Reduction of classification error rates and monitoring...

Details Reduction of classification error rates and monitoring... Reduction of classification error rates and monitoring...

2007-02-26

2010-06-29

Holmes, Michael B (Department: 2129)

Data processing: artificial intelligence

Neural network

Learning task

Reexamination Certificate

active

07747551

ABSTRACT:
Systems and methods for enhancing the accuracy of classifying a measurement by providing an artificial class. Seizure prediction systems may employ a classification system including an artificial class and a user interface for signaling uncertainty in classification when a measurement is classified in the artificial class.

REFERENCES:
patent: 5092343 (1992-03-01), Spitzer et al.
patent: 5995651 (1999-11-01), Gelenbe et al.
patent: 6507829 (2003-01-01), Richards et al.
patent: 6561992 (2003-05-01), Eberhart et al.
patent: 6596501 (2003-07-01), Roth
patent: 6852743 (2005-02-01), Ojima et al.
patent: 7043474 (2006-05-01), Mojsilovic et al.
patent: 7070817 (2006-07-01), Kuppam
patent: 7113819 (2006-09-01), Hamilton et al.
patent: 7202243 (2007-04-01), Hendrix et al.
patent: 7357934 (2008-04-01), Donovan et al.
patent: 7427601 (2008-09-01), Stoehr
patent: 7428323 (2008-09-01), Hillman
patent: 7544470 (2009-06-01), Boeke
patent: 2009/0030403 (2009-01-01), Leyde
Artificial Neural Network for the Analysis of Electroencephalogram Nayak, K.P.; Padmashree, T.K.; Rao, S.N.; Cholayya, N.U.; Intelligent Sensing and Information Processing, 2006. ICISIP 2006. Fourth International Conference on Oct. 15, 2006-Dec. 18, 2006 pp. 170-173 Digital Object Identifier 10.1109/ICISIP.2006.4286089.
Epileptic Seizure Detection using AR Model on EEG Signals Mousavi, S.R.; Niknazar, M.; Vahdat, B.V.; Biomedical Engineering Conference, 2008. CIBEC 2008. Cairo International Dec. 18-20, 2008 pp. 1-4 Digital Object Identifier 10.1109/CIBEC.2008.4786067.
A comparison of algorithms for detection of spikes in the electroencephalogram Pang, C.C.C.; Upton, A.R.M.; Shine, G.; Kamath, M.V.; Biomedical Engineering, IEEE Transactions on vol. 50, Issue 4, Apr. 2003 pp. 521-526 Digital Object Identifier 10.1109/TBME.2003.809479.
Processing EEG signals for Clinical Interpretation in Seizure-Suspected Patients Lay-Ekuakille, A.; Vendramin, G.; Trotta, A.; De Rinaldis, M.; Trabacca, A.; Medical Measurement and Applications, 2007. MEMEA '07. IEEE International Workshop on May 4-5, 2007 pp. 1-4 Digital Object Identifier 10.1109/MEMEA.2007.4285157.
Adjouadi, M. et al., “A New Mathematical Approach Based on Orthogonal Operators for the Detection of Interictal Spikes in Epileptogenic Data,” Presented at Rocky Mountain Bioengineering Symposium & International ISA Biomedical Sciences Instrumentation Symposium, 2004, pp. 175-180.
Adjouadi, M. et al., “Interictal Spike Detection Using the Walsh Transform,” IEEE Transactions on Biomedical Engineering, May 2004, pp. 868-872, vol. 51, No. 5.
Adjouadi, M. et al., “Detection of Interictal Spikes and Artifactual Data Through Orthogonal Transformations,” Journal of Clinical Neurophysiology, Feb. 2005, pp. 53-64, vol. 22, No. 1.
Aksenova, T.I. et al., “On-Line Disharmony Detection for Early Prediction of Epilepsy Seizure Onset,” 2003, 2 pages.
Aksenova, T.I. et al., “Nonparametric On-Line Detection of Changes in Signal Spectral Characteristics for Early Prediction of Epilepsy Seizure Onset,” Journal of Automation and Information Sciences, 2004, pp. 35-45, vol. 36, No. 8.
Andrzejak, R.G. et al., “Testing the Null Hypothesis of the Nonexistence of a Preseizure State,” Physical Review, 2003, pp. 1-4, The American Physical Society, vol. 67, No. 010901(R).
Andrzejak, R.G. et al., “Bivariate Surrogate Techniques: Necessity, Strengths, and Caveats,” Physical Review, 2003, pp. 1-15, The American Physical Society, vol. 68, No. 066202.
Aschenbrenner-Scheibe, R. et al., “How Well Can Epileptic Seizures be Predicted? An Evaluation of a Nonlinear Method,” Brain, Sep. 23, 2003, pp. 2616-2626, vol. 126.
Bangham, A.D. et al., “Diffusion of Univalet Ions Across the Lamellae of Swollen Phospholipids,” J. Mol. Biol., 1965, pp. 238-252, vol. 13.
