Classification of patterns of EEG synchronization for seizure prediction
Abstract
Objective Research in seizure prediction from intracranial EEG has highlighted the usefulness of bivariate measures of brainwave synchronization. Spatio-temporal bivariate features are very high-dimensional and cannot be analyzed with conventional statistical methods. Hence, we propose state-of-the-art machine learning methods that handle high-dimensional inputs.Methods We computed bivariate features of EEG synchronization (cross-correlation, nonlinear interdependence, dynamical entrainment or wavelet synchrony) on the 21-patient Freiburg dataset. Features from all channel pairs and frequencies were aggregated over consecutive time points, to form patterns. Patient-specific machine learning-based classifiers (support vector machines, logistic regression or convolutional neural networks) were trained to discriminate interictal from preictal patterns of features. In this explorative study, we evaluated out-of-sample seizure prediction performance, and compared each combination of feature type and classifier.Results Among the evaluated methods, convolutional networks combined with wavelet coherence successfully predicted all out-of-sample seizures, without false alarms, on 15 patients, yielding 71% sensitivity and 0 false positives.Conclusions Our best machine learning technique applied to spatio-temporal patterns of EEG synchronization outperformed previous seizure prediction methods on the Freiburg dataset.Significance By learning spatio-temporal dynamics of EEG synchronization, pattern recognition could capture patient-specific seizure precursors. Further investigation on additional datasets should include the seizure prediction horizon.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Self-citationRegression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · see citing works
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459 · cited 2× · see citing works
“Even if not all the variables of a chaotic system are observed, one can theoretically reconstruct the original chaotic attractor, thanks to time-delay embedding of the time series of the limited subset of observed variables, assuming the right embedding dimension and time delay (Takens, 1981).”
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,124 · see citing works
The Nature of Statistical Learning Theory
Sain, S. R., & Vapnik, V. N. (1996) · Technometrics · doi:10.2307/1271324 · Cited by 4,097 · see citing works
Neural Networks: Tricks of the Trade: Second Edition
(2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8 · Cited by 2,116 · see citing works
Advances in Neural Information Processing Systems 19 (NIPS 2006)
Simonotto, E., Whalley, H. C., Lawrie, S. M., Murray, L. M., McGonigle, D. J., & Storkey, A. (2006) · Cited by 1,651 · see citing works
Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field
Stam, C. J. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2005.06.011 · Cited by 1,410 · see citing works
LeCun, Y., Bottou, L., Orr, G., & Müller, K. (1998) · neural information processing systems · Cited by 1,333 · see citing works
Self-citationSeizure prediction: the long and winding road
Mormann, F., Andrzejak, R. G., Elger, C. E., & Lehnertz, K. (2006) · Brain · doi:10.1093/brain/awl241 · Cited by 1,112 · see citing works
Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony
Quyen, M. L. V., Foucher, J., Lachaux, J.-P., Rodríguez, E., Antoine Lutz, A., Martinerie, J. M., & Varela, F. J. (2001) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(01)00372-7 · Cited by 885 · see citing works
Neural Networks: Tricks of the Trade
Orr, G., & Müller, K.-R. (1998) · Lecture notes in computer science · doi:10.1007/3-540-49430-8 · Cited by 817 · see citing works
Prediction of epileptic seizures
Litt, B., & Echauz, J. (2002) · The Lancet Neurology · doi:10.1016/s1474-4422(02)00003-0 · Cited by 527 · see citing works
On the predictability of epileptic seizures
Mormann, F., Kreuz, T., Rieke, C., Andrzejak, R. G., Kraskov, A., David, P., Elger, C. E., & Lehnertz, K. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.08.025 · Cited by 489 · see citing works
(2005) · neural information processing systems · Cited by 446 · see citing works
Self-citationA robust method for detecting interdependences: application to intracranially recorded EEG
Arnhold, J., Grassberger, P., Lehnertz, K., & Elger, C. E. (1999) · Physica D Nonlinear Phenomena · doi:10.1016/s0167-2789(99)00140-2 · Cited by 441 · see citing works
A Robust Method for Detecting Interdependences: Application to Intracranially Recorded EEGPreprint
Arnhold, J., Grassberger, P., Lehnertz, K., & Elger, C. E. (2013) · Cited by 412 · see citing works
