Multifractal analysis and relevance vector machine-based automatic seizure detection in intracranial EEG

International Journal of Neural Systems

Volumn 25, Issue 6, 2015, Pages

  Zhang, Yanli ^a,b   Zhou, Weidong ^a   Yuan, Shasha ^a  

^a SHANDONG UNIVERSITY   (China)

^b SHANDONG INSTITUTE OF BUSINESS AND TECHNOLOGY   (China)

Author keywords

EEG; multifractal analysis; relevance vector machine; seizure detection

Indexed keywords

FEATURE EXTRACTION; FRACTAL DIMENSION; FRACTALS; TIME SERIES ANALYSIS;

AUTOMATIC SEIZURE DETECTIONS; ELECTRO-ENCEPHALOGRAM (EEG); MULTI-FRACTAL SPECTRUM; MULTIFRACTAL ANALYSIS; MULTIFRACTAL FORMALISM; MULTIFRACTAL STRUCTURE; PERFORMANCE ASSESSMENT; RELEVANCE VECTOR MACHINE;

ELECTROENCEPHALOGRAPHY;

ELECTROCORTICOGRAPHY; FRACTAL ANALYSIS; HUMAN; SEIZURES; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

ELECTROCORTICOGRAPHY; FRACTALS; HUMANS; SEIZURES; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

EID: 84954027508     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065715500203     Document Type: Article

References (65)

