Abstract
Accurate automated analysis of electroencephalography (EEG) would largely help clinicians effectively monitor and diagnose patients with various brain diseases. Compared to supervised learning with labelled disease EEG data which can train a model to analyze specific diseases but would fail to monitor previously unseen statuses, anomaly detection based on only normal EEGs can detect any potential anomaly in new EEGs. Different from existing anomaly detection strategies which do not consider any property of unavailable abnormal data during model development, a task-oriented self-supervised learning approach is proposed here which makes use of available normal EEGs and expert knowledge about abnormal EEGs to train a more effective feature extractor for the subsequent development of anomaly detector. In addition, a specific two-branch convolutional neural network with larger kernels is designed as the feature extractor such that it can more easily extract both larger-scale and small-scale features which often appear in unavailable abnormal EEGs. The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs for development of anomaly detector based on normal data and future anomaly detection for new EEGs, as demonstrated on three EEG datasets. The code is available at https://github.com/ironing/EEG-AD.
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References
Achilles, F., Tombari, F., Belagiannis, V., Loesch, A.M., Noachtar, S., Navab, N.: Convolutional neural networks for real-time epileptic seizure detection. Comput. Meth. Biomech. Biomed. Eng. Imaging Vis. 6, 264–269 (2018)
Alturki, F.A., AlSharabi, K., Abdurraqeeb, A.M., Aljalal, M.: EEG signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques. Sensors 20(9), 2505 (2020)
Audibert, J., Michiardi, P., Guyard, F., Marti, S., Zuluaga, M.A.: USAD: unsupervised anomaly detection on multivariate time series. In: KDD, pp. 3395–3404 (2020)
Chalapathy, R., Menon, A.K., Chawla, S.: Anomaly detection using one-class neural networks. arXiv preprint arXiv:1802.06360 (2018)
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: Self-supervised learning for medical image analysis using image context restoration. Med. Image Anal. 58, 101539 (2019)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML, pp. 1597–1607 (2020)
Chen, Z., Yeo, C.K., Lee, B.S., Lau, C.T.: Autoencoder-based network anomaly detection. In: WTS (2018)
Craley, J., Johnson, E., Venkataraman, A.: A novel method for epileptic seizure detection using coupled hidden Markov models. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 482–489. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_55
Dai, G., Zhou, J., Huang, J., Wang, N.: HS-CNN: a CNN with hybrid convolution scale for motor imagery classification. J. Neural Eng. 17, 016025 (2020)
Dhar, P., Garg, V.K.: Brain-related diseases and role of electroencephalography (EEG) in diagnosing brain disorders. In: ICT Analysis and Applications, pp. 317–326 (2021)
Fiest, K.M., et al.: Prevalence and incidence of epilepsy: a systematic review and meta-analysis of international studies. Neurology 88(3), 296–303 (2017)
Gemein, L.A., et al.: Machine-learning-based diagnostics of EEG pathology. Neuroimage 220, 117021 (2020)
Jia, W., Shukla, R.M., Sengupta, S.: Anomaly detection using supervised learning and multiple statistical methods. In: ICML (2019)
Li, C.L., Sohn, K., Yoon, J., Pfister, T.: CutPaste: self-supervised learning for anomaly detection and localization. In: CVPR, pp. 9664–9674 (2021)
Li, Z., et al.: Superpixel masking and inpainting for self-supervised anomaly detection. In: BMVC (2020)
Megiddo, I., Colson, A., Chisholm, D., Dua, T., Nandi, A., Laxminarayan, R.: Health and economic benefits of public financing of epilepsy treatment in India: an agent-based simulation model. Epilepsia 57, 464–474 (2016)
Pérez-García, F., Scott, C., Sparks, R., Diehl, B., Ourselin, S.: Transfer learning of deep spatiotemporal networks to model arbitrarily long videos of seizures. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 334–344. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_32
Rippel, O., Mertens, P., Merhof, D.: Modeling the distribution of normal data in pre-trained deep features for anomaly detection. In: ICPR (2021)
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019)
Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. In: NeurIPS (1999)
Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. Ph.D. thesis, Massachusetts Institute of Technology (2009)
Shvetsova, N., Bakker, B., Fedulova, I., Schulz, H., Dylov, D.V.: Anomaly detection in medical imaging with deep perceptual autoencoders. IEEE Access 9, 118571–118583 (2021)
Temko, A., Sarkar, A., Lightbody, G.: Detection of seizures in intracranial EEG: UPenn and Mayo clinic’s seizure detection challenge. In: EMBC, pp. 6582–6585 (2015)
Tian, Y., et al.: Constrained contrastive distribution learning for unsupervised anomaly detection and localisation in medical images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 128–140. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_13
Wang, J., Cherian, A.: GODS: generalized one-class discriminative subspaces for anomaly detection. In: CVPR, pp. 8201–8211 (2019)
Xu, J., Zheng, Y., Mao, Y., Wang, R., Zheng, W.S.: Anomaly detection on electroencephalography with self-supervised learning. In: BIBM (2020)
Zavrtanik, V., Kristan, M., Skočaj, D.: Reconstruction by inpainting for visual anomaly detection. Pattern Recogn. 112, 107706 (2021)
Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H., Chawla, N.V.: A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In: AAAI, pp. 1409–1416 (2019)
Acknowledgments
This work is supported by NSFCs (No. 62071502, U1811461), the Guangdong Key Research and Development Program (No. 2020B1111190001), and the Meizhou Science and Technology Program (No. 2019A0102005).
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Zheng, Y., Liu, Z., Mo, R., Chen, Z., Zheng, Ws., Wang, R. (2022). Task-Oriented Self-supervised Learning for Anomaly Detection in Electroencephalography. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_19
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