Maiwald, T., Matthias Winterhalder, A., Aschenbrenner-Scheibe, R., Voss, H. U., Schulze‐Bonhage, A., & Timmer, J. (2004) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2004.02.013 · Cited by 299 · see citing works
D’Alessandro, M., Esteller, R., Vachtsevanos, G., Hinson, A., Echauz, J., & Litt, B. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.810706 · Cited by 270 · see citing works
Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG
Petrosian, A. A., Prokhorov, D. V., Homan, R. W., Dasheiff, R. M., & Wunsch, D. C. (2000) · Neurocomputing · doi:10.1016/s0925-2312(99)00126-5 · Cited by 269 · see citing works
Matthias Winterhalder, A., Maiwald, T., Voss, H. U., Aschenbrenner-Scheibe, R., Timmer, J., & Schulze‐Bonhage, A. (2003) · Epilepsy & Behavior · doi:10.1016/s1525-5050(03)00105-7 · Cited by 237 · see citing works
Long-term prospective on-line real-time seizure prediction
Iasemidis, L. D., Shiau, D., Pardalos, P. M., Chaovalitwongse, W. A., Narayanan, K., Prasad, A., Tsakalis, K. S., Carney, P. R., & Sackellares, J. C. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.10.013 · Cited by 197 · see citing works
Preictal state identification by synchronization changes in long-term intracranial EEG recordings
Quyen, M. L. V., Soss, J. R., Navarro, V., Robertson, R., Chávez, M., Baulac, M., & Martinerie, J. M. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.10.014 · Cited by 191 · see citing works
Schelter, B. O., Matthias Winterhalder, A., Maiwald, T., Brandt, A., Schad, A., Schulze‐Bonhage, A., & Timmer, J. (2006) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.2137623 · Cited by 182 · see citing works
How well can epileptic seizures be predicted? An evaluation of a nonlinear method
Aschenbrenner-Scheibe, R. (2003) · Brain · doi:10.1093/brain/awg265 · Cited by 162 · see citing works
Testing the null hypothesis of the nonexistence of a preseizure state
Andrzejak, R. G., Mormann, F., Kreuz, T., Rieke, C., Kraskov, A., Elger, C. E., & Lehnertz, K. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.67.010901 · Cited by 136 · see citing works
The First International Collaborative Workshop on Seizure Prediction: summary and data description
Lehnertz, K., & Litt, B. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.08.020 · Cited by 132 · see citing works
Rajna, P., Clemens, B., Csibri, É., Dobos, E., Geregely, A., Gottschal, M., György, I., Horváth, Á., Horváth, F., Mezőfi, L., et al. (1997) · Seizure · doi:10.1016/s1059-1311(97)80035-0 · Cited by 113 · see citing works
Schelter, B. O., Matthias Winterhalder, A., Maiwald, T., Brandt, A., Schad, A., Timmer, J., & Schulze‐Bonhage, A. (2006) · Epilepsia · doi:10.1111/j.1528-1167.2006.00848.x · Cited by 105 · see citing works
Toward a Neurodynamical Understanding of Ictogenesis
Quyen, M. L. V., Navarro, V., Martinerie, J. M., Baulac, M., & Varela, F. J. (2003) · Epilepsia · doi:10.1111/j.0013-9580.2003.12007.x · Cited by 91 · see citing works
Continuous energy variation during the seizure cycle: towards an on-line accumulated energy
Esteller, R., Echauz, J., D’Alessandro, M., Worrell, G. A., Cranstoun, S., Vachtsevanos, G., & Litt, B. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.10.015 · Cited by 91 · see citing works
A multi-feature and multi-channel univariate selection process for seizure prediction
D’Alessandro, M., Vachtsevanos, G., Esteller, R., Echauz, J., Cranstoun, S. D., Worrell, G. A., Parish, L. M., & Litt, B. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.11.014 · Cited by 88 · see citing works
State-of-the-Art of Seizure Prediction
Lehnertz, K., Mormann, F., Osterhage, H., Müller, A., Prusseit, J., Chernihovskyi, A., Staniek, M., Krug, D., Bialonski, S., & Elger, C. E. (2007) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e3180336f16 · Cited by 87 · see citing works
Measurement and Quantification of Spatiotemporal Dynamics of Human Epileptic Seizures
(2009) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470545379.ch12 · Cited by 80 · see citing works
Schulze‐Bonhage, A., Kurth, C., Carius, A., Steinhoff, B. J., & Mayer, T. (2006) · Epilepsy Research · doi:10.1016/j.eplepsyres.2006.02.001 · Cited by 79 · see citing works
Seizure anticipation: from algorithms to clinical practice
Mormann, F., Elger, C. E., & Lehnertz, K. (2006) · Current Opinion in Neurology · doi:10.1097/01.wco.0000218237.52593.bc · Cited by 74 · see citing works
Harrison, M. A. F., Frei, M. G., & Osorio, I. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.08.022 · Cited by 54 · see citing works
Signal complexity and synchrony of epileptic seizures: is there an identifiable preictal period?
