Skip to main content
SpringerLink
Log in

An efficient epileptic seizure detection by classifying focal and non-focal EEG signals using optimized deep dual adaptive CNN-HMM classifier

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

Abstract

Seizures are defined as short occurrences of unusual elevated brain electrical activity that can result in a variety of symptoms and actions where Seizures are the main sign of epilepsy. Due to the unexpected character of seizures and the individual variances in symptoms, examining individuals who are experiencing epileptic seizures could pose some difficulties. Recent researches have very low accuracies in epileptic seizure detection so in order to solve these above issues a detection model is developed that helps the health care sector. In this research, an improved deep dual adaptive CNN-HMM classifier is developed to detect the epileptic seizures automatically with focal and non-focal epileptic EEG signals. The inputs are collected from the four datasets and preprocessing is performed for converting unstructured data into structured data. The preprocessed signal is divided into five separate sub-bands and subjected to wavelet decomposition to decrease noise. The Human learning optimization (HLO) algorithm is proposed to perform the electrode selection process to identify the best electrode and also helps to reduce the overfitting problem. Once the signals are decided optimally, the features extraction takes place through three steps such as TQWT, Hjorth and statistical features are preferred for analyzing the EEG signals to derive the deep analysis of the data. The seizure detection is done using the deep dual adaptive CNN-HMM classifier, which helps in the efficient detection of epileptic seizure. The accuracy, sensitivity, specificity, precision and f-measure of the deep dual adaptive CNN-HMM classifier's outputs are evaluated. For dataset 1, attains 99.46%, 98.48%, 99.46%, 99.90%, and 99.58% with TP, 98.13%, 98.46%, 97.56%, 99.88%, and 99.56% with tenfold. For dataset 2, attains 94.53%, 92.37%, 99.94%, 93.11% and 93.60% with TP, 90.84%, 91.17%, 90.27%, 93.09% and 93.58% with tenfold. Similarly, for dataset 3 attains 94.48%, 94.62%, 96.82%, 95.41%, and 96.40% with TP, 94.54%, 94.68%, 96.87%, 95.46% and 96.45% with tenfold. For dataset 4, attains 99.13%, 98.72%, 98.00%, 96.73% and 97.72% with TP, 99.28%, 99.32%, 99.22%, 98.85% and 98.92% with tenfold, which is more efficient than other existing methods.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

The datasets available for detecting epileptic seizures automatically with focal and non-focal epileptic EEG signals include the CHB-MIT Scalp EEG Database, Siena Scalp EEG Database, Epileptic EEG Dataset and Bern-Barcelona EEG database.

References

  1. Rahman MM, Bhuiyan MIH, Das AB (2019) Classification of focal and non-focal EEG signals in VMD-DWT domain using ensemble stacking. Biomed Signal Process Control 50:72–82

    Article  Google Scholar 

  2. Fraiwan L, Alkhodari M (2020) Classification of focal and non-focal epileptic patients using single channel EEG and long short-term memory learning system. IEEE Access 8:77255–77262

    Article  Google Scholar 

  3. Epilepsy, key facts (2020) https://www.who.int/health-topics/epilepsy#tab=tab1. Accessed Oct 2022

  4. Yuan Y, Xun G, Jia K, Zhang A (2019) A multi-view deep learning framework for EEG seizure detection. IEEE J Biomed Health Inform 23(1):83–94

    Article  Google Scholar 

  5. Ahmedt-Aristizabal D, Fookes C, Denman S, Nguyen K, Sridharan S, Dionisio S (2019) Aberrant epileptic seizure identification: a computer vision perspective. Seizure 65, 65 – 71. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1059131118307076. Accessed Oct 2022

  6. Zhang Z, Parhi KK (2016) Low-complexity seizure prediction from ieeg/seeg using spectral power and ratios of spectral power. IEEE Trans Biomed Circuits Syst 10(3):693–706

    Article  Google Scholar 

  7. Dissanayake T, Fernando T, Denman S, Sridharan S, Fookes C (2021) Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals. IEEE Sens J 21(7):9377–9388

    Article  Google Scholar 

  8. Jukic S, Saracevic M, Subasi A, Kevric J (2020) Comparison of ensemble machine learning methods for automated classification of focal and non-focal epileptic EEG signals. Mathematics 8(9):1481

    Article  Google Scholar 

  9. Tuncer T, Dogan S, Akbal E (2019) A novel local senary pattern based epilepsy diagnosis system using EEG signals. Australas Phys Eng Sci Med 42:939–948

    Article  Google Scholar 

  10. World Health Organization (2006) Neurological disorders: public health challenges. World Health Organization

  11. 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 

  12. Chakrabarti S, Swetapadma A, Ranjan A et al (2020) 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

    Article  Google Scholar 

  13. Cheng C, You Bo, Liu Y, Dai Y (2021) Patient-specific method of sleep electroencephalography using wavelet packet transform and Bi-LSTM for epileptic seizure prediction. Biomed Signal Process Control 70:102963

