An automatic patient-specific seizure onset detection method in intracranial EEG based on incremental nonlinear dimensionalityreduction

Computers in Biology and Medicine

Volumn 40, Issue 11-12, 2010, Pages 889-899

  Zhang, Yizhuo ^a,b   Xu, Guanghua ^a,b   Wang, Jing ^a,b   Liang, Lin ^a,b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

IEEG; Incremental learning; Nonlinear dimensionality reduction; Seizure onset detection

Indexed keywords

AVERAGE SENSITIVITIES; CONTINUOUS WAVELET TRANSFORMS; EEG RECORDING; EEG SIGNALS; ELECTROENCEPHALOGRAPHIC SIGNALS; EPILEPTIC PATIENTS; EPILEPTIC SEIZURES; FALSE POSITIVE; FALSE POSITIVE RATES; FEATURE SETS; IEEG; INCREMENTAL LEARNING; LOCAL TANGENT SPACE ALIGNMENT; NONLINEAR DIMENSIONALITY REDUCTION; NONLINEAR WAVEFORM; SEIZURE DETECTION; SEIZURE ONSET; SPATIAL DISTRIBUTION;

NONLINEAR ANALYSIS; WAVELET TRANSFORMS;

SIGNAL DETECTION;

ADOLESCENT; ADULT; ARTICLE; AUTOMATION; CLINICAL ARTICLE; DATA BASE; ELECTROENCEPHALOGRAM; FALSE POSITIVE RESULT; FEMALE; HUMAN; LEARNING; MALE; METHODOLOGY; NONLINEAR SYSTEM; PRIORITY JOURNAL; SEIZURE;

ADOLESCENT; ADULT; BRAIN WAVES; CHILD; ELECTROENCEPHALOGRAPHY PHASE SYNCHRONIZATION; FEMALE; HUMANS; MALE; MIDDLE AGED; SEIZURES; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 78649324881     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2010.09.010     Document Type: Article

References (55)