Jouny, C. C., Franaszczuk, P. J., & Bergey, G. K. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.08.024 · Cited by 48 · see citing works
Multivariate linear discrimination of seizures
Jerger, K. K., Weinstein, S. L., SAUER, T. D., & Schiff, S. J. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.08.023 · Cited by 44 · see citing works
Mirowski, P., Madhavan, D., & LeCun, Y. (2007) · Cited by 19 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115 · see citing works
Deep learning in bioinformatics
Min, S., Lee, B., & Yoon, S. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw068 · Cited by 1,514 · see citing works
Applications of Deep Learning and Reinforcement Learning to Biological Data
Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2790388 · Cited by 852 · see citing works
Tsiouris, Κ. Μ., Pezoulas, V. C., Zervakis, M. E., Konitsiotis, S., Koutsouris, D., & Fotiadis, D. I. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.019 · Cited by 646 · see citing works
Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., & Gramfort, A. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2813138 · Cited by 632 · see citing works
Deep Learning and Its Applications in Biomedicine
Cao, C., Liu, F., Tan, H., Song, D., Shu, W., Li, W., Zhou, Y., Bo, X., & Xie, Z. (2018) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2017.07.003 · Cited by 625 · see citing works
An automated system for epilepsy detection using EEG brain signals based on deep learning approach
Ullah, I., Hussain, M., Qazi, E.-U.-H., & Aboalsamh, H. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.04.021 · Cited by 622 · see citing works
Application of Machine Learning To Epileptic Seizure Detection
Shoeb, A. H., & Guttag, J. V. (2010) · Cited by 503 · see citing works
Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy
Ramgopal, S., Thome-Souza, S., Jackson, M., Kadish, N. E., Sánchez Fernández, I., Klehm, J., Bosl, W., Reinsberger, C., Schachter, S., & Loddenkemper, T. (2014) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2014.06.023 · Cited by 469 · see citing works
Deep Learning in Mining Biological Data
Mahmud, M., Kaiser, M. S., McGinnity, T. M., & Hussain, A. (2021) · Cognitive Computation · doi:10.1007/s12559-020-09773-x · Cited by 465 · see citing works
A review of channel selection algorithms for EEG signal processing
Alotaiby, T. N., El‐Samie, F. E. A., Alshebeili, S. A., & Ahmad, I. (2015) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-015-0251-9 · Cited by 461 · see citing works
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
Rasheed, K., Qayyum, A., Qadir, J., Sivathamboo, S., Kwan, P., Kuhlmann, L., O'Brien, T. J., & Razi, A. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3008792 · Cited by 417 · see citing works
Aličković, E., Kevrić, J., & Subaşı, A. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.07.022 · Cited by 413 · see citing works
Seizure prediction with spectral power of EEG using cost-sensitive support vector machines
Park, Y., Luo, L., Parhi, K. K., & Netoff, T. I. (2011) · Epilepsia · doi:10.1111/j.1528-1167.2011.03138.x · Cited by 398 · see citing works
Focal Onset Seizure Prediction Using Convolutional Networks
Khan, H., Marcuse, L. V., Fields, M. C., Swann, K., & Yener, B. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2785401 · Cited by 358 · see citing works
Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network
Zhang, Y., Guo, Y., Yang, P., Chen, W., & Lo, B. P. L. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2933046 · Cited by 322 · see citing works
Deep Learning Human Mind for Automated Visual Classification
Spampinato, C., Palazzo, S., Kavasidis, I., Giordano, D., Souly, N., & Shah, M. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.479 · Cited by 309 · see citing works
Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals
Ay, B., Yıldırım, Ö., Talo, M., Baloğlu, U. B., Galip Aydin, B., Puthankattil, S. D., & Acharya, U. R. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1345-y · Cited by 291 · see citing works
Crowdsourcing reproducible seizure forecasting in human and canine epilepsy
Brinkmann, B. H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S. C., Chen, M., Tieng, Q. M., He, J., Muñoz–Almaraz, F. J., Botella-Rocamora, P., et al. (2016) · Brain · doi:10.1093/brain/aww045 · Cited by 268 · see citing works
