Skip to main content
SpringerLink
Log in

Combining temporal and spatial attention for seizure prediction

  • Research
  • Published:
Health Information Science and Systems Aims and scope Submit manuscript

Abstract

Purpose:

Approximately 1% of the world population is currently suffering from epilepsy. Successful seizure prediction is necessary for those patients. Influenced by neurons in their own and surrounding locations, the electroencephalogram (EEG) signals collected by scalp electrodes carry information of spatiotemporal interactions. Therefore, it is a great challenge to exploit the spatiotemporal information of EEG signals fully.

Methods:

In this paper, a new seizure prediction model called Gatformer is proposed by fusing the graph attention network (GAT) and the Transformer. The temporal and spatial attention are combined to extract EEG information from the perspective of spatiotemporal interactions. The model aims to explore the temporal dependence of single-channel EEG signals and the spatial correlations among multi-channel EEG signals. It can automatically identify the most noteworthy interaction in brain regions and achieve accurate seizure prediction.

Results:

Compared with the baseline models, the performance of our model is significantly improved. The false prediction rate (FPR) on the private dataset is 0.0064/h. The average accuracy, specificity and sensitivity are 98.25%, 99.36% and 97.65%.

Conclusion:

The proposed model is comparable to the state of the arts. Experiments on different datasets show that it has good robustness and generalization performance. The high sensitivity and low FPR prove that this model has great potential to realize clinical assistance for diagnosis and treatment.

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

Similar content being viewed by others

References

  1. Assi EB, Nguyen DK, Rihana S, Sawan M. Towards accurate prediction of epileptic seizures: a review. Biomed Signal Process Control. 2017;34:144–57.

    Article  Google Scholar 

  2. Calle-López Y, Ladino LD, Benjumea-Cuartas V, Castrillón-Velilla DM, Téllez-Zenteno JF, Wolf P. Forced normalization: a systematic review. Epilepsia. 2019;60(8):1610–8.

    Article  Google Scholar 

  3. Zhang Z, Chen Z, Zhou Y, Du S, Zhang Y, Mei T, Tian X. Construction of rules for seizure prediction based on approximate entropy. Clin Neurophysiol. 2014;125(10):1959–66.

    Article  Google Scholar 

  4. Zandi AS, Tafreshi R, Javidan M, Dumont GA. Predicting epileptic seizures in scalp EEG based on a variational Bayesian Gaussian mixture model of zero-crossing intervals. IEEE Trans Biomed Eng. 2013;60(5):1401–13.

    Article  Google Scholar 

  5. Li S, Zhou W, Yuan Q, Liu Y. Seizure prediction using spike rate of intracranial EEG. IEEE Trans Neural Syst Rehabil Eng. 2013;21(6):880–6.

    Article  Google Scholar 

  6. Alotaiby TN, Alshebeili SA, Alotaibi FM, Alrshoud SR. Epileptic seizure prediction using CSP and LDA for scalp EEG signals. Comput Intell Neurosci. 2017;2017:1240323.

    Article  Google Scholar 

  7. Affes A, Mdhaffar A, Triki C, Jmaiel M, Freisleben B. A convolutional gated recurrent neural network for epileptic seizure prediction. In: International conference on smart homes and health telematics. Cham: Springer; 2019. pp. 85–96.

  8. Xu Y, Yang J, Sawan M. Multichannel synthetic preictal EEG signals to enhance the prediction of epileptic seizures. IEEE Trans Biomed Eng. 2022;69(11):3516–25.

    Article  Google Scholar 

  9. Singh K, Malhotra J. Prediction of epileptic seizures from spectral features of intracranial EEG recordings using deep learning approach. Multimed Tools Appl. 2022;81:1–24.

    Article  Google Scholar 

  10. Li F, Liang Y, Zhang L, Yi C, Liao Y, Jiang Y, Si Y, Zhang Y, Yao D, Yu L, et al. Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis. Cogn Neurodyn. 2019;13(2):175–81.

    Article  Google Scholar 

  11. Wei X, Zhou, Y. A methodical approach to epileptic classification with multi-scale patterns. In: Proceedings of the 2018 5th International conference on biomedical and bioinformatics engineering, 2018, pp. 25–29.

  12. Ma M, Cheng Y, Wei X, Chen Z, Zhou Y. Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN. BMC Med Inform Decis Mak. 2021;21(2):1–13.

    Google Scholar 

  13. Parvez MZ, Paul M. Seizure prediction using undulated global and local features. IEEE Trans Biomed Eng. 2016;64(1):208–17.