    Article  Google Scholar 

  14. Hussein AF, Arunkumar N, Gomes C, Alzubaidi AK, Habash QA, Santamaria-Granados L, Mendoza-Moreno JF, Ramirez-Gonzalez G (2018) Focal and non-focal epilepsy localization: a review. IEEE Access 6:49306–49324

    Article  Google Scholar 

  15. Pati S, Alexopoulos AV (2010) Pharmacoresistant epilepsy: from pathogenesis to current and emerging therapies. Cleve Clinic J Med 77(7):457–467

    Article  Google Scholar 

  16. Stam CJ (2005) Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 116:2266–2301 ([CrossRef])

    Article  Google Scholar 

  17. Prathaban BP, Balasubramanian R (2021) Dynamic learning framework for epileptic seizure prediction using sparsity based EEG reconstruction with optimized CNN classifier. Expert Syst Appl 170:114533

    Article  Google Scholar 

  18. Pereda E, QuianQuiroga R, Bhattacharya J (2005) Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol 77:1–37

    Article  Google Scholar 

  19. Chavan P, Desai S (2021) A review on BCI emotions classification for EEG signals using deep learning, vol 39. IOS Press BV, pp 544–551. https://doi.org/10.3233/apc210241

  20. Jiang Z, Zhao W (2020) Optimal selection of customized features for implementing seizure detection in wearable electroencephalography sensor. IEEE Sensors J 20(21):12 941-12 949

    Article  Google Scholar 

  21. Subathra MSP, Mohammed MA, Maashi MS, Garcia-Zapirain B, Sairamya NJ, Thomas George S (2020) Detection of focal and non-focal electroencephalogram signals using fast Walsh-Hadamard transform and artificial neural network. Sensors 20(17):4952

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Natu M, Bachute M, Gite S, Kotecha K, Vidyarthi A (2022) Review on epileptic seizure prediction: machine learning and deep learning approaches. Comput Math Methods Med 2023:1–17

    Article  Google Scholar 

  24. Desai S, Patil ST (2018) Boosting decision trees for prediction of market trends. J Eng Appl Sci 13(3):552–556

    Google Scholar 

  25. Khan H, Marcuse L, Fields M, Swann K, Yener B (2018) Focal onset seizure prediction using convolutional networks. IEEE Trans Biomed Eng 65(9):2109–2118

    Article  Google Scholar 

  26. Radman M, Moradi M, Chaibakhsh A, Kordestani M, Saif M (2020) Multi-feature fusion approach for epileptic seizure detection from EEG signals. IEEE Sens J 21(3):3533–3543

    Article  Google Scholar 

  27. Hassan AR, Subasi A, Zhang Y (2020) Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise. Knowl-Based Syst 191:105333. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0950705119306045. Accessed Oct 2022

  28. Singh K, Malhotra J (2022) Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features. Complex Intell Syst 8(3):2405–2418

    Article  Google Scholar 

  29. Dissanayake T, Fernando T, Denman S, Sridharan S, Fookes C (2021) Geometric deep learning for subject independent epileptic seizure prediction using scalp EEG signals. IEEE J Biomed Health Inform 26(2):527–538

    Article  Google Scholar 

  30. Rashed-Al-Mahfuz M, Moni MA, Uddin S, Alyami SA, Summers MA, Eapen V (2021) A deep convolutional neural network method to detect seizures and characteristic frequencies using Epileptic Electroencephalogram (EEG) data. IEEE J Transl Eng Health Med 9:1–12. https://doi.org/10.1109/JTEHM.2021.3050925. (Art no. 2000112)

    Article  Google Scholar 

  31. Raghu S, Sriraam N, Temel Y, Rao SV, Kubben PL (2020) EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Netw 124:202–212

    Article  Google Scholar 

  32. Syed Rafiammal S, Najumnissa Jamal D, KajaMohideen S (2021) Detection of epilepsy seizure in adults using discrete wavelet transform and cluster nearest neighborhood classifier. Iran J Sci Technol Trans Electr Eng 45:1103–1115

    Article  Google Scholar 

  33. Glory HA, Vigneswaran C, Jagtap SS, Shruthi R, Hariharan G, Shankar Sriram VS (2021) AHW-BGOA-DNN: a novel deep learning model for epileptic seizure detection. Neural Comput Appl 33:6065–6093

    Article  Google Scholar 

  34. Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Alizadehsani R, Zare A, Khosravi A, Subasi A, Acharya UR, Gorriz JM (2022) Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies. Biomed Signal Process Control 73:103417

    Article  Google Scholar 

  35. Singh K, Malhotra J (2022) Prediction of epileptic seizures from spectral features of intracranial eeg recordings using deep learning approach. Multimed Tools Appl 81(20):28875–28898