Epileptic seizure prediction using relative spectral power features
Bandarabadi, M., Teixeira, C., Rasekhi, J., & Dourado, A. (2014) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.05.022 · Cited by 264 · see citing works
Waytowich, N., Lawhern, V. J., Garcia, J. O., Cummings, J., Faller, J., Sajda, P., & Vettel, J. M. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae5d8 · Cited by 262 · see citing works
van Mierlo, P., Papadopoulou, M., Carrette, E., Boon, P., Vandenberghe, S., Vonck, K., & Marinazzo, D. (2014) · Progress in neurobiology · doi:10.1016/j.pneurobio.2014.06.004 · Cited by 266 · see citing works
Epileptic Seizures Prediction Using Deep Learning Techniques
Usman, S. M., Khalid, S., & Aslam, M. H. (2020) · IEEE Access · doi:10.1109/access.2020.2976866 · Cited by 245 · see citing works
Seo, H., Back, S., Lee, S., Park, D., Kim, T., & Lee, K. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102037 · Cited by 238 · see citing works
Deep Convolutional Neural Networks for mental load classification based on EEG data
Jiao, Z., Gao, X., Wang, Y., Li, J., & Xu, H. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.12.002 · Cited by 229 · see citing works
Raghu, S., & Sriraam, N. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.06.031 · Cited by 226 · see citing works
Machine-learning-based diagnostics of EEG pathology
Gemein, L. A. W., Schirrmeister, R. T., Chrabąszcz, P., Wilson, D., Boedecker, J., Schulze‐Bonhage, A., Hutter, F., & Tonio Ball, A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117021 · Cited by 215 · see citing works
Towards accurate prediction of epileptic seizures: A review
Assi, E. B., Nguyen, D. K., Rihana, S. A., & Sawan, M. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.02.001 · Cited by 212 · see citing works
Acharya, U. R., Hagiwara, Y., & Adeli, H. (2018) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2018.09.030 · Cited by 208 · see citing works
Seizure prediction using EEG spatiotemporal correlation structure
Williamson, J. R., Bliss, D. W., Browne, D. W., & Narayanan, J. (2012) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2012.07.007 · Cited by 203 · see citing works
Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection
Tian, X., Deng, Z., Ying, W., Choi, K., Wu, D., Qin, B., Wang, J., Shen, H., & Wang, S. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2940485 · Cited by 197 · see citing works
Deep Learning in the Biomedical Applications: Recent and Future Status
Zemouri, R. M., Noureddine Zerhouni, A., & Racoceanu, D. (2019) · Applied Sciences · doi:10.3390/app9081526 · Cited by 194 · see citing works
Seizure prediction for therapeutic devices: A review
Gadhoumi, K., Lina, J., Mormann, F., & Gotman, J. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.06.010 · Cited by 191 · see citing works
DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression
Sharma, G., Parashar, A., & Joshi, A. M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102393 · Cited by 189 · see citing works
A wireless and battery-less implant for multimodal closed-loop neuromodulation in small animals
Ouyang, W., Lu, W., Zhang, Y., Liu, Y., Kim, J. U., Shen, H., Wu, Y., Luan, H., Kilner, K. J., Lee, S. P., et al. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01029-x · Cited by 186 · see citing works
Identification of neural connectivity signatures of autism using machine learning
Deshpande, G., Libero, L. E., Sreenivasan, K. R., Deshpande, H., & Kana, R. K. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00670 · Cited by 183 · see citing works
Seizure Prediction Using Spike Rate of Intracranial EEG
Li, S., Zhou, W., Yuan, Q., & Liu, Y. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2282153 · Cited by 169 · see citing works
EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings
Detti, P., Vatti, G., & de Lara, G. Z. M. (2020) · Processes · doi:10.3390/pr8070846 · Cited by 169 · see citing works
A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology
Shashikumar, S. P., Shah, A. J., Qiao, L., Clifford, G. D., & Nemati, S. (2017) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2017.7897225 · Cited by 167 · see citing works
All together now: Analogies between chimera state collapses and epileptic seizures
