Abstract
Intracranial electroencephalography (iEEG) is of great importance for the preoperative evaluation of drug-resistant epilepsy. Automatic classification of iEEG signals can speed up the process of epilepsy diagnosis. Existing deep learning-based approaches for iEEG signal classification usually rely on convolutional neural network (CNN) and long short-term memory network. However, these approaches have limitations in terms of classification accuracy. In this study, we propose a CNN and Transformer based method, which is named as IEEG-CT, for iEEG signal classification. Firstly, IEEG-CT utilizes deep one-dimensional CNN to extract the critical local features from the raw iEEG signals. Secondly, IEEG-CT combines a Transformer encoder, which employs a multi-head attention mechanism to capture long-range global information among the extracted features. In particular, we leverage a causal convolution multi-head attention instead of the standard Transformer block to efficiently capture the temporal dependencies within the input features. Finally, the obtained global features by the Transformer encoder are employed for the classification. We assess the performance of IEEG-CT on two publicly available multicenter iEEG datasets. According to the experimental results, IEEG-CT surpasses state-of-the-art techniques in terms of several evaluation metrics, i.e., accuracy, AUROC, and AUPRC.
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References
Thijs, R.D., Surges, R., O’Brien, T.J., Sander, J.W.: Epilepsy in adults. Lancet 393(10172), 689–701 (2019)
Wiebe, S., Jette, N.: Pharmacoresistance and the role of surgery in difficult to treat epilepsy. Nat. Rev. Neurol. 8(12), 669–677 (2012)
Wang, Y., Yan, J., Wen, J., Yu, T., Li, X.: An intracranial electroencephalography (iEEG) brain function mapping tool with an application to epilepsy surgery evaluation. Front. Neuroinform. 10, 15 (2016)
Urrestarazu, E., Jirsch, J.D., LeVan, P., Hall, J.: High-frequency intracerebral EEG activity (100–500 Hz) following interictal spikes. Epilepsia 47(9), 1465–1476 (2006)
Nejedly, P., Cimbalnik, J., Klimes, P., Plesinger, F., Halamek, J., et al.: Intracerebral EEG artifact identification using convolutional neural networks. Neuroinformatics 17, 225–234 (2019)
Sui, L., Zhao, X., Zhao, Q., Tanaka, T., Cao, J.: Localization of epileptic foci by using convolutional neural network based on iEEG. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2019. IAICT, vol. 559, pp. 331–339. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19823-7_27
Guo, J., Wang, Y., Yang, Y., Kang, G.: IEEG-TCN: a concise and robust temporal convolutional network for intracranial electroencephalogram signal identification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 668–673. IEEE (2021)
Wang, Y., et al.: Computer-aided intracranial EEG signal identification method based on a multi-branch deep learning fusion model and clinical validation. Brain Sci. 11(5), 615 (2021)
Nejedly, P., et al.: Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals. Sci. data 7(1), 179 (2020)
Wang, Y., et al.: SEEG-Net: an explainable and deep learning-based cross-subject pathological activity detection method for drug-resistant epilepsy. Comput. Biol. Med. 148, 105703 (2022)
Jiang, W.B., Yan, X., Zheng, W.L., Lu, B.L.: Elastic graph transformer networks for EEG-based emotion recognition. In: 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE (2023)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Manzari, O.N., Ahmadabadi, H., Kashiani, H., Shokouhi, S.B., Ayatollahi, A.: MedViT: a robust vision transformer for generalized medical image classification. Comput. Biol. Med. 157, 106791 (2023)
Sun, Y.: Continuous seizure detection based on transformer and long-term iEEG. IEEE J. Biomed. Health Inform. 26(11), 5418–5427 (2022)
Xu, M., Zhou, W., Shen, X., Wang, Y., Mo, L., Qiu, J.: Swin-TCNet: swin-based temporal-channel cascade network for motor imagery iEEG signal recognition. Biomed. Signal Process. Control 85, 104885 (2023)
Peh, W.Y., Thangavel, P., Yao, Y., Thomas, J., Tan, Y.L., Dauwels, J.: Multi-center assessment of CNN-transformer with belief matching loss for patient-independent seizure detection in scalp and intracranial EEG. arXiv preprint arXiv:2208.00025 (2022)
Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31(7), 1235–1270 (2019)
Zhu, X., Li, L., Liu, J., Peng, H., Niu, X.: Captioning transformer with stacked attention modules. Appl. Sci. 8(5), 739 (2018)
Eldele, E., et al.: An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 809–818 (2021)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations (ICLR 2015). Computational and Biological Learning Society (2015)
Van Den Oord, A., et al.: WaveNet: a generative model for raw audio. In: 9th ISCA Speech Synthesis Workshop, pp. 125–125 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)
Bandos, A.I., Rockette, H.E., Song, T., Gur, D.: Area under the free-response ROC curve (FROC) and a related summary index. Biometrics 65(1), 247–256 (2009)
Acknowledgements.
This work is supported by the Natural Science Foundation of Shandong Province, China, under Grant ZR2019MF071, and the Project of Shandong Province Higher Educational Science and Technology Program, China, under Grant J16LN05.
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Yu, M., Zhang, Y., Liu, H., Wu, X., Du, M., Liu, X. (2024). IEEG-CT: A CNN and Transformer Based Method for Intracranial EEG Signal Classification. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_41
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