Skip to main content
SpringerLink
Log in

Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction

  • Research Article
  • Published:
Journal of Healthcare Informatics Research Aims and scope Submit manuscript

Abstract

Epilepsy affects more than 50 million people worldwide, making it one of the world’s most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches model to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives.

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

Similar content being viewed by others

Availability of Data and Materials

The datasets used in the paper are publicly available.

References

  1. Shahbazi M, Aghajan H (2018) A generalizable model for seizure prediction based on deep learning using cnn-lstm architecture. In: 2018 IEEE Global conference on signal and information processing (GlobalSIP), Anaheim, USA

  2. Beghi E, Giussani G (2018) Aging and the epidemiology of epilepsy. Neuroepidemiology 51:216–223

    Article  Google Scholar 

  3. Litt B, Echauz J (2002) Prediction of epileptic seizures. Neurology 1:22–30

    Google Scholar 

  4. Nguyen R, T’ellez Zenteno JF (2009) Injuries in epilepsy: a review of its prevalence, risk factors, type of injuries and prevention. Neurol Int 1

  5. Ridsdale L, Charlton J, Ashworth M, Richardson MP, Gulliford MC (2011) Epilepsy mortality and risk factors for death in epilepsy: a population-based study. Br J Gen Pract 61:271–278

    Article  Google Scholar 

  6. Fisher RS (2000) Epilepsy from the patient’s perspective: Review of results of a community-based survey. Epilepsy Behav 1:9–14

    Article  Google Scholar 

  7. Kuhlmann L, Lehnertz K, Richardson MP, Schelter B, Zaveri HP (2018) Seizure prediction — ready for a new era. Nat Rev Neurol 14:618–630

  8. Stacey W, Le Van Quyen M, Mormann F, Schulze-Bonhage A (2011) What is the present-day eeg evidence for a preictal state? Epilepsy Res 97:243–251

    Article  Google Scholar 

  9. Brinkamann BH, Wagenaar J, Abbot D, Adkins P, Bosshard SC, Chen M, Tieng QM, He J, Muñoz-Almaraz FJ, Botella-Rocamora P, Pardo J, Zamora-Martinez F, Hills M, Wu W, Korshunova I, Cukierski W, Vite C, Patterson EE, Litt B, Worrel GA (2016) Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139:1713–1722

    Article  Google Scholar 

  10. Bandarabadi M, Rasekhi J, Teixeira CA, Karami MR, Dourado A (2015) On the proper selection of preictal period for seizure prediction. Epilepsy Behav 158–166:46

    Google Scholar 

  11. Giannakakis G, Sakkalis V, Pediaditis M, Tsiknakis M (2014) Methods for seizure detection and prediction: An overview. Mod Electroencephalographic Assem Tech 91:131–157

    Article  Google Scholar 

  12. Georgis-Yap Z, Popovic MR, Khan SS (2022) Preictal-interictal classification for seizure prediction. In: The 35th Canadian conference on artificial intelligence

  13. Burrello A, Cavigelli L, Schindler K, Benini L, Rahimi A (2019) Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, automation & test in Europe conference & exhibition (DATE). Florence, Italy

  14. Shoeb AH (2009) Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology

  15. Acharya UR, Hagiwara Y, Adeli H (2018) Automated seizure prediction. Epilepsy Behav 88:251–261

    Article  Google Scholar 

  16. 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 2022

  17. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  18. Teixeira CA, Direito B, Bandarabadi M, Le Van Quyen M, Valerrama M, Schelter B, Schulze-Bonhage A, Navarro V, Sales F, Dourado A (2014) Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Comput Methods Prog Biomed 114:324–336

    Article  Google Scholar 

  19. Fei K, Wang W, Yang Q, Tang S (2017) Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249:290–298

    Article  Google Scholar 

  20. Mirowski P, Madhavan D, LeCun Y, Kuzniecky R (2009) Classification of patterns of eeg synchronization for seizure prediction. Clin Neurophysiol 120:1927–1940

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Truong ND, Nguyen AD, Kuhlmann L, Bonyadi MR, Yang J, Ippolito S, Kavehei O (2018) Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw 105:104–111

    Article  Google Scholar 

  23. Eberlein M, Hildebrand R, Tetzlaff R, Hoffmann N, Kuhlmann L, Brinkmann B, M”uller J (2018) Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International conference on bioinformatics and biomedicine (BIBM), Madrid

  24. Zhang Y, Guo Y, Yang P, Chen W, Lo B (2020) Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE J Biomed Health Informat 24:465–474

    Article  Google Scholar 

  25. Liu Y, Sivathamboo S, Goodin P, Bonnington P, Kwan P, Kuhlmann L, O’Brien T, Perucca P, Ge Z (2020) Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian computer science week multiconference, Melbourne, Australia

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

    Article  Google Scholar 

  27. Jana R, Mukherjee I (2021) Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomed Signal Process Control 68

  28. Xu Y, Yang J, Zhao S, Wu H, Sawan M (2020) An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International conference on artificial intelligence circuits and systems (AICAS), Italy

  29. Tsiouris KM, 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 

  30. Abdelhameed AM, Bayoumi M (2018) Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International conference on machine learning and applications (ICMLA), Orlando

