Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement
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Justin A. Blanco | B Litt | D F Wulsin | J R Gupta | R Mani | J A Blanco | D. Wulsin | B. Litt | J. Blanco | R. Mani | J. R. Gupta | Drausin Wulsin
[1] J. Gotman,et al. Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG. , 1976, Electroencephalography and clinical neurophysiology.
[2] J Gotman,et al. Improvement in the performance of automated spike detection using dipole source features for artefact rejection , 2003, Clinical Neurophysiology.
[3] R. Agarwal,et al. Compressed EEG pattern analysis for critically III neurological-neurosurgical patients , 2006 .
[4] Justin A. Blanco,et al. Unsupervised classification of high-frequency oscillations in human neocortical epilepsy and control patients. , 2010, Journal of neurophysiology.
[5] Brian Litt,et al. One-Class Novelty Detection for Seizure Analysis from Intracranial EEG , 2006, J. Mach. Learn. Res..
[6] Nicolette de Keizer,et al. Integrating classification trees with local logistic regression in Intensive Care prognosis , 2003, Artif. Intell. Medicine.
[7] G. Vachtsevanos,et al. A multi-feature and multi-channel univariate selection process for seizure prediction , 2005, Clinical Neurophysiology.
[8] Yoshua Bengio,et al. An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.
[9] T. Pedley. Current Practice of Clinical Electroenceph‐alography , 1980, Neurology.
[10] J. Praagman. Book reviewClassification and regression trees: Leo BREIMAN, Jerome H. FRIEDMAN, Richard A. OLSHEN and Charles J. STONE The Wadsworth Statistics/Probability Series, Wadsworth, Belmont, 1984, x + 358 pages , 1985 .
[11] V. Lagerburg,et al. Detecting temporal lobe seizures from scalp EEG recordings: A comparison of various features , 2005, Clinical Neurophysiology.
[12] Rajat Raina,et al. Large-scale deep unsupervised learning using graphics processors , 2009, ICML '09.
[13] I. Osorio,et al. Real‐Time Automated Detection and Quantitative Analysis of Seizures and Short‐Term Prediction of Clinical Onset , 1998, Epilepsia.
[14] J. Gotman,et al. A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: possible use as a warning device , 1997, IEEE Transactions on Biomedical Engineering.
[15] Lawrence Ver Hoef,et al. Effect of Detection Parameters on Automated Electroencephalography Spike Detection Sensitivity and False-Positive Rate , 2010, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.
[16] E. Poole,et al. Current practice of clinical electroencephalography D. W. Klass &D. D. Daly, Raven Press, 1979, 544 pp. $61.20 , 1980, Neuroscience.
[17] L. Hirsch,et al. Which EEG Patterns Warrant Treatment in the Critically Ill? Reviewing the Evidence for Treatment of Periodic Epileptiform Discharges and Related Patterns , 2005, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.
[18] Kemal Polat,et al. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform , 2007, Appl. Math. Comput..
[19] T. Pedley,et al. Current practice of clinical electroencephalography, 3rd edn , 2003 .
[20] James McNames,et al. A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[21] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[22] D. Treiman,et al. Interobserver Agreement in the Interpretation of EEG Patterns in Critically Ill Adults , 2008, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.
[23] G. Alarcón,et al. Power spectrum and intracranial EEG patterns at seizure onset in partial epilepsy. , 1995, Electroencephalography and clinical neurophysiology.
[24] A. Flisberg,et al. Automatic classification of background EEG activity in healthy and sick neonates , 2010, Journal of neural engineering.
[25] J Gotman,et al. Automatic EEG analysis during long-term monitoring in the ICU. , 1998, Electroencephalography and clinical neurophysiology.
[26] Nicolas Le Roux,et al. Representational Power of Restricted Boltzmann Machines and Deep Belief Networks , 2008, Neural Computation.
[27] Scott B. Wilson,et al. Spike detection: a review and comparison of algorithms , 2002, Clinical Neurophysiology.
[28] A Värri,et al. Evaluation of a computerized system for recognition of epileptic activity during long-term EEG recording. , 1994, Electroencephalography and clinical neurophysiology.
[29] Blaz Zupan,et al. Feature mining and predictive model construction from severe trauma patient's data , 2001, Int. J. Medical Informatics.
[30] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[31] Brian Litt,et al. Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings , 2007, Clinical Neurophysiology.
[32] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[33] Yoshua Bengio,et al. Scaling learning algorithms towards AI , 2007 .
[34] Brian Litt,et al. Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets , 2010, 2010 Ninth International Conference on Machine Learning and Applications.
[35] G. Lightbody,et al. A comparison of quantitative EEG features for neonatal seizure detection , 2008, Clinical Neurophysiology.
[36] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[37] Brian Litt,et al. Line length: an efficient feature for seizure onset detection , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[38] A. Liu,et al. Detection of neonatal seizures through computerized EEG analysis. , 1992, Electroencephalography and clinical neurophysiology.
[39] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.