Kernel collaborative representation-based automatic seizure detection in intracranial EEG

International Journal of Neural Systems

Volumn 25, Issue 2, 2015, Pages

  Yuan, Shasha ^a   Zhou, Weidong ^a   Yuan, Qi ^a   Li, Xueli ^a   Wu, Qi ^b   Zhao, Xiuhe ^b   Wang, Jiwen ^b  

^a SHANDONG UNIVERSITY   (China)

^b SHANDONG UNIVERSITY   (China)

Author keywords

EEG; kernel collaborative representation; Seizure detection; wavelet

Indexed keywords

KNOWLEDGE REPRESENTATION; WAVELET DECOMPOSITION;

AUTOMATIC SEIZURE DETECTIONS; AVERAGE SENSITIVITIES; COLLABORATIVE REPRESENTATIONS; FALSE DETECTIONS; MONITORING AND DIAGNOSIS; REGULARIZED LEAST SQUARES; SEIZURE DETECTION; WAVELET;

ELECTROENCEPHALOGRAPHY;

AUTOMATION; ELECTROENCEPHALOGRAPHY; HUMAN; PATHOPHYSIOLOGY; PROCEDURES; SEIZURE; SIGNAL PROCESSING; SUPPORT VECTOR MACHINE;

AUTOMATION; ELECTROENCEPHALOGRAPHY; HUMANS; SEIZURES; SIGNAL PROCESSING, COMPUTER-ASSISTED; SUPPORT VECTOR MACHINES;

EID: 84928406498     PISSN: 01290657     EISSN: 17936462     Source Type: Journal    
DOI: 10.1142/S0129065715500033     Document Type: Article

References (70)