Andrzejak, R. G., Rummel, C., Mormann, F., & Schindler, K. A. (2016) · Scientific Reports · doi:10.1038/srep23000 · Cited by 166 · see citing works
Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals
Alotaiby, T. N., Alshebeili, S. A., Alotaibi, F. M., & Alrshoud, S. R. (2017) · Computational Intelligence and Neuroscience · doi:10.1155/2017/1240323 · Cited by 154 · see citing works
An Effective Dual Self-Attention Residual Network for Seizure Prediction
Yang, X., Zhao, J., Sun, Q. L., Lu, J., & Ma, X. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3103210 · Cited by 147 · see citing works
Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture
O’Shea, A., Lightbody, G., Boylan, G. B., & Temko, A. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.11.023 · Cited by 143 · see citing works
Cho, D., Min, B., Kim, J., & Lee, B. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2618937 · Cited by 140 · see citing works
Rasekhi, J., Mollaei, M. R. K., Bandarabadi, M., Teixeira, C., & Dourado, A. (2013) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2013.03.019 · Cited by 129 · see citing works
Deep learning for EEG data analytics: A survey
Li, G., Lee, C. H., Jung, J. J., Youn, Y. C., & Camacho, D. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5199 · Cited by 122 · see citing works
Detecting Abnormal Pattern of Epileptic Seizures via Temporal Synchronization of EEG Signals
Fan, M., & Chou, C.-A. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2850959 · Cited by 115 · see citing works
Kassiri, H., Tonekaboni, S., Salam, M. T., Soltani, N., Abdelhalim, K., Velázquez, J. L. P., & Genov, R. (2017) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2017.2694638 · Cited by 114 · see citing works
Wang, G., Wang, D., Du, C., Li, K., Zhang, J., Liu, Z., Tao, Y., Wang, M., Cao, Z., & Yan, X. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3035836 · Cited by 112 · see citing works
Parvez, M. Z., & Paul, M. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2458982 · Cited by 110 · see citing works
A Multi-view Deep Learning Method for Epileptic Seizure Detection using Short-time Fourier Transform
Yuan, Y., Xun, G., Jia, K., & Zhang, A. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3107419 · Cited by 110 · see citing works
Cecotti, H., Eckstein, M. P., & Giesbrecht, B. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2302898 · Cited by 109 · see citing works
Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network
Liu, G., Zhou, W., & Geng, M. (2019) · International Journal of Neural Systems · doi:10.1142/s0129065719500242 · Cited by 109 · see citing works
Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures
Golmohammadi, M., Torbati, A. H. H. N., de Diego, S. L., Iyad Obeid, A., & Picone, J. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00076 · Cited by 104 · see citing works
Seizure Prediction in EEG Signals Using STFT and Domain Adaptation
Peng, P., Song, Y., Lü, Y., & Wei, H. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.825434 · Cited by 104 · see citing works
Energy Distribution Of Eeg Signals: Eeg Signal Wavelet-Neural Network Classifier
Omerhodžić, I., Avdaković, S., Nuhanović, A., & Dizdarević, K. (2010) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1058048 · Cited by 101 · see citing works
A Realistic Seizure Prediction Study Based on Multiclass SVM
Direito, B., Teixeira, C., Sales, F., Castelo-Branco, M. S., & Dourado, A. (2016) · International Journal of Neural Systems · doi:10.1142/s012906571750006x · Cited by 100 · see citing works
Epileptic Seizure Prediction Based on Permutation Entropy
Yang, Y., Zhou, M., Niu, Y., Li, C., Cao, R., Wang, B., Yan, P., Ma, Y., & Xiang, J. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00055 · Cited by 96 · see citing works
Deep Learning: Current and Emerging Applications in Medicine and Technology
Akay, A. R., & Hess, H. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2894713 · Cited by 96 · see citing works
Ke, H., Chen, D., Xiaoli Li, A., Tang, Y., Shah, T., & Ranjan, R. K. (2018) · IEEE Access · doi:10.1109/access.2018.2810882 · Cited by 95 · see citing works
An algorithm for seizure onset detection using intracranial EEG
Kharbouch, A. A., Shoeb, A. H., Guttag, J. V., & Cash, S. S. (2011) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2011.08.031 · Cited by 92 · see citing works