Baruchi, I. et al., “Functional Holography of Recorded Neuronal Networks Activity,” Neuroinformatics, 2004, pp. 333-352, vol. 4.
Baruchi, I. et al., Functional Holography of Complex Networks Activity—From Cultures to the Human Brain, Complexity, 2005, pp. 38-51, vol. 10, No. 3, Wiley Periodicals, Inc.
Ben-Hur, A. et al., “Detecting Stable Clusters Using Principal Component Analysis,” in Methods in Molecular Biology, 2003 pp. 1-23.
Bergey, G.K. et al., “Epileptic Seizures are Characterized by Changing Signal Complexity,” Clinical Neurophysiology, 2001, pp. 1-9, Elsevier.
Betterton, P. et al., “Determining State of Consciousness from the Intracranial Electroencephalogram (IEEG) for Seizure Prediction,” Proceedings of the 22ndIASTED International Conference Modelling, Identification and Control, Feb. 10-13, 2003, Innsbruck, Austria, pp. 313-317.
Bhattacharya, J. et al., “Enhanced Phase Synchrony in the Electroencephalograph γ Band for Musicians While Listening to Music,” Physical Review, 2001, pp. 1-4, The American Physical Society, vol. 64, No. 012902.
Boley, D. et al., “Training Support Vector Machine Using Adaptive Clustering,” Proceedigns of the Fourth SIAM International Conference on Data Mining, 2004,12 pages.
Burges, C.J.C., “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, 1998, pp. 121-167, vol. 2, Kluwer Academic Publishers.
Cao, Y. et al., “Detecting Dynamical Changes in Time Series Using the Permutation Entropy,” Physical Review, 2004, pp. 1-7, vol. 70, No. 046217.
Carretero-González, R. et al., “Scaling and Interleaving of Subsystem Lyapunov Exponents for Spatio-Temporal Systems,” Chaos, Jun. 1999, pp. 466-482, vol. 9, No. 2.
Casdagli, M.C. et al., “Nonlinear Analysis of Mesial Temporal Lobe Seizures Using a Surrogate Data Technique,” Epilepsia, Annual Meeting of the American Epilepsy Society, Baltimore, Maryland, Dec. 1-6, 1995, p. 142, vol. 36, Suppl. 4.
Casdagli, M.C. et al., “Characterizing Nonlinearity in Invasive EEG Recordings from Temporal Lobe Epilepsy,” Physica D, Dec. 15, 1996, pp. 1-17, vol. 99, Issues 2-3.
Casdagli, M.C. et al., “Non-Linearity in Invasive EEG Recordings from Patients with Temporal Lobe Epilepsy,” Electroencephalography and Clinical Neurophysiology, Feb. 1997, pp. 1-10, vol. 102, Issue 2.
Cerf, R. et al., “Criticality and Synchrony of Fluctuations in Rhythmical Brain Activity: Pretransitional Effectsin Epileptic Patients,” Biological Cybernetics, Mar. 30, 2004, pp. 239-255, vol. 903.
Chaovalitwongse, W. et al., “EEG Classification in Epilepsy,” Jun. 30, 2004, Annals.tex, pp. 1-32, vol. 2, No. 37, Kluwer Academic Publishers, Netherlands.
Chaovalitwongse, W. et al., “Performance of a Seizure Warning Algorithm Based on the Dynamics of Intracranial EEG,” Epilepsy Research, 2003, pp. 1-46, vol. 64, Issue 3.
Chaovalitwongse, W. A. et al., “Reply to Comments on “Performance of a Seizure Warning Algorithm Based on the Dynamics of Intracranial EEG” by Winterhalder, M., Schelter, B., Achulze-Bonhage, A., Timmer, J.,” Epilepsy Research, Jul. 31, 2006, pp. 82-84, vol. 72, Elsevier.
Chávez, M. et al., “Spatio-Temporal Dynamics Prior to Neocortical Seizures: Amplitude Versus Phase Couplings,” IEEE Transactions on Biomedical Engineering, May 2003, pp. 571-583, vol. 50 No. 5.
Chrichton, M., “The Terminal Man,” 1972, pp. 21-24, 32-33, 70-81, Alfred A. Knopf, New York.
D'Alessandro, M. et al., “A Multi-Feature and Multi-Channel Univariate Selection Process for Seizure Prediction,” Clinical Neurophysiology, 2005, pp. 506-516, vol. 116.
D'Alessandro, M. et al., “Epileptic Seizure Prediction Using Hybrid Feature Selection Over Multiple Intracranial EEG Electrode Contacts: A Report of Four Patients,” IEEE Transactions on Biomedical Engineering, May 2003, pp. 603-615, vol. 50, No. 5.
Drury, I. et al., “Seizure Prediction Using Scalp Electroencephalogram,&#

Affiliated with

Also associated with

Rating

Reduction of classification error rates and monitoring... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Reduction of classification error rates and monitoring..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Reduction of classification error rates and monitoring... will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-4193821