1. W. A. Hauser, J. F. Annegers and W. A. Rocca, Descriptive epidemiology of epilepsy: Contributions of population-based studies from Rochester, Minnesota, Mayo Clin. Proc. 71(6) (1996) 578-586.
2. S. Sanei and J. A. Chambers, EEG Signal Processing (John Wiley & Sons Ltd, Chichester, 2007), p. 161.
3. R. A. B. Badawy, G. D. Jackson, S. F. Berkovic and R. A. L. Macdonell, Cortical excitability and refractory epilepsy: A three-year longitudinal transcranial magnetic stimulation study, Int. J. Neural Syst. 23(1) (2013) 1250030.
4. R. A. B. Badawy, S. J. Vogrin, A. Lai and M. J. Cook, On the midway to epilepsy: Is cortical excitability normal in patients with isolated seizures? Int. J. Neural Syst. 24(2) (2014) 1430002.
5. T. Gandhi, B. K. Panigrahi, M. Bhatia and S. Anand, Expert model for detection of epileptic activity in EEG signature, Expert Syst. Appl. 37(4) (2010) 3513-3520.
6. K. Vonck, M. Sprengers, E. Carrette, I. Dauwe, M. Miatton, A. Meurs, L. Goossens, V. D. Herdt, R. Achten, E. Thiery, R. Raedt, D. V. Roost and P. Boon, A decade of experience with deep brain stimulation for patients with refractory medial temporal lobe epilepsy, Int. J. Neural Syst. 23(1) (2013) 1250034.
7. H. Adeli and S. Ghosh-Dastidar, Automated EEGBased Diagnosis of Neurological Disorders: Inventing the Future of Neurology (CRC Press, Florida, 2010), p. 72.
8. M. Ahmadlou and H. Adeli, Wavelet-Synchronization Methodology: A new approach for EEG-based diagnosis of ADHD, Clin. EEG Neurosci. 41(1) (2010) 1-10.
9. M. Ahmadlou and H. Adeli, Functional community analysis of brain: A new approach for EEG-based investigation of the brain pathology, Neuroimage 58(2) (2011) 401-408.
10. M. Ahmadlou, H. Adeli and A. Adeli, Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder, J. Clin. Neurophysiol. 27(5) (2010) 328-333.
11. Z. Sankari and H. Adeli, Probabilistic neural networks for EEG-based diagnosis of Alzheimer's disease using conventional and wavelet coherence, J. Neurosci. Methods 197(1) (2011) 165-170.
12. G. Lee, M. Kwon, S. Kavuri and M. Lee, Actionperception cycle learning for incremental emotion recognition in a movie clip using 3D fuzzy GIST based on visual and EEG signals, Integr. Comput.-Aided Eng. 21(3) (2014) 295-310.
13. C. Zhang, H. Wang, H. Wang and M. Wu, EEGbased expert system using complexity measures and probability density function control in alpha subband, Integr. Comput.-Aided Eng. 20(4) (2013) 391-405.
14. L. B. Good, S. Sabesan, S. T. Marsh, K. S. Tsakalis and L. D. Iasemidis, Control of synchronization of brain dynamics leads to control of epileptic seizures in rodents, Int. J. Neural Syst. 19(3) (2009) 173-196.
15. Z. Sankari, H. Adeli and A. Adeli, Intrahemispheric, interhemispheric and distal EEG coherence in alzheimer's disease, Clin. Neurophysiol. 122(5) (2011) 897-906.
16. M. Ahmadlou and H. Adeli, Fuzzy synchronization likelihood with application to attentiondeficit/hyperactivity disorder, Clin. EEG Neurosci. 42(1) (2011) 6-13.
17. M. Ahmadlou, H. Adeli and A. Adeli, Spatiotemporal analysis of relative convergence (STARC) of EEGs reveals differences between brain dynamics of depressive women and men, Clin. EEG Neurosci. 44(3) (2013) 175-181.
18. A. Shoeb, J. Guttag, T. Pang and S. Schachter, Noninvasive computerized system for automatically initiating vagus nerve stimulation following patient specific detection of seizures or epileptiform discharges, Int. J. Neural Syst. 19(3) (2009) 157-172.
19. M. Ahmadlou, H. Adeli and A. Amir, Graph theoretical analysis of organization of functional brain networks in ADHD, Clin. EEG Neurosci. 43(1) (2012) 5-13.
20. G. Rodŕ?guez-Bermúdez, P. J. Garć?a-Laencina and J. Roca-Dorda, Efficient automatic selection and combination of EEG features in least squares classifiers for motor-imagery brain computer interfaces, Int. J. Neural Syst. 23(4) (2013) 1350015.
21. F. Cong, A. H. Phan, P. Astikainen, Q. Zhao, Q. Wu, J. K. Hietanen, T. Ristaniemi and A. Cichocki, Multi-domain feature extraction for small eventrelated potentials through non-negative multi-way array decomposition from low dense array EEG, Int. J. Neural Syst. 23(2) (2013) 1350006.