1. Hauser W., Annegers J., Rocca W. Descriptive epidemiology of epilepsy: contributions of population-based studies from Rochester Minnesota. Mayo Clinic Proceedings 1996, 71:578-586.
2. Montina A., Mendoza C., Arecchi F.T. Role of refractory period in homoclinic models of neural synchronization. International Journal of Neural Systems 2007, 17(2):79-86.
3. Chiappalone M., Vato A., Berdondini L, Koudelka M., Martinoia S. Network dynamics and synchronous activity in cultured cortical neurons. International Journal of Neural Systems 2007, 17(2):87-103.
4. Acir N., Oztura I., Kuntalp M., et al. Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks. IEEE Transactions on Biomedical Engineering 2005, 52(1):30-40.
5. Kramer M.A., Chang F.L., Cohen M.E., Hudson D., Szeri A.J. Synchronization measures of the scalp EEG can discriminate healthy from Alzheimers subjects. International Journal of Neural Systems 2007, 17(2):61-69.
6. Wang X., Chen Y., Bressler S., Ding M. Evaluating causal relations among multiple neurobiological time series: blockwise versus pairwise granger causality. International Journal of Neural Systems 2007, 17(2):71-78.
7. Shoeb A., Guttag J., Pang T., Schachter S. Non-invasive computerized system for automatically initiating vagus nerve stimulation following patient-specific detection of seizures or epileptiform discharges. International Journal of Neural Systems 2009, 19(3):157-172.
8. Adeli H., Ghosh-Dastidar S. Automated EEG-based Diagnosis of Neurological Disorders-Inventing the Future of Neurology 2010, CRC Press, Taylor & Francis, Boca Raton, Florida.
9. Ahmadlou M., Adeli H. Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD. Clinical EEG and Neuroscience 2010, 41(1):1-10.
10. M. Ahmadlou, H. Adeli, Fuzzy synchronization likelihood with application to attention-deficit/hyperactivity disorder, Clinical EEG and Neuroscience, in press.
11. Lee H., Cichocki A., Choi S. Nonnegative matrix factorization for motor imagery EEG classification. International Journal of Neural Systems 2007, 17(4):305-317.
12. Shoeb A., Edwards H., Connolly J., Bourgeois B., Treves S.T., Guttag J. Patient-specific seizure onset detection. Epilepsy Behavior 2004, 5(4):483-498.
13. Guler I., Übeyli E.D. Multiclass support vector machines for EEG signals classification. IEEE Transactions on Information Technology in Biomedicine 2007, 11(2):117-126.
14. Gabor A.J., Leach R.R., Dowla F.U. Automated seizure detection using a self-organizing neural network. Electroencephalography and Clinical Neurophysiology 1996, 99(3):257-266.
15. Übeyli E.D., Guler I. Features extracted by eigenvector methods for detecting variability of EEG signals. Pattern Recognition Letters 2007, 28:592-603.
16. Ghosh-Dastidar S., Adeli H. Improved spiking neural networks for EEG classification and epilepsy and seizure detection. Integrated Computer-Aided Engineering 2007, 14(3):187-212.
17. Ghosh-Dastidar S., Adeli H. Spiking neural networks. International Journal of Neural Systems 2009, 19(4):295-308.
18. Ghosh-Dastidar S., Adeli H. A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural Networks 2009, 22:1419-1431.
19. Postnov D.E., Ryazanova L.S., Zhirin R.A., Mosekilde E., Sosnovtseva O.V. Noise controlled synchronization in potassium coupled neural networks. International Journal of Neural Systems 2007, 17(2):105-113.
20. Chen M., Jiang C.S., Wu Q.X., Chen W.H. Synchronization in arrays of uncertain delay neural networks by decentralized feedback control. International Journal of Neural Systems 2007, 17(2):115-122.
21. Saab M.E., Gotman J. A system to detect the onset of epileptic seizures in scalp EEG. Clinical Neurophysiology 2005, 116(2):427-442.
22. Qu H., Gotman J. A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: possible use as a warning device. IEEE Transactions on Biomedical Engineering 1997, 44(2):115-122.
23. Chakravarthy N., Sabesan S., Tsakalis K., Iasemidis L. Controling synchronization in a neural-level population model. International Journal of Neural Systems 2007, 17(2):123-138.
24. Good L.B., Sabesan S., Marsh S.T., Tsakalis K.S., Iasemidis L.D. Control of synchronization of brain dynamics leads to control of epileptic seizures in rodents. International Journal of Neural Systems 2009, 19(3):173-196.
25. Osterhage H., Mormann F., Wagner T., Lehnertz K. Measuring the directionality of coupling: phase versus state space dynamics and application to EEG time series. International Journal of Neural Systems 2007, 17(3):139-148.
26. Osorio I., Frei M.G. Seizure abatement with single DC pulses: is phase resetting at play?. International Journal of Neural Systems 2009, 19(3):149-156.
27. Elger C.E., Lehnertz K. Seizure prediction by non-linear time series analysis of brain electrical activity. European Journal of Neuroscience 1998, 10:786-789.
28. Martinerie J., Adam C., Le Van Quyen M., et al. Epileptic seizures can be anticipated by non-linear analysis. Nature Medicine 1998, 4:1173-1176.
29. Van Drongelen W., Nayak S., Frim D.M., et al. Seizure anticipation in pediatric epilepsy: use of Kolmogorov entropy. Pediatric Neurology 2003, 29:207-213.
30. Iasemidis L.D., et al. Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures. Brain Topography 1990, 2(3):187-201.
31. Khan Yu, Gotman J. Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clinical Neurophysiology 2003, 114:898-908.
32. Guler I, Übeyli E.D Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. Journal of Neuroscience Methods 2005, 148:113-121.
33. Hopfengärtner R, et al. An efficient, robust and fast method for the offline detection of epileptic seizures in long-term scalp EEG recordings. Clinical Neurophysiology 2007, 118:2332-2343.
34. Adeli H, Ghosh-Dastidar S, Dadmehr N A wavelet-chaos methodology for analysis of EEGs and EEG sub-bands to detect seizure and epilepsy. IEEE Transactions on Biomedical Engineering 2007, 54(2):205-211.
35. Ghosh-Dastidar S, Adeli H, Dadmehr N Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Transactions on Biomedical Engineering 2007, 54(9):1545-1551.
36. Adeli H, Ghosh-Dastidar S, Dadmehr N A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer's disease. Neuroscience Letters 2008, 444(2):190-194.
37. Tenenbaum J., de Silva V., Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290:2319-2322.
38. Roweis S., Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290:2323-2326.
39. Belkin M., Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 2003, 15(6):1373-1396.
40. Lee G., Rodriguez C., Madabhushi A. Investigating the efficacy of nonlinear dimensionality reduction schemes in classifying gene and protein expression studies. IEEE/ACM transaction on Computational Biology and Bioinformatics 2008, 5(3):368-384.
41. Zhenyue Z., Hongyuan Z. Principal manifolds and nonlinear dimension reduction via local tangent space alignment. SIAM Scientific Computing 2004, 26:313-338.
42. Law Martin H.C., Jain A.K. Incremental nonlinear dimensionality reduction by manifold learning. IEEE Transactions of Pattern Analysis and Intelligence 2006, 28(3):377-391.
43. Kouropteva Olga, Okun Oleg, Pietikainen Matti Incremental locally linear embedding. Pattern Recognition 2005, 38:1764-1767.
44. Xiaoming L., Jianwei Y., Zhilin F., Jinxiang D. Incremental manifold learning via tangent space alignment. Artificial Neural Networks in Pattern Recognition 2006, 4087:107-121.
45. Maiwald T., Winterhalder M., Aschenbrenner-Scheibe R., Voss, A.H.U., et al. Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic. Physica D 2004, 194:357-368.
46. Albert-Ludwigs-University, Freiburg, Germany, 〈〉. http://www.fdm.uni-freiburg.de/groups/timeseries/epi/EEGData/download.
47. Adeli H., Zhou Z., Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods 2003, 123:69-87.
48. Samant A., Adeli H. Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis. Computer-aided Civil and Infrastructure Engineering 2000, 15:241-250.
49. Karim A., Adeli H. Incident detection algorithm using wavelet energy representation of traffic patterns. Journal of Transportation Engineering 2002, 128:232-242.
50. Sitnikova E., Hramov A.E., Koronovsky A.A., van Luijtelaar G. Sleep spindles and spike-wave discharges in EEG: their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysis. Journal of Neuroscience Methods 2009, 180(2):304-316.
51. Kiymik M.K., Guler I., Dizibuyuk A., Akin M. Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Computers in Biology and Medicine 2005, 35(7):603-616.
52. Ahm Lee S., Spencer D., Spencer S. Intracranial EEG seizure-onset patterns in neocortical epilepsy. Clinical Research 2000, 41(3):297-307.
53. Chan M, Sun T, Boto H, Wingeier M Automated seizure onset detection for accurate onset time determination in intracranial EEG. Clinical Neurophysiology 2008, 119(12):2687-2696.
54. D'Alessandro M., Esteller R., Vachtsevanos G., 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 2003, 50(5):603-615.
55. Ghosh-Dastidar S., Adeli H., Dadmehr N. Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Transactions on Biomedical Engineering 2008, 55(2):512-518.