Skip to main content
SpringerLink
Log in

A CNN-LSTM hybrid network for automatic seizure detection in EEG signals

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Epilepsy is a chronic neurological disorder. Epileptics are prone to sudden seizures that cause disruptions in their daily lives. The separation of epileptic and non-epileptic activity on the electroencephalogram (EEG) and identification of the form of epileptic activity play critical roles in providing patients with appropriate treatment. To recognize epileptic seizures, medical experts visually inspect recordings of EEG signals, which require much time and effort. Therefore, a seizure detection system can improve the monitoring and diagnosis of epilepsy and reduce the doctors' workload. This paper presents an end-to-end automated seizure detection method based on deep learning that does not require considerable EEG data preprocessing or feature extraction. As a result presents a one-dimensional convolutional neural network-long short-term memory (1D-CNN-LSTM) model for differentiating normal, ictal, and interictal EEG data. This method is evaluated using the University of Bonn (BoU) and Neurology and Sleep Centre database (NSC). We achieved accuracy values of 99–100% for the BoU dataset and 100% for the NSC dataset with our best model. In contrast with recent studies, our hybrid automated approach does not require any pre-selected features to be estimated and shows high performance with promising possibilities for their use in clinical practice.

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

Similar content being viewed by others

Data availability statement

Following are the links that provide access to the datasets used in this work: (1) Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., and Elger, C. E. (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 Stat Nonlin Soft Matter Phys, 64(6), 061907. (2) Available: https://www.researchgate.net/publication/308719109_EEG_Epilepsy_Datasets.

References

  1. Mormann F, Andrzejak RG, Elger CE, Lehnertz K (2006) Seizure prediction: the long and winding road. Brain 130(2):314–333. https://doi.org/10.1093/brain/awl241

    Article  Google Scholar 

  2. Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS (2013) Automated EEG analysis of epilepsy: a review. Knowl.-Based Syst 45:147–165. https://doi.org/10.1016/j.knosys.2013.02.014.

  3. Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh JE (2015) Application of entropies for automated diagnosis of epilepsy using EEG signals: A review. Knowl-BasedSyst 88:85–96. https://doi.org/10.1016/j.knosys.2015.08.004

    Article  Google Scholar 

  4. Forooghifar F, Aminifar A, Alonso DA (2018) Self-aware wearable systems in epileptic seizure detection. In: 2018 21st Euromicro conference on digital system design (DSD), pp 426–43. https://doi.org/10.1109/DSD.2018.00078.

  5. Subouchi Y, Tanabe A, Saito Y, Noma H, Maegaki Y (2019) Long-term prognosis of epilepsy in patients with cerebral palsy. Dev Med Child Neurol 61:1067–1073. https://doi.org/10.1111/dmcn.14188

    Article  Google Scholar 

  6. Smith SJM (2005) EEG in the diagnosis classification, and management of patients with epilepsy. J NeurolNeurosurgPsychiatry. https://doi.org/10.1136/jnnp.2005.069245

    Article  Google Scholar 

  7. Muhammad G, Masud M, Amin SU, Alrobaea R, Alhamid MF (2018) Automatic seizure detection in a mobile multimedia framework. IEEE Access 6:45372–45383

    Article  Google Scholar 

  8. Elezi L, Koren JP, Pirker S, Baumgartner C (2022) Automatic seizure detection and seizure pattern morphology. Clin Neurophysiol**.

  9. Li Y, Yu Z, Chen Y, Yang C, Li Y, Allen Li X, Li B (2020) Automatic seizure detection using fully convolutional nested LSTM. Int J Neural Syst 30(04):2050019

    Article  Google Scholar 

  10. Faust O, Hagiwara Y, Hong TJ et al (2018) Deep learning forhealthcare applications based on physiological signals: a review.**

  11. Selvakumari RS, Mahalakshmi M, Prashalee P (2019) Patient-specific seizure detection method using hybrid classifier with optimized electrodes. J Med Syst 43(5):1–7

    Article  Google Scholar 

  12. Divya R, Kumari SS (2021) Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification. Neural Comput Appl 33(14):8435–8444