    Article  Google Scholar 

  14. He Z, Zhong Y, Pan J. Joint temporal convolutional networks and adversarial discriminative domain adaptation for EEG-based cross-subject emotion recognition. In: ICASSP 2022—2022 IEEE international conference on acoustics, speech and signal processing (ICASSP), 2022. IEEE; 2022. p. 3214–8.

  15. Howlader KC, Satu M, Awal M, Islam M, Islam SMS, Quinn JM, Moni MA, et al. Machine learning models for classification and identification of significant attributes to detect type 2 diabetes. Health Inf Sci Syst. 2022;10(1):1–13.

    Article  Google Scholar 

  16. Akbari H, Sadiq MT, Siuly S, Li Y, Wen P. Identification of normal and depression EEG signals in variational mode decomposition domain. Health Inf Sci Syst. 2022;10(1):1–14.

    Article  Google Scholar 

  17. Wei X, Zhou L, Zhang Z, Chen Z, Zhou Y. Early prediction of epileptic seizures using a long-term recurrent convolutional network. J Neurosci Methods. 2019;327: 108395.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Jana R, Mukherjee I. Deep learning based efficient epileptic seizure prediction with EEG channel optimization. Biomed Signal Process Control. 2021;68: 102767.

    Article  Google Scholar 

  21. Zhao S, Yang J, Xu Y, Sawan M. Binary single-dimensional convolutional neural network for seizure prediction. In: 2020 IEEE international symposium on circuits and systems (ISCAS), 2020.

  22. Jiang Y, Lu Y, Yang L. An epileptic seizure prediction model based on a time-wise attention simulation module and a pretrained ResNet. Methods. 2022;202:117–26.

    Article  Google Scholar 

  23. Sun B, Lv J-J, Rui L-G, Yang Y-X, Chen Y-G, Ma C, Gao Z-K. Seizure prediction in scalp EEG based channel attention dual-input convolutional neural network. Physica A. 2021;584: 126376.

    Article  Google Scholar 

  24. Ma M, Cheng Y, Wang Y, Li X, Mao Q, Zhang Z, Chen Z, Zhou Y. Early prediction of epileptic seizure based on the BNLSTM-CASA model. IEEE Access. 2021;9:79600–10.

    Article  Google Scholar 

  25. Yang X, Zhao J, Sun Q, Lu J, Ma X. An effective dual self-attention residual network for seizure prediction. IEEE Trans Neural Syst Rehabil Eng. 2021;29:1604–13.

    Article  Google Scholar 

  26. Zhao Y, Dong C, Zhang G, Wang Y, Chen X, Jia W, Yuan Q, Xu F, Zheng Y. EEG-Based Seizure detection using linear graph convolution network with focal loss. Comput Methods Programs Biomed. 2021;208: 106277.

    Article  Google Scholar 

  27. He J, Cui J, Zhang G, Xue M, Chu D, Zhao Y. Spatial–temporal seizure detection with graph attention network and bi-directional LSTM architecture. Biomed Signal Process Control. 2022;78: 103908.

    Article  Google Scholar 

  28. Chen X, Zheng Y, Dong C, Song S. Multi-dimensional enhanced seizure prediction framework based on graph convolutional network. Front Neuroinform. 2021;15: 605729.

    Article  Google Scholar 

  29. Veličković P, Cucurull G, Casanova A, Romero, A, Lio P, Bengio Y. Graph attention networks. arXiv preprint; 2017. arXiv:1710.10903.

  30. Zhao Y, Zhang G, Dong C, Yuan Q, Xu F, Zheng Y. Graph attention network with focal loss for seizure detection on electroencephalography signals. Int J Neural Syst. 2021;31(07):2150027.

    Article  Google Scholar 

  31. Sartipi S, Torkamani-Azar M, Cetin M. EEG emotion recognition via graph-based spatio-temporal attention neural networks. In: 43rd Annual international conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021. IEEE; 2021. p. 571–4.

  32. Wang Y, Shi Y, Cheng Y, He Z, Wei X, Chen Z, Zhou Y. A spatiotemporal graph attention network based on synchronization for epileptic seizure prediction. IEEE J Biomed Health Inform. 2023;27(2):900–11.

    Article  Google Scholar 

  33. Zhao Y, Xue M, Dong C, He J, Chu D, Zhang G, Xu F, Ge X, Zheng Y. Automatic seizure identification from EEG signals based on brain connectivity learning. Int J Neural Syst. 2022;32(11):2250050.

    Article  Google Scholar 

  34. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Advances in neural information processing systems, 2017, vol 30.

  35. Devlin J, Chang M.-W, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint, 2018. arXiv:1810.04805.

  36. Zou C, Wang B, Hu Y, Liu J, Wu Q, Zhao Y, Li B, Zhang C, Zhang C, Wei Y, et al. End-to-end human object interaction detection with hoi transformer. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 11825–34.