1. R. S. Fisher, W. van Emde Boas, W. Blume, C. Elger, P. Genton, P. Lee and J. Engel J., Epileptic seizures and epilepsy: Definitions proposed by the Inter-national League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE), Epilepsia 46 (4) (2005) 470-472.
2. 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.
3. 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.
4. 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.
5. H. Adeli and S. Ghosh-Dastidar, Automated EEGBased Diagnosis of Neurological Disorders: Inventing the Future of Neurology (CRC Press, Florida, 2010), p. 72.
6. 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.
7. 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.
8. 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.
9. 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.
10. Z. Sankari, H. Adeli and A. Adeli, Intrahemispheric, Interhemispheric and distal EEG coherence in Alzheimer's disease, Clin. Neurophysiol. 122 (5) (2011) 897-906.
11. Z. Sankari, H. Adeli and A. Adeli, Wavelet coherence model for diagnosis of Alzheimer's disease, Clin. EEG Neurosci. 43 (3) (2012) 268-278.
12. 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.
13. M. Kwon, S. Kavuri and M. Lee, Action-perception 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.
14. 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.
15. S. Ghosh-Dastidar and H. Adeli, Improved spiking neural networks for EEG classification and epilepsy and seizure detection, Integr. Computer-Aided Eng. 14 (3) (2007) 187-212.
16. 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.
17. S. Ghosh-Dastidar and H. Adeli, Spiking neural networks, Int. J. Neural Syst. 19 (4) (2009) 295-308.
18. 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 (10) (2009) 1419-1431.
19. P. Sharma, Y. U. Khan, O. Farooq, M. Tripathi and H. Adeli, A wavelet-statistical features approach for nonconvulsive seizure detection, Clin. EEG Neurosci. 45 (4) (2014) 274-284.
20. 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.
21. 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 EEGsignals for automated seizure prediction, Int. J. Neural Syst. 23 (5) (2013) 1350023.
22. 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.
23. S. Yuan, W. Zhou, Q. Yuan, L. Li, Q. Wu, X. Zhao and J. Wang, Kernel collaborative representation based automatic seizure detection in intracranial EEG, Int. J. Neural Syst. 25 (2) (2015) 1550003.
24. Y. Zhang, W. Zhou, Q. Yuan and Q.Wu, A low computation cost method for seizure prediction, Epilepsy Res. 108 (8) (2014) 1357-1366.
25. 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.
26. Y. Zhang, W. Zhou, S. Yuan and Q. Yuan, Seizure detection method based on fractal dimension and gradient boosting, Epilepsy Behav. 43 (2015) 30-38.
27. J. Gotman, Automatic recognition of epileptic seizures in the EEG, Electroencephalogr. Clin. Neurophysiol. 54 (5) (1982) 530-540.
28. 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.
29. L. Ayoubian, H. Lacoma and J. Gotman, Automatic seizure detection in SEEG using high frequency activities in wavelet domain, Med. Eng. Phys. 35 (3) (2013) 319-328.
30. U. R. Acharya, S. V. Sree, P. C. A. Ang, R. Yanti and S. Jasjit, Application of nonlinear and wavelet based features for the automated identification of epileptic EEG signals, Int. J. Neural Syst. 22 (2) (2012) 1250002.
31. 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.
32. 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.
33. 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.
34. H. Ocak, Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy, Expert Syst. Appl. 36 (2) (2009) 2027-2036.
35. 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.
36. 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.
37. C. J. Christophe and K. B. Gregory, Characterization of early partial seizure onset: Frequency, complexity and entropy, Clin. Neurophysiol. 123 (2012) 658-669.
38. M. Zavar, S. Rahati, M. R. Akbarzadeh-T and H. Ghasemifard, Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection, Expert Syst. Appl. 38 (9) (2011) 10751-10758.
39. R. Esteller, G. Vachtsevanos, J. Echauz, T. Henry, P. Pennell, C. Epstein, R. Bakay, C. Bowen and B. Litt, Fractal dimension characterizes seizure onset in epileptic patients, in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Vol. 4 (1999), pp. 2343-2346.
40. M. Ahmadlou, H. Adeli and A. Adeli, Fractality and a wavelet-chao methodology for EEG-based diagnosis of Alzheimer's disease, Alzheimer Dis. Assoc. Disord. 25 (1) (2011) 85-92.
41. M. Ahmadlou, H. Adeli and A. Adeli, Graph theoretical analysis of organization of functional brain networks in ADHD, Clin. EEG Neurosci. 43 (1) (2012) 5-13.
42. M. Ahmadlou and H. Adeli, Visibility graph similarity: A new measure of generalized synchronization in coupled dynamic systems, Physica D 241 (4) (2012) 326-332.
43. M. Ahmadlou, H. Adeli and A. Adeli, Improved visibility graph fractality with application for diagnosis of autism spectrum disorder, Physica A 391 (20) (2012) 4720-4726.
44. M. Ahmadlou, H. Adeli and A. Adeli, Fractality analysis of frontal brain in major depressive disorder, Int. J. Psychophysiol. 85 (2) (2012) 206-211.
45. S. Dutta, D. Ghosh, S. Samanta and S. Dey, Multifractal parameters as an indication of different physiological and pathological states of the human brain, Physica A 396 (2014) 155-163.
46. R. Lopes and N. Betrouni, Fractal and multifractal analysis: A review, Med. Image Anal. 13 (4) (2009) 634-649.
47. B. Weiss, Z. Clemens, R. Bódizs, Z. Vágó and P. Halász, Spatio-temporal analysis of monofractal and multifractal properties of the human sleep EEG, J. Neurosci. Methods 185 (1) (2009) 116-124.
48. N. Brodu, F. Lotte and A. Lécuyer, Exploring two novel features for EEG-based brain-computer interfaces: Multifractal cumulants and predictive complexity, Neurocomputing 79 (2012) 87-94.
49. 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.
50. M. E. Tipping, Sparse Bayesian learning and the relevance vector machine, J. Mach. Learn. Res. 1 (2001) 211-244.
51. B. Demir and S. Erturk, Hyperspectral image classification using relevance vector machines, IEEE Geosci. Remote Sens. Lett. 4 (2007) 586-590.
52. O. Williams, A. Blake and R. Cipolla, Sparse Bayesian learning for efficient visual tracking, IEEE Trans. Pattern Anal. Mach. Intell. 27 (2005) 1292-1304.
53. B. Ribeiro, A. Marques, J. Henriques and M. Antunes, Choosing real-time predictors for ventricular arrhythmia detection, Int. J. Pattern Recogn. Artif. Intell. 21 (2007) 1249-1263.
54. B. Schelter, M. Winterhalder, T. Maiwald, A. Brandt, A. Schad, J. Timmer and A. Schulze-Bonhage, Do false predictions of seizures depend on the state of vigilance? A report from two seizureprediction methods and proposed remedies, Epilepsia 47 (12) (2006) 2058-2070.
55. A. Aarabi, R. Fazel-Rezai and Y. Aghakhani, A fuzzy rule-based system for epileptic seizure detection in intracranial EEG, Clin. Neurophysiol. 120 (9) (2009) 1648-1657.
56. T. Maiwald, M. Winterhalder, R. Aschenbrenner-Scheibe, H. U. Voss, A. Schulze-Bonhage and J. Timmer, Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic, Physica D 194 (2004) 357-368.
57. B. B. Mandelbrot, Multifractal measures, specially for the geophysicist, Pure Appl. Geophys. 131 (1-2) (1989) 5-42.
58. T. C. Halsey, M. H. Jensen, L. P. Kadano, I. Procaccia and B. I. Shraiman, Fractal measures and their singularities: The characterization of strange sets, Phys. Rev. A 33 (1986) 1141-1151.
59. E. V. Vázquez, J. G. V. Miranda and J. Paz-Ferreiro, A multifractal approach to characterize cumulative rainfall and tillage effects on soil surface microtopography and to predict depression storage, Biogeosciences 7 (2) (2010) 2989-3004.
60. A. Temko, E. Thomas, W. Marnane, G. Lightbody and G. B. Boylan, EEG-based neonatal seizure detection with support vector machines, Clin. Neurophysiol 122 (2011) 464-473.
61. A. Temko, E. Thomas, W. Marnane, G. Lightbody and G. B. Boylan, Performance assessment for EEGbased neonatal seizure detectors, Clin. Neurophysiol. 122 (2011) 474-482.
62. H. Hinrikus, M. Bachmann, J. Kalda, M. Sakki, J. Lass and R. Tomson, Methods of electroencephalographic signal analysis for detection of small hidden changes, Nonlinear Biomed. Phys. 1 (1) (2007) 9.
63. S. Raghunathan, A. Jaitli and P. P. Irazoqui, Multistage seizure detection techniques optimized for low-power hardware platforms, Epilepsy Behav. 22 (2011) S61-68.
64. 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.
65. Q. Yuan, W. Zhou, Y. Liu and J. Wang, Epileptic seizure detection with linear and nonlinear features, Epilepsy Behav. 24 (4) (2012) 415-421.