    Article  Google Scholar 

  36. Singh K, Malhotra J (2022) Predicting epileptic seizures from EEG spectral band features using convolutional neural network. Wireless Pers Commun 125(3):2667–2684

    Article  Google Scholar 

  37. Singh K, Malhotra J (2021) Deep learning based smart health monitoring for automated prediction of epileptic seizures using spectral analysis of scalp EEG. Phys Eng Sci Med 44(4):1161–1173

    Article  Google Scholar 

  38. Chavan PA, Desai S (2023) Effective epileptic seizure detection by classifying focal and non-focal EEG signals using human learning optimization-based hidden Markov Model. Biomed Signal Process Control 83:104682. https://doi.org/10.1016/j.bspc.2023.104682. (ISSN 1746-8094)

    Article  Google Scholar 

  39. Mousavirad SJ, Ebrahimpour-Komleh H (2017) Human mental search: a new population-based metaheuristic optimization algorithm. App Intell 47(3):850–887

    Article  Google Scholar 

  40. Rao RV (2016) Teaching-learning-based optimization algorithm. In: Teaching learning based optimization algorithm. Springer, Cham, pp 9–39

  41. Children Hospital Boston, Massachusetts Institute of Technology (CHB-MIT) - EEG Dataset. https://physionet.org/content/chbmit/1.0.0/. Accessed Oct 2022

  42. Siena Scalp EEG Database. https://physionet.org/content/siena-scalp-eeg/1.0.0/. Accessed Oct 2022

  43. Epileptic EEG Dataset is taken form: https://data.mendeley.com/datasets/5pc2j46cbc/. Accessed Apr 2023

  44. Bern-Barcelona EEG database is taken from https://www.upf.edu/web/ntsa/downloads/-/asset_publisher/xvT6E4pczrBw/content/2012-nonrandomness-nonlinear-dependence-and-nonstationarity-of-electroencephalographic-recordings-from-epilepsy-patients. Accessed Apr 2023

  45. Sharmila A, Geethanjali P (2016) DWT based detection of epileptic seizure from EEG signals using naive bayes and k-NN classifiers. IEEE Access 4:7716–7727. https://doi.org/10.1109/ACCESS.2016.2585661

    Article  Google Scholar 

  46. Subasi A, Kevric J, Abdullah Canbaz M (2019) Epileptic seizure detection using hybrid machine learning methods. Neural Comput Appl 31:317–325

    Article  Google Scholar 

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

    Article  Google Scholar 

  48. Geng M, Zhou W, Liu G, Li C, Zhang Y (2020) Epileptic seizure detection based on stockwell transform and bidirectional long short-term memory. IEEE Trans Neural Syst Rehabil Eng 28(3):573–580. https://doi.org/10.1109/TNSRE.2020.2966290

    Article  Google Scholar 

  49. Shoka AAE, Dessouky MM, El-Sayed A, Hemdan EE-D (2023) An efficient CNN based epileptic seizures detection framework using encrypted EEG signals for secure telemedicine applications. Alex Eng J 65:399–412

    Article  Google Scholar 

  50. Dash DP, Kolekar MH, Jha K (2020) Multi-channel EEG based automatic epileptic seizure detection using iterative filtering decomposition and Hidden Markov Model. Comput Biol Med 116:103571

    Article  Google Scholar 

  51. Natu M, Bachute M, Kotecha K (2023) HCLA_CBiGRU: hybrid convolutional bidirectional GRU Based model for epileptic seizure detection. Neuroscience Informatics:100135

  52. Shabani A, Asgarian B, Salido M, Gharebaghi SA (2020) Search and rescue optimization algorithm: a new optimization method for solving constrained engineering optimization problems. Expert Syst Appl 161:113698

    Article  Google Scholar 

  53. Ghoneim SSM, Mahmoud K, Lehtonen M, Darwish MMF (2021) Enhancing diagnostic accuracy of transformer faults using teaching-learning-based optimization. IEEE Access 9:30817–30832. https://doi.org/10.1109/ACCESS.2021.3060288

    Article  Google Scholar 

  54. Behnam M, Pourghassem H (2015) Lagged correlogram patterns-based seizure detection algorithm using optimized HMM feature fusion. In: 2015 annual IEEE India conference (INDICON). IEEE, pp 1–6

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Paud Rd, Kothrud, Pune, Maharashtra, 411038, India

    Puja A. Chavan & Sharmishta Desai

Authors
  1. Puja A. Chavan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sharmishta Desai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Puja A. Chavan.

Ethics declarations

Competing interests

The authors declare that they have 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

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

Chavan, P.A., Desai, S. An efficient epileptic seizure detection by classifying focal and non-focal EEG signals using optimized deep dual adaptive CNN-HMM classifier. Multimed Tools Appl 83, 57347–57388 (2024). https://doi.org/10.1007/s11042-024-18560-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-024-18560-x

Keywords

Search

Navigation