  37. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al. An image is worth 16 \(\times \) 16 words: transformers for image recognition at scale. arXiv preprint, 2020. arXiv:2010.11929.

  38. Kim D, Lee J, Woo Y, Jeong J, Kim C, Kim D-K. Deep learning application to clinical decision support system in sleep stage classification. J Pers Med. 2022;12(2):136.

    Article  Google Scholar 

  39. Bhattacharya A, Baweja T, Karri S. Epileptic seizure prediction using deep transformer model. Int J Neural Syst. 2022;32(02):2150058.

    Article  Google Scholar 

  40. Hu S, Liu J, Yang R, Wang YN, Wang A, Li K, Liu W, Yang C. Exploring the applicability of transfer learning and feature engineering in epilepsy prediction using hybrid transformer model. IEEE Trans Neural Syst Rehabil Eng. 2023;31:1321–32.

    Article  Google Scholar 

  41. Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R, et al. Relational inductive biases, deep learning, and graph networks. arXiv preprint, 2018. arXiv:1806.01261.

  42. He Z, Li Z, Yang F, Wang L, Li J, Zhou C, Pan J. Advances in multimodal emotion recognition based on brain–computer interfaces. Brain Sci. 2020;10(10):687.

    Article  Google Scholar 

  43. Maiwald T, Winterhalder M, Aschenbrenner-Scheibe R, Voss HU, Schulze-Bonhage A, Timmer J. Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic. Physica D. 2004;194(3–4):357–68.

    Article  Google Scholar 

  44. Zhang Y, Guo Y, Yang P, Chen W, Lo B. Epilepsy seizure prediction on EEG using common spatial pattern and convolutional neural network. IEEE J Biomed Health Inform. 2019;24(2):465–74.

    Article  Google Scholar 

  45. Park Y, Luo L, Parhi KK, Netoff T. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia. 2011;52(10):1761–70.

    Article  Google Scholar 

  46. Pedoeem J, Bar Yosef G, Abittan S, Keene S. TABS: transformer based seizure detection. In: Biomedical sensing and analysis. Berlin: Springer; 2022. p 133–160.

  47. Truong ND, Nguyen AD, Kuhlmann L, Bonyadi MR, Yang J, Ippolito S, Kavehei O. Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw. 2018;105:104–11.

    Article  Google Scholar 

  48. Tang L, Xie N, Zhao M, Wu X. Seizure prediction using multi-view features and improved convolutional gated recurrent network. IEEE Access. 2020;8:172352–61.

    Article  Google Scholar 

  49. Abdelhameed AM, Bayoumi M. An efficient deep learning system for epileptic seizure prediction. In: IEEE international symposium on circuits and systems (ISCAS), 2021. IEEE; 2021. p. 1–5.

  50. Singh K, Malhotra J. Predicting epileptic seizures from EEG spectral band features using convolutional neural network. Wirel Pers Commun. 2022;125:1–18.

    Article  Google Scholar 

  51. Dissanayake T, Fernando T, Denman S, Sridharan S, Fookes C. Geometric deep learning for subject independent epileptic seizure prediction using scalp EEG signals. IEEE J Biomed Health Inform. 2021;26(2):527–38.

    Article  Google Scholar 

  52. Van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(11):2579–605.

    Google Scholar 

Download references

Funding

This work was supported by the Key Research and Development Program of China under Grant 2022YFC3601600 and 2021YFC2009400, in part by the National Natural Science Foundation of China (NSFC) under Grant 61876194, in part by the Province Natural Science Foundation of Guangdong under Grant 2021A1515011897, in part by the Key Research and Development Program of Guangzhou under Grant 202206010028, in part by the Fundamental Research Funds for the Central Universities, Sun Yat-sen University 23ptpy119.

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510006, Guangdong, China

    Yao Wang

  2. Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China

    Yufei Shi, Zhipeng He & Yi Zhou

  3. Department of Neurology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China

    Ziyi Chen

Authors
  1. Yao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yufei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhipeng He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ziyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yi Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Zhou.

Ethics declarations

Conflict of interest

The authors confirm that there are no conflict of interest.

Ethical approval

The private dataset of this study involving human participants were reviewed and approved by Medical Ethics Committee of First Affiliated Hospital, Sun Yat-sen University. The patients provided their written informed consent to participate in this study.

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

Wang, Y., Shi, Y., He, Z. et al. Combining temporal and spatial attention for seizure prediction. Health Inf Sci Syst 11, 38 (2023). https://doi.org/10.1007/s13755-023-00239-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13755-023-00239-6

Keywords

Search

Navigation