasi R, Yogesh P, Vijayakumar P, Kannan A (2014) An intelligent temporal pattern classification system using fuzzy temporal rules and particle swarm optimization. Sadhana 39(2):283–302

    Article  MathSciNet  MATH  Google Scholar 

  8. Jiang L, Zhang H, Cai Z (2009) A Novel Bayes Model: Hidden Naive Bayes. IEEE Trans Knowl Data Eng 21(10):1361–1371

    Article  Google Scholar 

  9. Kanimozhi U, Ganapathy S, Manjula D, Kannan A (2019) An intelligent risk prediction system for breast Cancer using fuzzy temporal rules. Natl Acad Sci Lett 42(03):227–232

    Article  Google Scholar 

  10. Kumar A, Kolekar MH (2014) Machine learning approach for epileptic seizure detection using wavelet analysis of EEG signals. In: 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom), Greater Noida, pp 412–416

  11. Lestari FP, Haekal M, Edison RE, Fauzy F r, Khotimah SN, Haryanto F (2020) Epileptic Seizure Detection in EEGs by Using Random Tree Forest, Naive Bayes and KNN Classification. J Phys Conf Ser 1505:012055

    Article  Google Scholar 

  12. Manasvi Bhat K, Anchalia PP, Yashashree S, Sanjeetha R, Kanavalli A (2019) "Detection and Prediction of the Preictal State of an Epileptic Seizure using Machine Learning Techniques on EEG Data," 2019 IEEE Bombay Section Signature Conference (IBSSC), pp. 1–5

  13. Muhammad Usman S, Khalid S, Aslam MH (2020) Epileptic seizures prediction using deep learning techniques. IEEE Access 8:39998–40007

    Article  Google Scholar 

  14. Pariserum Perumal S, Sannasi G, Arputharaj K (2019) An intelligent fuzzy rule-based e-learning recommendation system for dynamic user interests. J Supercomput 75:5145–5160

  15. Pinto MF, Leal A, Lopes F, Dourado A, Martins P, Teixeira CA (2021) A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction. Sci Rep 11:3415

    Article  Google Scholar 

  16. Randon NJ, Lawry J, Horsburgh K, Cluckie ID (2008) Fuzzy Bayesian modeling of sea-level along the East Coast of Britain. IEEE Trans Fuzzy Syst 16(3):725–738

    Article  Google Scholar 

  17. Rasheed K, Qayyum A, Qadir J, Sivathamboo S, Kawn P, Kuhlmann L, Razi A (2021) Machine learning for predicting epileptic seizures using EEG signals: a review. IEEE Rev Biomed Eng 14:139–155

  18. Riyaz B, Ganapathy S (2020) A deep learning approach for effective intrusion detection in wireless networks using CNN. Soft Comput 24:17265–17278

    Article  Google Scholar 

  19. Selim S, Elhinamy E, Othman H, Abouelsaadat W, Salem MA-M (2019) "A Review of Machine Learning Approaches for Epileptic Seizure Prediction," 2019 14th International Conference on Computer Engineering and Systems (ICCES), pp. 239–244

  20. Shirakawa M, Nakayama K, Hara T, Nishio S (2015) Wikipedia-based semantic similarity measurements for Noisy short texts using extended naive Bayes. IEEE Trans Emerg Top Comput 3(2):205–219

    Article  Google Scholar 

  21. Siddiqui MK, Morales-Menendez R, Huang X, Hussain N (2020) A review of epileptic seizure detection using machine learning classifiers. Brain Informatics 7(5):1–18

    Google Scholar 

  22. Singh K, Malhotra J (2022) Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features. Complex Intell Syst 8:2405–2418

    Article  Google Scholar 

  23. Usman SM, Latif S, Beg A (2019) Principle components analysis for seizures prediction using wavelet transform. Int J Adv Appl Sci 6(3):50–55

  24. Usman SM, Usman M, Fong S (2017) Epileptic seizures Prediction using Machine Learning Methods. Comput Math Methods Med 2017, Article ID: 9074759:1–10

    Article  MathSciNet  Google Scholar 

  25. Wang S, Ren J, Bai R (2020) A regularized attribute weighting framework for naive Bayes. IEEE Access 8:225639–225649

    Article  Google Scholar 

  26. Wu X, Zhang T, Zhang L, Qiao L (2022) Epileptic seizure prediction using successive variational mode decomposition and transformers deep learning network. Front Neurosci 16(982541):1–15

    Google Scholar 

  27. Yu L, Gan S, Chen Y, He M (2020) Correlation-based weight adjusted naive Bayes. IEEE Access 8:51377–51387

    Article  Google Scholar 

Download references

Funding

No Funding for this work.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

    P. Srihari & V. Santosh

  2. Centre for Cyber Physical Systems & School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

    Sannasi Ganapathy

Authors
  1. P. Srihari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Santosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sannasi Ganapathy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P. Srihari has been implemented the work and written the initial level paper.

V. Santosh has been implemented the work and written the initial level paper.

Sannasi Ganapathy finalized the problem and gave guidance to implement the work. Moreover, he has revised the paper from rough draft of the paper and taken care of the revision.

Corresponding author

Correspondence to Sannasi Ganapathy.

Ethics declarations

Ethics approval

Not Applicable.

Consent to participate

Not Applicable.

Consent for publication

Not Applicable.

Conflicts of interest/Competing interests.

There is no conflicts 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

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

Srihari, P., Santosh, V. & Ganapathy, S. An epileptic seizures diagnosis system using feature selection, fuzzy temporal naive Bayes and T-CNN. Multimed Tools Appl 82, 34075–34094 (2023). https://doi.org/10.1007/s11042-023-14928-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14928-7

Keywords

Search

Navigation