An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures

Entropy

Volumn 17, Issue 8, 2015, Pages 5218-5240

  Sharma, Rajeev ^a   Pachori, Ram Bilas ^a   Rajendra Acharya, U ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY INDORE   (India)

^b DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

Author keywords

Classifier; EEG; Entropy; Epilepsy; Wavelet

Indexed keywords

EID: 84940481172     PISSN: None     EISSN: 10994300     Source Type: Journal    
DOI: 10.3390/e17085218     Document Type: Article

References (70)

1. Cross, D.J.; Cavazos, J.E. The Role of Sprouting and Plasticity in Epileptogenesis and Behavior; Behavioral Aspects of Epilepsy; Demos Medical Publishing: New York, NY, USA, 2007.
2. Acharya, U.R.; Sree, S.V.; Swapna, G.;Martis, R.J.; Suri, J.S. Automated EEG analysis of epilepsy:A review. Knowledge-Based Syst. 2013, 45, 147-165.
3. Pati, S.; Alexopoulos, A.V. Pharmacoresistant epilepsy: From pathogenesis to current and emerging therapies. Clevel. Clin. J. Med. 2010, 77, 457-467.
4. Ortega, G.J.; Menendez de la Prida, L.; Sola, R.G.; Pastor, J. Synchronization clusters of interictal activity in the lateral temporal cortex of epileptic patients: Intraoperative electrocorticographic analysis. Epilepsia 2008, 49, 269-280.
5. Van Mierlo, P.; Carrette, E.; Hallez, H.; Raedt, R.; Meurs, A.; Vandenberghe, S.; Van Roost, D.; Boon, P.; Staelens, S.; Vonck, K. Ictal-onset localization through connectivity analysis of intracranial EEG signals in patients with refractory epilepsy. Epilepsia 2013, 54, 1409-1418.
6. Towle, V.L.; Carder, R.K.; Khorasani, L.; Lindberg, D. Electrocorticographic coherence patterns. J. Clin. Neurophysiol. 1999, 16, 528-547.
7. Schevon, C.A.; Cappell, J.; Emerson, R.; Isler, J.; Grieve, P.; Goodman, R.; Mckhann, G., Jr.; Weiner, H.; Doyle,W.; Kuzniecky, R.; Devinsky, O.; Gilliam, F. Cortical abnormalities in epilepsy revealed by local EEG synchrony. NeuroImage 2007, 35, 140-148.
8. Lehnertz, K.; Elger, C. Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Electroencephalogr. Clin. Neurophysiol. 1995, 95, 108-117.
9. Andrzejak, R.G.; Schindler, K.; Rummel, C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E 2012, 86, 046206.
10. Gajic, D.; Djurovic, Z.; Gligorijevic, J.; Di Gennaro, S.; Savic-Gajic, I. Detection of epileptiform activity in EEG signals based on time-frequency and nonlinear analysis. Front. Comput. Neurosci. 2015, 9.
11. Gajic, D.; Djurovic, Z.; Di Gennaro, S.; Gustafsson, F. Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomed. Eng. Appl. Basis Commun. 2014, 26, 1450021.
12. Subasi, A. EEG signal classification using wavelet feature extraction and a mixture of expertmodel. Expert Syst. Appl. 2007, 32, 1084-1093.
13. Kannathal, N.; Choo, M.L.; Acharya, U.R.; Sadasivan, P.K. Entropies for detection of epilepsy in EEG. Comput. Methods Progr. Biomed. 2005, 80, 187-194.
14. Acharya, U.R.; Molinari, F.; Sree, S.V.; Chattopadhyay, S.; Ng, K.H.; Suri, J.S. Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 2012, 7, 401-408.
15. Daubechies, I. Ten Lectures on Wavelets; SIAM: Philadelphia, PA, USA, 1992.
16. Acharya, U.R.; Vidya, K.S.; Ghista, D.N.; Lim, W.J.E.; Molinari, F.; Sankaranarayanan, M. Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method. Knowledge-Based Syst. 2015, 81, 56-64.
17. Rosso, O.A.; Blanco, S.; Yordanova, J.; Kolev, V.; Figliola, A.; Schürmann, M.; Başar, E. Wavelet entropy: A new tool for analysis of short duration brain electrical signals. J. Neurosci. Methods 2001, 105, 65-75.
18. Rényi, A. On measures of entropy and information. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability: Contributions to the Theory of Statistics; University of California Press: Berkeley, CA, USA, 1961; Volume 1, pp. 547-561.
19. Chen, J.; Li, G. Tsallis wavelet entropy and its application in power signal analysis. Entropy 2014, 16, 3009-3025.
20. Ramirez-Villegas, J.F.; Ramirez-Moreno, D.F. Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions. Neurocomputing 2012, 77, 82-100.
21. Chen, W.; Wang, Z.; Xie, H.; Yu, W. Characterization of surface EMG signal based on fuzzy entropy. IEEE Trans. Neural Syst. Rehabil. Eng. 2007, 15, 266-272.