22. L. J. Herrera, C. M. Fernandes, A. M. Mora, D. Migotina, R. Largo, A. Guillen and A. C. Rosa, Combination of heterogeneous EEG feature extraction methods and stacked sequential learning for sleep stage classification, Int. J. Neural Syst. 23(3) (2014) 1350012.
23. K. Lehnertz, Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy-An overview, Int. J. Psychophysiol. 34(1) (1999) 45-52.
24. U. R. Acharya, S. V. Sree, S. Chattopadhyay, W. Yu and P. C. A. Ang, Application of recurrence quantification analysis for the automated identification of epileptic EEG signals, Int. J. Neural Syst. 21(3) (2011) 199-211.
25. T. S. Nelson, C. L. Suhr, D. R. Freestone, A. Lai, A. J. Halliday, K. J. McLean, A. N. Burkitt and M. J. Cook, Closed-loop seizure control with very high frequency electrical stimulation at seizure onset in the GAERS model of absence epilepsy, Int. J. Neural Syst. 21(2) (2011) 163-173.
26. S. Ghosh-Dastidar, H. Adeli and N. Dadmehr, Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection, IEEE Trans. Biomed. Eng. 54(9) (2007) 1545-1551.
27. S. Ghosh-Dastidar and H. Adeli, Improved spiking neural networks for EEG classification and epilepsy and seizure detection, Integr. Comput.-Aided Eng. 14(3) (2007) 187-212.
28. A. Temko, G. Boylan, W. Marnane and G. Lightbody, Robust neonatal EEG classification through adaptive background modeling, Int. J. Neural Syst. 23(4) (2013) 1350018.
29. U. R. Acharya, R. Yanti, Z. J. Wei, M. M. Ramakrishnan, T. J. Hong, R. J. Martis and L. C. Min, Automated diagnosis of epilepsy using CWT, HOS and Texture parameters, Int. J. Neural Syst. 23(3) (2013) 1350009.
30. R. J. Martis, U. R. Acharya, J. H. Tan, A. Petznick, L. Tong, C. K. Chua and E. Y. Ng, Application of intrinsic time-scale decomposition (ITD) to EEG signals for automated seizure prediction, Int. J. Neural Syst. 23(5) (2013) 1350023.
31. D. Sherman, N. Zhang, S. Garg, N. V. Thakor, M. A. Mirski, M. A.White and M. J. Hinich, Detection of nonlinear interactions of EEG alpha waves in the brain by a new coherence measure and its application to epilepsy and anti-epileptic drug therapy, Int. J. Neural Syst. 21(2) (2011) 115-126.
32. Z. Sankari, H. Adeli and A. Adeli, Wavelet coherence model for diagnosis of alzheimer's disease, Clin. EEG Neurosci. 43(3) (2012) 268-278.
33. S. Ghosh-Dastidar, H. Adeli and N. Dadmehr, Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection, IEEE Trans. Biomed. Eng. 55(2) (2008) 512-518.
34. S. Ghosh-Dastidar and H. Adeli, A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection, Neural Netw. 22 (2009) 1419-1431.
35. J. Gotman, Automatic recognition of epileptic seizures in the EEG, Electroencephalogr. Clin. Neurophysiol. 54(5) (1982) 530-540.
36. A. M. Murro, D. W. King, J. R. Smith, B. B. Gallagher, H. F. Flanigin and K. Meador, Computerized seizure detection of complex partial seizures, Electroencephalogr. Clin. Neurophysiol. 79(4) (1991) 330-333.
37. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, Alzheimer's disease and models of computation: Imaging, classification, and neural models, J. Alzheimers Dis. 7(3) (2005) 187-199.
38. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of alzheimer's disease, Neurosci. Lett. 444(2) (2008) 190-194.
39. U. R. Acharya, S. V. Sree, P. C. A. Ang, R. Yanti and S. Jasjit, Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals, Int. J. Neural Syst. 22(2) (2012) 1250002.
40. V. Srinivasan, C. Eswaran and N. Sriraam, Approximate entropy based epileptic EEG detection using artificial neural networks, IEEE Trans. Inf. Technol. Biomed. 11(3) (2007) 288-295.
41. E. D. Ubeyli, Lyapunov exponents/probabilistic neural networks for analysis of EEG signals, Expert Syst. Appl. 37(2) (2010) 985-992.
42. U. R. Acharya, S. V. Sree and J. S. Suri, Automatic detection of epileptic EEG signals using higher order cumulant features, Int. J. Neural Syst. 21(5) (2011) 403-414.
43. Y. Wang, W. Zhou, Q. Yuan, X. Li, Q. Meng, X. Zhao and J. Wang, Comparison of ictal and interictal EEG signals using fractal features, Int. J. Neural Syst. 23(6) (2013) 1350028.