    Article  Google Scholar 

  13. Tiwari S, Goel S, Bhardwaj A (2022) MIDNN-a classification approach for the EEG based motor imagery tasks using deep neural network. Appl Intell 52(5):4824–4843

    Article  Google Scholar 

  14. Aydemir E, Tuncer T, Dogan S, Gururajan R, Acharya UR (2021) Automated major depressive disorder detection using melamine pattern with EEG signals. Appl Intell 51(9):6449–6466

    Article  Google Scholar 

  15. Sharma M, Deb D, Acharya UR (2018) A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals. Appl Intell 48:1368–1378. https://doi.org/10.1007/s10489-017-1042-9

    Article  Google Scholar 

  16. Chen D, Wan S, Xiang J, Bao FS (2017) A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG. PLoS ONE 12(3): e0173138. https://doi.org/10.1371/journal.pone.0173138

  17. 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. Biomed Signal Process Control 39:94–102

    Article  Google Scholar 

  18. Liu Y, Zhou W, Yuan Q, Chen S (2012) Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. IEEE Trans Neural Syst Rehabilit Eng 20(6):749–755

    Article  Google Scholar 

  19. Li S, Zhou W, Yuan Q, Geng S, Cai D (2013) Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 43(7):807–816

    Article  Google Scholar 

  20. Riaz F, Hassan A, Rehman S, Niazi IK, Dremstrup K (2015) EMD-based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Trans Neural Syst Rehabil Eng 24(1):28–35

    Article  Google Scholar 

  21. Chen LL, Zhang J, Zou JZ, Zhao CJ, Wang GS (2014) A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection. Biomed Signal Process Control 10:1–10

    Article  Google Scholar 

  22. Anuragi A, Sisodia DS, Pachori RB (2022) Epileptic-seizure classification using phase-space representation of FBSE-EWT based EEG sub-band signals and ensemble learners. Biomed Signal Process Control 71:103138

    Article  Google Scholar 

  23. Zhang T, Chen W, Li M (2017) AR based quadratic feature extraction in the VMD domain for the automated seizure detection of EEG using random forest classifier. Biomed Signal Process Control 31:550–559. https://doi.org/10.1016/j.bspc.2016.10.001

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Omidvar M, Zahedi A, Bakhshi H (2021) EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers. J Ambient Intell Humaniz Comput 12(11):10395–10403

    Article  Google Scholar 

  26. Wang Y, Li Z, Feng L, Zheng C, Zhang W (2017) Automatic detection of epilepsy and seizure using multiclass sparse extreme learning machine classification. Comput Math Methods Med**.

  27. Ahila Priyadharshini R, Arivazhagan S, Arun M (2021) A deep learning approach for person identification using ear biometrics. Appl Intell 51(4):2161–2172

    Article  Google Scholar 

  28. Arivazhagan S, Amrutha E, Jebarani SL, Veena ST (2021) Hybrid convolutional neural network architecture driven by residual features for steganalysis of spatial steganographic algorithms. Neural Comput Appl 33(17):11465–11485

    Article  Google Scholar 

  29. Russel NS, Selvaraj A (2021) Fusion of spatial and dynamic CNN streams for action recognition. Multimedia Syst 27(5):969–984

    Article  Google Scholar 

  30. Zhou M, Tian C, Cao R, Wang B, Niu Y, Hu T, Guo H, Xiang J (2018) Epileptic seizure detection based on EEG signals and CNN. Front Neuroinformatics, 95.

