Research progress of epileptic seizure predictions based on electroencephalogram signals_Journal of Biomedical Engineering

Authors：

HAN Changming ^1,2 ,  PENG Fulai ² , CHEN Cai ² , LI Wenchao ² , ZHANG Xikun ² , WANG Xingwei ² ,  ZHOU Weidong ¹

1. School of Microelectronics, Shandong University, Jinan 250101, P.R.China;
2. Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250000, P.R.China;

Corresponding author：

PENG Fulai, Email: pengfulai1112@163.com

Keywords：

epilepsy; seizure prediction; electroencephalogram signals; machine learning; deep learning

DOI：

10.7507/1001-5515.202105052

Video：

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Abstract Full text Figures/Tables Video References Cited by

As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.

Citation： HAN Changming, PENG Fulai, CHEN Cai, LI Wenchao, ZHANG Xikun, WANG Xingwei, ZHOU Weidong. Research progress of epileptic seizure predictions based on electroencephalogram signals. Journal of Biomedical Engineering, 2021, 38(6): 1193-1202. doi: 10.7507/1001-5515.202105052 Copy

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4. 葛燕, 刘崇, 孟凡刚, 等. 脑深部电刺激在癫痫治疗中的应用进展. 中华医学杂志, 2013(7): 558-559.
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6. 覃小雅, 袁媛, 陈彦, 等. 头皮脑电图在迷走神经电刺激治疗难治性癫痫研究中的应用. 生物医学工程学杂志, 2020, 37(4): 699-707.
7. 黄莎, 肖波, 冯莉, 等. 187例成人睡眠相关性癫痫患者的临床特征及视频脑电图分析. 癫痫杂志, 2019, 5(1): 16-20.
8. 金洋, 张玮, 徐斌, 等. 发作期头皮电极脑电图在局灶性癫痫诊断中的价值. 癫痫杂志, 2019, 5(6): 431-439.
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12. Mahmoodian N, Haddadnia J, Illanes A, et al. Seizure prediction with cross-higher-order spectral analysis of EEG signals. Signal Image Video P, 2020, 14(4): 821-828.
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18. Zandi A S, Tafreshi R, Javidan M, et al. 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): 1401-1413.
19. Li F L, Liang Y, Zhang L Y, 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): 175-181.
20. Muhlberg K, Muller J, Tetzlaff R. Seizure prediction by multivariate autoregressive model order optimization. Current Directions in Biomedical Engineering, 2018, 4(1): 395-398.
21. Osman A H, Alzahrani A A. New approach for automated epileptic disease diagnosis using an integrated self-organization map and radial basis function neural network algorithm. IEEE Access, 2019, 7: 4741-4747.
22. Daoud H, Williams P, Bayoumi M, et al. IoT based efficient epileptic seizure prediction system using deep learning// 6th IEEE Virtual World Forum on Internet of Things (IEEE WF-IoT). New Orleans: IEEE, 2020: 1-6.
23. Zhang R J, Jiang X Y, Dai C Y, et al. Tensor-based uncorrelated multilinear discriminant analysis for epileptic seizure prediction// 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Montreal: IEEE, 2020: 541-544.
24. Naftulin J S, Ahmed O J, Piantoni G, et al. Ictal and preictal power changes outside of the seizure focus correlate with seizure generalization. Epilepsia, 2018, 59(7): 1398-1409.
25. Kitano L A S, Sousa M A A, Santos S D, et al. Epileptic seizure prediction from EEG signals using unsupervised learning and a polling-based decision process// 27th International Conference on Artificial Neural Networks (ICANN). Rhodes: ENNS, 2018: 117-126.
26. Raghu S, Sriraam N, Rao S V, et al. Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG. Neural Comput Appl, 2020, 32(13): 8965-8984.
27. Mahmoodian N, Boese A, Friebe M, et al. Epileptic seizure detection using cross-bispectrum of electroencephalogram signal. Seizure-Eur J Epilep, 2019, 66: 4-11.
28. Liu Xuefei, Li Jinbao, Shu Minglei. Epileptic seizure prediction based on region correlation of EEG signal// 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Proceedings. Rochester: IEEE, 2020: 120-125.
29. Zhang Q Z, Hu Y J, Potter T, et al. Establishing functional brain networks using a nonlinear partial directed coherence method to predict epileptic seizures. J Neurosci Methods, 2020, 329: 108447.
30. Yuan Q, Wei D M. A seizure prediction method based on efficient features and BLDA// IEEE International Conference on Digital Signal Processing (DSP). Singapore: IEEE, 2015: 177-181.
31. Usman S M, Latif S, Beg A. Principle components analysis for seizures prediction using wavelet transform. Int J Adv Appl Sci, 2019, 6(3): 50-55.
32. Chen S N, Zhang X, Chen L L, et al. Automatic diagnosis of epileptic seizure in electroencephalography signals using nonlinear dynamics features. IEEE Access, 2019, 7: 61046-61056.
33. Alickovic E, Kevric J, Subasi A. Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed Signal Proces, 2018, 39: 94-102.
34. Truong N D, Nguyen A D, Kuhlmann L, et al. A generalised seizure prediction with convolutional neural networks for intracranial and scalp electroencephalogram data analysis. arXiv, 2017: 1707.01976.
35. 单绍杰, 李汉军, 王璐璐, 等. 基于LSTM模型的单导联脑电癫痫发作预测. 计算机应用研究, 2018, 35(11): 3251-3254.
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Research progress of epileptic seizure predictions based on electroencephalogram signals

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