44. H. Adeli, Z. Zhou and N. Dadmehr, Analysis of EEG records in an epileptic patient using wavelet transform, J. Neurosci. Methods 123(1) (2003) 69-87.
45. Y. U. Khan and J. Gotman, Wavelet based automatic seizure detection in intracerebral electroencephalogram, Clin. Neurophysiol. 114(5) (2003) 898-908.
46. Y. Liu, W. Zhou, Q. Yuan and S. Chen, Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG, IEEE Trans. Neural Syst. Rehabil. Eng. 20(6) (2012) 749-755.
47. D. L. Donoho, Compressed sensing, IEEE Trans. Inform. Theory 52(4) (2006) 1289-1306.
48. E. J. Candes and M. B. E. Wakin, An introduction to compressive sampling, IEEE Signal Process. Mag. 25(2) (2008) 21-30.
49. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry and Y. Ma, Robust face recognition via sparse representation, IEEE Trans. Pattern Anal. Mach. Intell. 31(2) (2009) 210-227.
50. J. Yang, K. Yu, Y. Gong and T. Huang, Linear spatial pyramid matching using sparse coding for image classification, in Proc. CVPR (2009), pp. 1794-1801.
51. Q. Yuan, W. Zhou, S. Yuan, X. Li, J. Wang and G. Jia, Epileptic EEG Classification Based On Kernel Sparse Representation, Int. J. Neural Syst. 24(4) (2014) 1450015.
52. L. Zhang, M. Yang and X. Feng, Sparse representation or collaborative representation: Which helps face recognition? in Proc. IEEE Int. Conf. Computer Vision (2011), pp. 471-478.
53. K. R. Müller, S. Mika, G. Rätsch, K. Tsuda and B. Schölkopf, An introduction to kernel-based learning algorithms, IEEE Trans. Neural. Netw. 12(2) (2001) 181-201.
54. L. Zhang, W. Zhou, P. Chang, J. Liu, Z. Yan, T. Wang and F. Li, Kernel sparse representationbased classifier, IEEE Trans. Signal Process. 60(4) (2012) 1684-1695.
55. C. J. C. Burges, A tutorial on support vector machines for pattern recognition, Data Min. Knowl. Discov. 2(2) (1998) 121-167.
56. D. Li, L. Xu, E. D. Goodman, Y. Xu and Y. Wu, Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking, Integr. Comput.-Aided Eng. 20(3) (2013) 201-216.
57. B. Schölkopf, A. Smola and K. R.Müller, Nonlinear component analysis as a kernel eigenvalue problem, Neural Comput. 10(5) (1998) 1299-1319.
58. S. Mika, G. Ratsch, J. Weston, B. Schölkopf and K. R. Müller, Fisher discriminant analysis with kernels, in Neural Networks for Signal Processing IX (1999), pp. 41-48.
59. Freiburg Seizure Prediction Project, Freiburg, Germany, Available at: http://epilepsy.uni-freburg.de/freiburg-seizure-prediction-project/eeg-database.
60. T. Kalayci and O. Ozdamar, Wavelet preprocessing for automated neural network detection of EEG spikes, IEEE Eng. Med. Biol. 14(2) (1995) 160-166.
61. A. Subasi, EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Syst. Appl. 32(4) (2007) 1084-1093.
62. S. Yuan, W. Zhou, Q. Yuan, Y. Zhang and Q. Meng, Automatic seizure detection using diffusion distance and BLDA in intracranial EEG, Epilepsy Behav. 31 (2014) 339-345.
63. W. Zhou, Y. Liu, Q. Yuan and X. Li, Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG, IEEE Trans. Biomed. Eng. 60(12) (2013) 3375-3381.
64. K. K. Majumdar and P. Vardhan, Automatic seizure detection in ECoG by differential operator and windowed variance, IEEE Trans. Neural Syst. Rehabil. Eng. 19(4) (2011) 356-365.
65. J. Yin, Z. Liu, Z. Jin and W. Yang, Kernel sparse representation based classification, Neurocomputing 77(1) (2012) 120-128.
66. S. Yang, Y. Han and X. R. Zhang, A sparse kernel representation method for image classification, IJCNN (2012), pp. 1-7.
67. A. Temko, E. Thomas, W. Marnane, G. Lightbody and G. Boylan, EEG-based neonatal seizure detection with Support Vector Machines, Clin. Neurophysiol. 122(3) (2011) 464-473.
68. E. C. Chua, K. Patel, M. Fitzsimons and C. J. Bleakley, Improved patient specific seizure detection during pre-surgical evaluation, Clin. Neurophysiol. 122(4) (2011) 672-679.
69. S. Raghunathan, A. Jaitli and P. P. Irazoqui, Multistage seizure detection techniques optimized for low-power hardware platforms, Epilepsy Behav. 22 (2011) 61-68.
70. A. F. Rabbi and R. Fazel-Rezai, A fuzzy logic system for seizure onset detection in intracranial EEG, Comput. Intell. Neurosci. 2012 (2012) 1.