  31. Rajendra Acharya U, Oh SL, Hagiwara Y, Tan JH, Adeli A (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278.

  32. Ihsan U, Hussain M, Aboalsamh H et al (2018) An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst Appl 107:61–71

    Article  Google Scholar 

  33. Hussein R, Elgendi M, Wang ZJ, Ward RK (2018) Robust detection of epileptic seizures based on L1-penalized robust regression of EEG signals. Expert Syst Appl 104:153–167

    Article  Google Scholar 

  34. Tsiouris ΚM, Pezoulas VC, Zervakis M, Konitsiotis S, Koutsouris DD, Fotiadis DI (2018) A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput Biol Med 99:24–37

    Article  Google Scholar 

  35. Liang W, Pei H, Cai Q, Wang Y (2020) Scalp EEG epileptogenic zone recognition and localization based on long-term recurrent convolutional network. Neurocomputing 396:569–576

    Article  Google Scholar 

  36. Li Y, Yu Z, Chen Y, Yang C, Li Y, Li XA, Li B (2020) Automatic seizure detection using fully convolutional nested LSTM. Int J Neural Syst. https://doi.org/10.1142/s0129065720500197

    Article  Google Scholar 

  37. Xu G, Ren T, Chen Y, Che W (2020) A one-dimensional CNN-LSTM model for epileptic seizure recognition using EEG signal analysis. Front Neurosci 14:1253

    Article  Google Scholar 

  38. 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 Stat Nonlin Soft Matter Phys 64(6):061907

    Article  Google Scholar 

  39. Available: https://www.researchgate.net/publication/308719109_EEG_Epilepsy_Datasets.

  40. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data 8(1):1–74

    Article  Google Scholar 

  41. Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451–2471. https://doi.org/10.1162/089976600300015015. (PMID: 11032042)

    Article  Google Scholar 

  42. Wang Y, Li Z, Feng L, Bai H, Wang C (2018) Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection. IET Circuits Devices Syst 12(1):108–115

    Article  Google Scholar 

  43. van der Maaten LJP, Hinton GE (2008) Visualizing high-dimensional data using t-SNE. J Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

  44. Zhao M, Zhong S, Fu X, Tang B, Pecht M (2019) Deep residual shrinkage networks for fault diagnosis. IEEE Trans Industr Inf 16(7):4681–4690

    Article  Google Scholar 

  45. San-Segundo R, Gil-Martín M, D’Haro-Enríquez LF, Pardo JM (2019) Classification of epileptic EEG recordings using signal transforms and convolutional neural networks. ComputBiol Med 109:148–158. https://doi.org/10.1016/j.compbiomed.2019.04.031. (Epub 2019 Apr 25 PMID: 31055181)

    Article  Google Scholar 

  46. Türk Ö, Özerdem MS (2019) Epilepsy detection by using scalogram based convolutional neural network from EEG signals. Brain Sci 9(5):115. Published 2019 May 17. https://doi.org/10.3390/brainsci9050115

  47. Zhao W, Zhao W, Wang W, Jiang X, Zhang X, Peng Y, Zhang B, Zhang G (2020) A novel deep neural network for robust detection of seizures using EEG signals. Comput Math Methods Med**.

  48. Zhao W, Wang W (2020) SeizureNet: a model for robust detection of epileptic seizures based on convolutional neural network. Cogn Comput Syst 2(3):119–124

    Article  Google Scholar 

  49. Peng H, Lei C, Zheng S, Zhao C, Wu C, Sun J, Hu B (2021) Automatic epileptic seizure detection via Stein kernel-based sparse representation. Comput Biol Med 132:104338

    Article  Google Scholar 

  50. Chakraborty M, Mitra D (2021) A novel automated seizure detection system from EMD-MSPCA denoised EEG: Refined composite multiscale sample, fuzzy and permutation entropies based scheme. Biomed Signal Process Control 67:102514

    Article  Google Scholar 

  51. Chakraborty M, Mitra D (2021) A computationally efficient automated seizure detection method based on the novel idea of multiscale spectral features. Biomed Signal Process Control 70:102990

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank their Management, Principal, and Head of the department for providing the facilities to carry out this research work and also for their support and encouragement.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, 626 005, India

    Shalini Shanmugam & Selvathi Dharmar

Authors
  1. Shalini Shanmugam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Selvathi Dharmar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shalini Shanmugam.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Shanmugam, S., Dharmar, S. A CNN-LSTM hybrid network for automatic seizure detection in EEG signals. Neural Comput & Applic 35, 20605–20617 (2023). https://doi.org/10.1007/s00521-023-08832-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08832-2

Keywords

Search

Navigation