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

    Article  Google Scholar 

  32. Wei X, Zhou L, Zhang Z, Chen Z, Zhou Y (2019) Early prediction of epileptic seizures using a long-term recurrent convolutional network. J Neurosci Methods 327

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

    Article  Google Scholar 

  34. Hussein A, Djandji M, Mahmoud R, Dhaybi M, Hajj HM (2020) Augmenting dl with adversarial training for robust prediction of epilepsy seizures. J ACM 1

  35. 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

  36. Ozcan AR, Erturk S (2019) Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Trans Neural Syst Rehab Eng 27:2284–2293

    Article  Google Scholar 

  37. Truong ND, Zhou L, Kavehei O (2019) Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual international conference of the ieee engineering in medicine and biology society (EMBC), Berlin

  38. Sherstinsky A (2020) Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physic D Nonlinear Phenom 404:132306

    Article  MathSciNet  Google Scholar 

  39. Thill M, Konen W, Wang H, B”ack T, (2021) Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Appl Soft Comput 112:107751

  40. Stefan Denkovski SSK, Mihailidis A (2023) Temporal shift - multi-objective loss function for improved anomaly fall detection. In: \(15^{th}\) Asian conference on machine learning

  41. Khan SS, Khoshbakhtian F, Ashraf AB (2021) Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian conference on AI

  42. Jacob Nogas SSK, Mihailidis A (2018) Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd workshop on aging, rehabilitation and independent assisted living, IJCAI workshop

  43. Abedi A, Khan SS (2023) Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. SIViP

  44. Al-Fahoum AS, Al-Fraihat AA (2014) Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. Int Scholar Res Not 2014

  45. Herrmann CS, Rach S, Vosskuhl J, Str’’uber D (2014) Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topogr 27:438–450

    Article  Google Scholar 

  46. Herrmann CS, Rach S, Vosskuhl J, Str’’uber D (2014) Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topogr 27:438–450

    Article  Google Scholar 

  47. Fadzal CWNFCW, Mansor W, Khuan LY, Zabidi A (2012) Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International colloquium on signal processing and its applications. Malacca, Malaysia

  48. Edakawa K, Yanagisawa T, Kishima H, Fukuma R, Oshino S, Khoo HM, Kobayashi M, Tanaka M, Yoshimine T (2016) Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Sci Rep 6(1):1–8

    Article  Google Scholar 

  49. Huang J, Ling CX (2005) Using auc and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17:299–310

    Article  Google Scholar 

  50. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tajani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: An imperative style, high-performance deep learning library. In: Advances in neural information processing systems 32, Vancouver

  51. Branco P, Torgo L, Ribeiro RR (2016) A survey of predictive modeling on imbalanced domains. ACM Comput Surv (CSUR) 49:1–50

    Article  Google Scholar 

  52. Khan SS, Karg ME, Kulić D, Hoey J (2017) Detecting falls with x-factor hidden markov models. Appl Soft Comput 55:168–177

    Article  Google Scholar 

  53. Khan SS, Mishra PK, Ye B, Newman K, Iaboni A, Mihailidis A (2023) Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on robots and vision (CRV), pp 265–272. IEEE

  54. Habashi AG, Azab AM, Eldawlatly S, Aly GM (2023) Generative adversarial networks in eeg analysis: an overview. J NeuroEng Rehab 20(1):40

    Article  Google Scholar 

  55. Khan SS, Nogas J, Mihailidis A (2021) Spatio-temporal adversarial learning for detecting unseen falls. Pattern Anal Applic 24:381–391

    Article  Google Scholar 

Download references

Funding

This work is supported by the Natural Sciences and Engineering Research Council of Canada and Data Science Institute, University of Toronto.

Author information

Author notes

  1. Zakary Georgis-Yap and Shehroz S. Khan contributed equally to this work.

Authors and Affiliations

  1. KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 550, University Avenue, Toronto, M5G 2A2, Ontario, Canada

    Zakary Georgis-Yap, Milos R. Popovic & Shehroz S. Khan

  2. Institute of Biomedical Engineering, University of Toronto, 64 College St., Toronto, M5S 3G9, Ontario, Canada

    Zakary Georgis-Yap, Milos R. Popovic & Shehroz S. Khan

Authors
  1. Zakary Georgis-Yap
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Milos R. Popovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shehroz S. Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Zakary Georgis-Yap and Shehroz S. Khan contributed equally in terms of developing algorithms, evaluation of results and manuscript preparation. Shehroz S. Khan prepared subsequent revisions of the paper. Milos R. Popovic provided academic support and consultation throughout the study.

Corresponding author

Correspondence to Shehroz S. Khan.

Ethics declarations

Ethics Approval

Two publicly available datasets were used in this work; therefore, ethics approvals were not required to use them.

Informed Consent

All the co-authors give their consent to publish this paper.

Conflict of Interest

The authors declare no competing interests.

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

Georgis-Yap, Z., Popovic, M.R. & Khan, S.S. Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction. J Healthc Inform Res 8, 286–312 (2024). https://doi.org/10.1007/s41666-024-00160-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41666-024-00160-x

Keywords

Search

Navigation