Abstract
A reliable seizure prediction system has important implications for improving the quality of epileptic patients’ life and opening new therapeutic possibilities for human health. In this paper, a new method combining local mean decomposition (LMD) and convolutional neural network (CNN) is proposed for seizure prediction. Firstly, the LMD is employed to decompose the raw EEG signals into a string of product functions (PFs). Subsequently, three PFs (PF2–PF4) are selected to learn the EEG features automatically using the deep CNN. In order to obtain the most important information from the features extracted by the CNN, the principal components analysis is applied to remove the redundant features. After that, these features are fed into the Bayesian linear discriminant analysis for classifying the cerebral state as interictal or preictal. The proposed method achieves a sensitivity of 87.7% with the false prediction rate of 0.25/h using intracranial EEG signals of 21 patients from a publicly available EEG dataset. The experimental results suggest that the proposed method can become a potential approach for predicting the impending seizures in clinical application.
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References
Parvez MZ, Paul M (2017) Seizure prediction using undulated global and local features. IEEE Trans Biomed Eng 64(1):208–217
Varatharajah Y, Iyer RK, Berry BM, Worrell GA, Brinkmann BH (2017) seizure forecasting and the preictal state in canine epilepsy. Int J Neural Syst 27(1):1650046
Aarabi A, He B (2017) Seizure prediction in patients with focal hippocampal epilepsy. Clin Neurophysiol 128(7):1299–1307
Zheng Y, Wang G, Li K, Bao G, Wang J (2014) Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition. Clin Neurophysiol 125(6):1104–1111
Bandarabadi M, Teixeira CA, Rasekhi J, Dourado A (2015) Epileptic seizure prediction using relative spectral power features. Clin Neurophysiol 126(2):237–248
Li S, Zhou W, Yuan Q, Liu Y (2013) Seizure prediction using spike rate of intracranial EEG. IEEE Trans Neural Syst Rehab Eng 21(6):880–886
Yuan S, Zhou W, Chen L (2018) Epileptic seizure prediction using diffusion distance and bayesian linear discriminate analysis on intracranial EEG. Int J Neural Syst 28(01):1750043
Aarabi A, He B (2014) Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach. Clin Neurophysiol 125(5):930–940
Mas K, Amirsalari S, Haidari MR (2017) Analysis of variations of correlation dimension and nonlinear interdependence for the prediction of pediatric myoclonic seizures—a preliminary study. Epilepsy Res 135:102–114
Ulate-Campos A, Coughlin F, Gainza-Lein M, Fernández IS, Pearl P, Loddenkemper T (2016) Automated seizure detection systems and their effectiveness for each type of seizure. Seizure 40:88–101
Wang L, Long X, Arends JB, Aarts RM (2017) EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures. J Neurosci Methods 290:85–94
Orosco L, Correa AG, Diez P, Laciar E (2016) Patient non-specific algorithm for seizures detection in scalp EEG. Comput Biol Med 71:128–134
Mathieson S, Rennie J, Livingstone V, Temko A, Low E, Pressler R, Boylan G (2016) In-depth performance analysis of an EEG based neonatal seizure detection algorithm. Clin Neurophysiol 127:2246–2256
Zhang Z, Chen Z, Zhou Y, Du S, Zhang Y, Mei T, Tian X (2014) Construction of rules for seizure prediction based on approximate entropy. Clin Neurophysiol 125(10):1959–1966
Li S, Zhou W, Yuan Q, Geng S, Cai D (2013) Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 43(7):807–816
Smith JS (2005) The local mean decomposition and its application to EEG perception data. J R Soc Interface 2(5):443–454
Xie L, Lang X, Chen J, Horch A, Su H (2016) Time-varying oscillation detector based on improved LMD and robust Lempel-Ziv complexity. Control Eng Pract 51:48–57
Barat C, Ducottet C (2016) String representations and distances in deep convolutional neural networks for image classification. Pattern Recogn 54:104–115
Zhong Z, Lei M, Cao D, Fan J, Li S (2017) Class-specific object proposals re-ranking for object detection in automatic driving. Neurocomputing 242:187–194
Wu G, Lu W, Gao G, Zhao C, Liu J (2016) Regional deep learning model for visual tracking. Neurocomputing 175:310–323
Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3):211–252
Long J, Shelhamer E, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651
He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916
Shaham U, Lederman RR (2018) Learning by coincidence: Siamese networks and common variable learning. Pattern Recogn 74:52–63
Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35(5):1207–1216
Hoffmann U, Vesin J-M, Ebrahimi T, Diserens K (2008) An efficient P300-based brain–computer interface for disabled subjects. J Neurosci Methods 167(1):115–125
Yuan S, Zhou W, Yuan Q, Zhang Y, Meng Q (2014) Automatic seizure detection using diffusion distance and BLDA in intracranial EEG. Epilepsy Behav 31:339–345
Zhang Y, Zhou W, Yuan Q, Wu Q (2014) A low computation cost method for seizure prediction. Epilepsy Res 108(8):1357–1366
Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G (2017) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377
Hoffmann U, Vesin JM, Ebrahimi T, Diserens K (2008) An efficient P300-based brain–computer interface for disabled subjects. J Neurosci Methods 167:115–125
Snyder DE, Echauz J, Grimes DB, Litt B (2008) The statistics of a practical seizure warning system. J Neural Eng 5(4):392–401
Winterhalder M, Maiwald T, Voss H, Aschenbrenner-Scheibe R, Timmer J, Schulze-Bonhage A (2003) The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy Behav 4(3):318–325
Winterhalder M, Schelter B, Maiwald T, Brandt A, Schad A, Schulze-Bonhage A, Timmer J (2006) Spatio-temporal patient–individual assessment of synchronization changes for epileptic seizure prediction. Clin Neurophysiol 117(11):2399–2413
Aarabi A, He B (2012) A rule-based seizure prediction method for focal neocortical epilepsy. Clin Neurophysiol 123(6):1111–1122
Acknowledgements
This work was jointly supported by National Natural Science Foundation of China (Nos. 61501283, 61701279, 61701270 and 61401259), Shandong Provincial Natural Science Foundation (Nos. ZR2015PF012, ZR2017LH049 and ZR2017PF006), and China Postdoctoral Science Foundation (Nos. 2015M582129, 2017M622219 and 2015M582128).
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Yu, Z., Nie, W., Zhou, W. et al. Epileptic seizure prediction based on local mean decomposition and deep convolutional neural network. J Supercomput 76, 3462–3476 (2020). https://doi.org/10.1007/s11227-018-2600-6
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DOI: https://doi.org/10.1007/s11227-018-2600-6