22. Xie, H.B.; Chen,W.T.; He,W.X.; Liu, H. Complexity analysis of the biomedical signal using fuzzy entropy measurement. Appl. Soft Comput. 2011, 11, 2871-2879.
23. Nicolaou, N.; Georgiou, J. Detection of epileptic electroencephalogram based on permutation entropy and support vector machine. Expert Syst. Appl. 2012, 39, 202-209.
24. Bandt, C.; Pompe, B. Permutation entropy: A natural complexity measure for time series. Phys. Rev. Lett. 2002, 88, 174102.
25. Riedl, M.; Müller, A.; Wessel, N. Practical considerations of permutation entropy. Eur. Phys. J. Spec. Top. 2013, 222, 249-262.
26. Acharya, U.R.; Sree, S.V.; Ang, P.C.A.; Yanti, R.; Suri, J.S. Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int. J. Neural Syst. 2012, 22, 1250002.
27. Chua, K.C.; Chandran, V.; Acharya, U.R.; Lim, C.M. Analysis of epileptic EEG signals using higher order spectra. J. Med. Eng. Technol. 2009, 33, 42-50.
28. Sharma, R.; Pachori, R.B.; Acharya, U.R. Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 2015, 17, 669-691.
29. Acharya, U.R.; Ng, E.Y.K.; Eugene, L.W.J.; Noronha, K.P.; Min, L.C.; Nayak, K.P.; Bhandary, S.V. Decision support system for the glaucoma using Gabor transformation. Biomed. Signal Process. Control 2015, 15, 18-26.
30. Kailath, T. The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans. Commun. Technol. 1967, 15, 52-60.
31. Zhu, W.;Wang, X.; Ma, Y.; Rao, M.; Glimm, J.; Kovach, J.S. Detection of cancer-specific markers amid massive mass spectral data. Proc. Natl. Acad. Sci. USA 2003, 100, 14666-14671.
32. Derryberry, D.R.; Schou, S.B.; Conover, W.J. Teaching rank-based tests by emphasizing structural similarities to corresponding parametric tests. J. Stat. Educ. 2010, 18, 1-19.
33. Kruskal, W.H. Historical notes on the Wilcoxon unpaired two-sample test. J. Am. Stat. Assoc. 1957, 52, 356-360.
34. Mann, H.B.; Whitney, D.R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 1947, 18, 50-60.
35. Bergmann, R.; Ludbrook, J.; Spooren, W.P.J.M. Different outcomes of the Wilcoxon-Mann-Whitney test from different statistics packages. Am. Stat. 2000, 54, 72-77.
36. Theodoridis, S.; Koutroumbas, K. Feature selection. In Pattern Recognition, second ed.; Academic Press: San Diego, CA, USA, 2003; pp. 163-205.
37. Kohavi, R. A. study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95), Montreal, QC, Canada, 20-25 August 1995; Morgan Kaufmann: San Francisco, CA, USA, 1995; pp. 1137-1143.
38. Swapna, G.; Acharya, U.R.; Sree, S.V.; Suri, J.S. Automated detection of diabetes using higher order spectral features extracted from heart rate signals. Intell. Data Anal. 2013, 17, 309-326.
39. Mao, K.; Tan, K.C.; Ser, W. Probabilistic neural-network structure determination for pattern classification. IEEE Trans. Neural Netw. 2000, 11, 1009-1016.
40. Han, J.; Kamber, M.; Pei, J. Classification: Advanced methods. In Data Mining, 3rd ed.; Kamber, J.H., Pei, J., Eds.; TheMorgan Kaufmann Series in DataManagement Systems;Morgan Kaufmann:Boston, MA, USA, 2012; pp. 393-442.
41. Ishibuchi, H.; Nakashima, T. Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes. IEEE Trans. Ind. Electron. 1999, 46, 1057-1068.
42. Acharya, U.R.; Sree, S.V.; Chattopadhyay, S.; Yu, W.; Ang, P.C.A. Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. Int. J. Neural Syst. 2011, 21, 199-211.
43. Acharya, U.R.; Sree, S.V.; Ang, P.C.A.; Yanti, R.; Suri, J.S. Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int. J. Neural Syst. 2012, 22, 1250002.
44. Vapnik, V.N. The Nature of Statistical Learning Theory; Springer: New York, NY, USA, 2000.
45. Suykens, J.A.K.; Vandewalle, J. Least squares support vector machine classifiers. Neural Process. Lett. 1999, 9, 293-300.
46. Pachori, R.B.; Sharma, R.; Patidar, S. Classification of normal and epileptic seizure EEG signals based on empirical mode decomposition. In Complex System Modelling and Control Through Intelligent Soft Computations; Zhu, Q., Azar, A.T., Eds.; Springer: Switzerland, 2015; Volume 319, pp. 367-388.
47. Joshi, V.; Pachori, R.B.; Vijesh, A. Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed. Signal Process. Control 2014, 9, 1-5.