Swift, J., & Coruzzi, G. M. (2016) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2016.08.007 · Cited by 91 · see citing works
DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal
Chambon, S., Thorey, V., Arnal, P. J., Mignot, E. J.-M., & Gramfort, A. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.03.017 · Cited by 88 · see citing works
Song, Y., & Zhang, J. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.08.026 · Cited by 87 · see citing works
Decoding of finger trajectory from ECoG using deep learning
Xie, Z., Schwartz, O., & Prasad, A. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa9dbe · Cited by 86 · see citing works
Ke, H., Chen, D., Shah, T., Liu, X., Zhang, X., Zhang, L., & Xiaoli Li, A. (2018) · Software Practice and Experience · doi:10.1002/spe.2668 · Cited by 85 · see citing works
Usman, S. M., Khalid, S., Akhtar, R., Bortolotto, Z. A., Bashir, Z. I., & Qiu, H. (2019) · Seizure · doi:10.1016/j.seizure.2019.08.006 · Cited by 84 · see citing works
Gadhoumi, K., Lina, J., & Gotman, J. (2012) · Clinical Neurophysiology · doi:10.1016/j.clinph.2012.03.001 · Cited by 82 · see citing works
A GENERALIZABLE MODEL FOR SEIZURE PREDICTION BASED ON DEEP LEARNING USING CNN-LSTM ARCHITECTURE
Shahbazi, M., & Aghajan, H. K. (2018) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2018.8646505 · Cited by 82 · see citing works
Prathaban, B. P., & Баласубраманиан, Р. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114533 · Cited by 81 · see citing works
An efficient seizure prediction method using KNN-based undersampling and linear frequency measures
Ghaderyan, P., Abbasi, A., & Sedaaghi, M. H. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.05.019 · Cited by 80 · see citing works
Extracting and Selecting Distinctive EEG Features for Efficient Epileptic Seizure Prediction
Wang, N., & Lyu, M. R.-T. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2358640 · Cited by 80 · see citing works
Seizure prediction in patients with focal hippocampal epilepsy
Aarabi, A., & He, B. (2017) · Clinical Neurophysiology · doi:10.1016/j.clinph.2017.04.026 · Cited by 80 · see citing works
Ke, H., Chen, D., Shi, B., Zhang, J., Liu, X., Zhang, X., & Xiaoli Li, A. (2019) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2019.2962673 · Cited by 79 · see citing works
Seizure Prediction Using Undulated Global and Local Features
Parvez, M. Z., & Paul, M. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2553131 · Cited by 79 · see citing works
On the proper selection of preictal period for seizure prediction
Bandarabadi, M., Rasekhi, J., Teixeira, C., Karami, M. R., & Dourado, A. (2015) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2015.03.010 · Cited by 76 · see citing works
Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach
Aarabi, A., & He, B. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.10.051 · Cited by 76 · see citing works
Long-term epileptic EEG classification via 2D mapping and textural features
Samiee, K., Serkan Kiranyaz, B., Gabbouj, M., & Saramäki, T. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.05.002 · Cited by 75 · see citing works
Chiang, C.-Y., Chang, N.-F., Chen, T.-C., Chen, H., & Chen, L. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091865 · Cited by 72 · see citing works
Ke, H., Chen, D., Yao, Q., Tang, Y., Wu, J. X., Monaghan, J. J. M., Sowman, P. F., & McAlpine, D. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3252577 · Cited by 72 · see citing works
Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons
Kuhlmann, L., Freestone, D. R., Lai, A., Burkitt, A. N., Fuller, K., Grayden, D. B., Seiderer, L., Vogrin, S. J., Mareels, I., & Cook, M. J. (2010) · Epilepsy Research · doi:10.1016/j.eplepsyres.2010.07.014 · Cited by 72 · see citing works
A review of Graph Neural Networks for Electroencephalography data analysis
Graña, M., & Morais-Quilez, I. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126901 · Cited by 69 · see citing works
Chaos feature study in fractional Fourier domain for preictal prediction of epileptic seizure
Fei, K., Wang, W., Yang, Q., & Tang, S. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.04.019 · Cited by 68 · see citing works
Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures
Bomela, W. B., Wang, S., Chou, C.-A., & Li, J.-S. (2020) · Scientific Reports · doi:10.1038/s41598-020-65401-6 · Cited by 67 · see citing works
