Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain

Biomedical Signal Processing and Control - Tập 53 - Trang 101551 - 2019
Zuochen Wei1, Junzhong Zou1, Jian Zhang1, Jianqiang Xu1
1Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China

Tài liệu tham khảo

World Health Organization, 2017, http://www.who.int/en/news-room/fact-sheets/detail/epilepsy. Wei, 2017, Automatic recognition of epileptic discharges based on shape similarity in time-domain, Biomed. Signal Process. Control, 33, 236, 10.1016/j.bspc.2016.12.007 Ahmad, 2017, Seizure Detection using EEG: A survey of dierent Techniques Zhang, 2013, Automatic detection of interictal epileptiform discharges based on time-series sequence merging method, Neurocomputing, 110, 35, 10.1016/j.neucom.2012.11.017 Faust, 2015, Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis, Seizure, 26, 56, 10.1016/j.seizure.2015.01.012 Zhang, 2015, Seizure detection method based on fractal dimension and gradient boosting, Epilepsy Behav., 43, 30, 10.1016/j.yebeh.2014.11.025 Li, 2018, Detection of epileptic seizure based on entropy analysis of short-term EEG, PloS one, 13, e0193691, 10.1371/journal.pone.0193691 Donos, 2015, Early seizure detection algorithm based on intracranial EEG and random forest classification, Int. J. Neural Syst., 25, 1550023, 10.1142/S0129065715500239 Shoeb, 2010, Application of machine learning to epileptic seizure onset detection Lin, 2017, A novel framework based on biclustering for automatic epileptic seizure detection, Int. J. Mach. Learn. Cybernet., 10, 1 Zhang, 2017, LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM, IEEE Trans. Neural Syst. Rehab. Eng., 25, 1100, 10.1109/TNSRE.2016.2611601 Bhattacharyya, 2018, A novel approach for automated detection of focal EEG signals using empirical wavelet transform, 29, 47 Zhang, 2018, Generalized Stockwell transform and SVD-based epileptic seizure detection in EEG using random forest, Biocyber. Biomed. Eng., 38, 519, 10.1016/j.bbe.2018.03.007 Alotaiby, 2017, Epileptic seizure prediction using CSP and LDA for scalp EEG signals, Comput. Intell. Neurosci., 6, 1, 10.1155/2017/1240323 Sharmila, 2018, Eect of filtering with time domain features for the detection of epileptic seizure from EEG signals, J. Med. Eng. Technol., 42, 217, 10.1080/03091902.2018.1464075 Ahmad, 2017, Seizure Detection using EEG: A survey of different Techniques LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Thodoroff, 2016, Learning robust features using deep learning for automatic seizure detection, Proc. Mach. Learn. Healthc., 56 Antoniades, 2016, Deep learning for epileptic intracranial EEG data, 2016 IEEE International Workshop on Machine Learning for Signal Processing, 10.1109/MLSP.2016.7738824 Truong, 2017 Acharya, 2017, Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, Comput. Biol. Med. Samiee, 2017, Epileptic seizure detection in long-term EEG records using sparse rational decomposition and local Gabor binary patterns feature extraction, Knowl.-Based Syst., 118, 228, 10.1016/j.knosys.2016.11.023 Khan, 2017, Focal onset seizure prediction using convolutional networks, IEEE Trans. Biomed. Eng., 99, 1 Tsiouris, 2018, A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals, Comput. Biol. Med., 10.1016/j.compbiomed.2018.05.019 Goodfellow, 2014, Generative adversarial nets, Adv. Neural Inf. Process. Syst., 2672 CHB-MIT Scalp EEG Database, https://www.physionet.org/pn6/chbmit/. Goldberger, 2000, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals, Circulation, 101, e215, 10.1161/01.CIR.101.23.e215 Shoeb, 2009 Khalid, 2015, Ensemble classifier for epileptic seizure detection for imperfect EEG data, Sci. World J., 1 Wei, 2016, Automatic recognition of chewing noises in epileptic EEG based on period segmentation, Neurocomputing, 190, 107, 10.1016/j.neucom.2016.01.029 Lee, 2017, Deep learning in medical imaging: general overview, Korean J. Radiol., 18, 570, 10.3348/kjr.2017.18.4.570 He, 2015, Delving Deep into Rectiers: Surpassing Human-Level Performance on Image Net Classification, arXiv preprint arXiv Gao, 2017, On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning, arXiv preprint arXiv Kingma, 2014 Arjovsky, 2017 Arjovsky, 2017 Piccoli, 2016, On Properties of the Generalized Wasserstein Distance, Arch. Ration. Mech. Anal., 222, 1339, 10.1007/s00205-016-1026-7 Gulrajani, 2017, Improved training of wasserstein gans, Adv. Neural Inf. Process. Systems, 5769 Radford, 2015 He, 2015, Delving deep into rectifiers: surpassing human-level performance on imagenet classification, Proceedings of the IEEE International Conference on Computer Vision, 1026 Arcos-Garca, 2018, Deep neural network for trac sign recognition systems: an analysis of spatial transformers and stochastic optimisation methods, Neural Networks, 99, 158, 10.1016/j.neunet.2018.01.005 Arunkumar, 2017, Classification of focal and non focal EEG using entropies, Pattern Recognit. Lett., 94, 112, 10.1016/j.patrec.2017.05.007 Cho, 2017, EEG-based prediction of epileptic seizures using phase synchronization elicited from noise-assisted multivariate empirical mode decomposition, IEEE Trans. Neural Syst. Rehabil. Eng., 25, 1309, 10.1109/TNSRE.2016.2618937 Samiee, 2015, Long-term epileptic EEG classification via 2D mapping and textural features, Expert Syst. Appl., 42, 7175, 10.1016/j.eswa.2015.05.002