48. Sharma, R.; Pachori, R.B. Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst. Appl. 2015, 42, 1106-1117.
49. Ghista, D.N. Physiological systems' numbers in medical diagnosis and hospital cost-effective operation. J. Mech. Med. Biol. 2004, 4, 401-418.
50. Acharya, U.R.; Faust, O.; Sree, S.V.; Ghista, D.N.; Dua, S.; Joseph, P.; Ahamed, V.I.T.; Janarthanan, N.; Tamura, T. An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes. Comput. Methods Biomech. Biomed. Eng. 2011, 16, 222-234.
51. Acharya, U.R.; Sree, S.V.; Krishnan, M.M.R.; Krishnananda, N.; Ranjan, S.; Umesh, P.; Suri, J.S. Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images. Comput. Methods Programs Biomed. 2013, 112, 624-632.
52. Acharya, U.R.; Faust, O.; Sree, S.V.; Molinari, F.; Garberoglio, R.; Suri, J.S. Cost-effective and non-invasive automated benign & malignant thyroid lesion classification in 3D contrast-enhanced ultrasound using combination of wavelets and textures: A class of ThyroScanTM algorithms. Technol. Cancer Res. Treat. 2011, 10, 371-380.
53. Patidar, S.; Pachori, R.B.; Acharya, U.R. Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals. Knowledge-Based Syst. 2015, 82, 1-10.
54. Acharya, U.R.; Ng, E.Y.K.; Tan, J.H.; Sree, S.V.; Ng, K.H. An integrated index for the identification of diabetic retinopathy stages using texture parameters. J. Med. Syst. 2012, 36, 2011-2020.
55. Ocak, H. Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Signal Process. 2008, 88, 1858-1867.
56. Yuen, S.Y.; Chow, C.K. A genetic algorithm that adaptively mutates and never revisits. IEEE Trans. Evol. Comput. 2009, 13, 454-472.
57. McKight, P.E.; Najab, J. Kruskal-Wallis Test. Corsini Encycl. Psychol. 2010.
58. Pachori, R.B. Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res. Lett. Signal Process. 2008, 2008, 293056.
59. Pachori, R.B.; Bajaj, V. Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput. Methods Programs Biomed. 2011, 104, 373-381.
60. Shah, M.; Saurav, S.; Sharma, R.; Pachori, R.B. Analysis of epileptic seizure EEG signals using reconstructed phase space of intrinsic mode functions. In Proceedings of 9th International Conference on Industrial and Information Systems, Gwalior, India, 15-17 December 2014; pp. 1-6.
61. Pachori, R.B.; Avinash, P.; Shashank, K.; Sharma, R.; Acharya, U.R. Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals. Expert Syst. Appl. 2015, 42, 4567-4581.
62. Azar, A.T.; El-Said, S.A. Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput. Appl. 2014, 24, 1163-1177.
63. Naro, D.; Rummel, C.; Schindler, K.; Andrzejak, R.G. Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score. Phys. Rev. E 2014, 90, 032913.
64. Subramaniyam, N.P.; Hyttinen, J. Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks. Phys. Rev. E 2015, 91, 022927.
65. Ben-Jacob, E.; Doron, I.; Gazit, T.; Rephaeli, E.; Sagher, O.; Towle, V.L. Mapping and assessment of epileptogenic foci using frequency-entropy templates. Phys. Rev. E 2007, 76, 051903.
66. Marciani, M.G.; Stefanini, F.; Stefani, N.; Maschio, M.C.E.; Gigli, G.L.; Roncacci, S.; Caltagirone, C.; Bernardi, G. Lateralization of the epileptogenic focus by computerized EEG study and neuropsychological evaluation. Int. J. Neurosci. 1992, 66, 53-60.
67. Sabesan, S.; Good, L.B.; Tsakalis, K.S.; Spanias, A.; Treiman, D.M.; Iasemidis, L.D. Information flow and application to epileptogenic focus localization from intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 2009, 17, 244-253.
68. Warren, C.P.; Hu, S.; Stead, M.; Brinkmann, B.H.; Bower, M.R.; Worrell, G.A. Synchrony in normal and focal epileptic brain: The seizure onset zone is functionally disconnected. J. Neurophysiol. 2010, 104, 3530-3539.
69. Zhu, G.; Li, Y.;Wen, P.P.;Wang, S.; Xi,M. Epileptogenic focus detection in intracranial EEG based on delay permutation entropy. In Proceeding of 2013 International Symposium on Computational Models for Life Science, Sydney, Australia, 27-29 November 2013; Volume 1559; pp. 31-36.
70. Sharma, R.; Pachori, R.B.; Gautam, S. Empirical mode decomposition based classification of focal and non-focal EEG signals. In Proceedings of 2014 International Conference on Medical Biometrics, Shenzhen, China, 30 May-1 June 2014; pp. 135-140.