Ke, H., Wang, F., Ma, H., & He, Z. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109082 · Cited by 67 · see citing works
Yang, X., Carlo, F. D., Phatak, C., & Gürsoy, D. (2017) · Journal of Synchrotron Radiation · doi:10.1107/s1600577516020117 · Cited by 66 · see citing works
Elahian, B., Yeasin, M., Mudigoudar, B. D., Wheless, J. W., & Abbas Babajani-feremi, A. (2017) · Seizure · doi:10.1016/j.seizure.2017.07.010 · Cited by 66 · see citing works
Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification
Goh, S. K., Abbass, H. A., Tan, K. C., Al-Mamun, A., Thakor, N. V., Bezerianos, A., & Li, J. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2864119 · Cited by 66 · see citing works
Trends and Focus of Machine Learning Applications for Health Research
Beaulieu‐Jones, B. K., Finlayson, S. G., Chivers, C., Chen, I. Y., McDermott, M. B. A., Kandola, J., Dalca, A. V., Beam, A. L., Fiterau, M., & Naumann, T. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.14051 · Cited by 65 · see citing works
Zhang, J., Zheng, S., Chen, W., Du, G., Fu, Q., & Jiang, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-67855-4 · Cited by 64 · see citing works
Raghu, S., Sriraam, N., Rao, S. V., Hegde, A. S., & Kubben, P. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04389-1 · Cited by 64 · see citing works
Detti, P., de Lara, G. Z. M., Bruni, R., Pranzo, M., Sarnari, F., & Vatti, G. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2874716 · Cited by 63 · see citing works
Maimaiti, B., Meng, H., Lv, Y., Qiu, J., Zhu, Z., Xie, Y., Li, Y., Yu-Cheng, Zhao, W., Liu, J., et al. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.11.017 · Cited by 61 · see citing works
Construction of rules for seizure prediction based on approximate entropy
Zhang, Z., Chen, Z., Zhou, Y., Du, S., Zhang, Y., Mei, T., & Tian, X. (2014) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.02.017 · Cited by 61 · see citing works
A Coincidence-Filtering-Based Approach for CNNs in EEG-Based Recognition
Gao, Z., Li, Y., Yang, Y., Dong, N., Yang, X., & Grebogi, C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2955447 · Cited by 60 · see citing works
Machine learning applications for electroencephalograph signals in epilepsy: a quick review
Si, Y. (2020) · Acta Epileptologica · doi:10.1186/s42494-020-00014-0 · Cited by 59 · see citing works
Phase-Synchronization Early Epileptic Seizure Detector VLSI Architecture
Abdelhalim, K., Smolyakov, V., & Genov, R. (2011) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2011.2170686 · Cited by 59 · see citing works
Wave2Vec: Deep representation learning for clinical temporal data
Yuan, Y., Xun, G., Suo, Q., Jia, K., & Zhang, A. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.074 · Cited by 57 · see citing works
Vahabi, Z., Amirfattahi, R., Shayegh, F., & Ghasemi, F. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065715500288 · Cited by 56 · see citing works
Modeling epileptic brain states using EEG spectral analysis and topographic mapping
Direito, B., Teixeira, C., Ribeiro, B., Castelo-Branco, M. S., Sales, F., & Dourado, A. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.07.006 · Cited by 54 · see citing works
- Authors
- Mirowski, P., Madhavan, D., LeCun, Y., & Kuzniecky, R.
- Affiliations
- Courant Institute of Mathematical Sciences (US)
- University of Nebraska Medical Center (US)
- New York University (US)
- Corresponding author
- Mirowski, Piotr
- Venue
- Clinical Neurophysiology
- Publisher
- Elsevier BV
- Published
- 2009-11
- Volume
- 120
- Issue
- 11
- Pages
- 1927-1940
- ISSN
- 1388-2457 (ISSN-L)
- OpenAlex
- W2009902427
- Cited by
- 380
- References
- 45
- Citations by year
- 2026: 13 · 2025: 13 · 2024: 16 · 2023: 19 · 2022: 26 · 2021: 26 · 2020: 34 · 2019: 40 · 2018: 48 · 2017: 34 · 2016: 21 · 2015: 27 · 2014: 16 · 2013: 13 · 2012: 10
- FWCI
- 5.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Seismology and Earthquake Studies
- Keywords
- seizure prediction, intracranial EEG, EEG synchronization, cross correlation function, wavelet coherence, spatiotemporal patterns, preictal EEG, interictal EEG, convolutional neural networks, support vector machine
- MeSH terms
- Electroencephalography, Humans, Predictive Value of Tests, Seizures, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
