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A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals

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Abstract

Alcoholism is a critical disorder related to the central nervous system, caused due to repeated and excessive consumption of alcohol. The electroencephalogram (EEG) signals are used to depict brain activities. It can also be employed for diagnosis of subjects consuming excessive alcohol. In this study, we have developed an automated system for the classification of alcoholic and normal EEG signals using a recently designed duration-bandwidth product (DBP), optimized three-band orthogonal wavelet filter bank (TBOWFB), and log-energy (LE). First, we obtain sub-bands (SBs) of EEG signals using the TBOWFB. Then, we use logarithms of the energies of the SBs as the discriminating features which are fed to the least square support vector machine (LS-SVM) for the discrimination of normal and alcoholic EEG signals. We have achieved a classification accuracy (CA) of 97.08%, with ten-fold cross validation strategy. The proposed model presents a promising performance, and therefore it can be used in a practical setup to assist the medical professionals in the diagnosis of alcoholism using EEG signals automatically.

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References

  1. Acharya UR, Bhat S, Adeli H, Adeli A et al (2014) Computer-aided diagnosis of alcoholism-related eeg signals. Epilepsy Behav 41:257–263

    Article  Google Scholar 

  2. Acharya UR, Mookiah MRK, Koh JE, Tan JH, Bhandary SV, Rao AK, Hagiwara Y, Chua CK, Laude A (2017) Automated diabetic macular edema (dme) grading system using dwt, dct features and maculopathy index. Comput Biol Med 84:59–68

    Article  Google Scholar 

  3. Acharya UR, Sree SV, Chattopadhyay S, Suri JS (2012) Automated diagnosis of normal and alcoholic eeg signals. Int J Neural Syst 22(3):1250,011

    Article  Google Scholar 

  4. Acharya UR, Sree SV, Krishnan MMR, Molinari F, Saba L, Ho SYS, Ahuja AT, Ho SC, Nicolaides A, Suri JS (2012) Atherosclerotic risk stratification strategy for carotid arteries using texture-based features. Ultrasound Med Biol 38(6):899–915

    Article  Google Scholar 

  5. Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS (2013) Automated EEG analysis of epilepsy: a review. Knowl-Based Syst 45:147–165

    Article  Google Scholar 

  6. Bhati D, Sharma M, Pachori RB, Gadre V (2017) Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification. Digital Signal Process 62:259–273

    Article  Google Scholar 

  7. Bhati D, Sharma M, Pachori RB, Nair SS, Gadre V (2016) Design of time–frequency optimal three-band wavelet filter banks with unit sobolev regularity using frequency domain sampling. Circuits Syst Signal Process 35(12):4501–4531

    Article  MathSciNet  MATH  Google Scholar 

  8. Bhattacharyya A, Sharma M, Pachori RB, Sircar P, Acharya UR (2016) A novel approach for automated detection of focal eeg signals using empirical wavelet transform. Neural Computing and Applications, pp 1–11

  9. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on computational learning theory. ACM, pp 144–152

    Google Scholar 

  10. Burrus C, Gopinath RA, Guo H (1998) Introduction to wavelets and wavelet transforms: A primer

  11. Cherpitel CJ (2009) Alcohol and injuries: emergency department studies in an international perspective. World Health Organization

  12. Chui CK, Lian JA (1995) Construction of compactly supported symmetric and antisymmetric orthonormal wavelets with scale= 3. Appl Comput Harmon Anal 2(1):21–51

    Article  MathSciNet  MATH  Google Scholar 

  13. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  14. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press

  15. Daubechies I (1988) Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math 41 (7):909–996

    Article  MathSciNet  MATH  Google Scholar 

  16. Druesne-Pecollo N, Tehard B, Mallet Y, Gerber M, Norat T, Hercberg S, Latino-Martel P (2009) Alcohol and genetic polymorphisms: effect on risk of alcohol-related cancer. Lancet Oncol 10(2):173–180

    Article  Google Scholar 

  17. Ehlers CL, Havstad J, Prichard D, Theiler J (1998) Low doses of ethanol reduce evidence for nonlinear structure in brain activity. J Neurosci 18(18):7474–7486

    Article  Google Scholar 

  18. Ethem A (2004) Introduction to machine learning (adaptive computation and machine learning). Mass MIT Press, Cambridge

    MATH  Google Scholar 

  19. Faust O, Acharya R, Allen AR, Lin C (2008) Analysis of eeg signals during epileptic and alcoholic states using ar modeling techniques. IRBM 29(1):44–52

    Article  Google Scholar 

  20. Faust O, Yu W, Kadri NA (2013) Computer-based identification of normal and alcoholic eeg signals using wavelet packets and energy measures. J Mech Med Biol 13(3):1350,033

    Article  Google Scholar 

  21. Gabor D (1946) Theory of communication. Proc Inst Elec Eng 93(26):429–441

    Google Scholar 

  22. Gopinath RA (1993) Wavelets and filter banks-new results and applications. Ph.D. thesis, Rice University

  23. Howlett M, Nguyen T, Davis R (2002) A 3-channel biorthogonal filter bank construction based on predict and update lifting steps. Real-Time Imaging and Sensing Group

  24. Jayawardena A (2003) 3-band linear phase bi-orthogonal wavelet filter banks. In: Proceedings of the 3rd IEEE international symposium on signal processing and information technology, 2003. ISSPIT 2003. IEEE, pp 46–49

    Google Scholar 

  25. Kannathal N, Acharya UR, Lim CM, Sadasivan P (2005) Characterization of eeg comparative study. Comput Methods Prog Biomed 80(1):17–23

    Article  Google Scholar 

  26. Kohavi R et al (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection, pp 1137–1145

  27. Lin T, Xu S, Shi Q, Hao P (2006) An algebraic construction of orthonormal m-band wavelets with perfect reconstruction. Appl Math Comput 172(2):717–730

    MathSciNet  MATH  Google Scholar 

  28. Mitchell TM, Michell T (1997) Machine learning. McGraw-Hill Series in Computer Science

  29. Organization WH et al (2004) Global status report on alcohol 2004

  30. Patidar S, Pachori RB, Upadhyay A, Acharya UR (2017) An integrated alcoholic index using tunable-q wavelet transform based features extracted from eeg signals for diagnosis of alcoholism. Appl Soft Comput 50:71–78

    Article  Google Scholar 

  31. Peng L, Wang Y (2001) Parameterization and algebraic structure of 3-band orthogonal wavelet systems. Sci China, Ser A Math 44(12):1531–1543

    Article  MathSciNet  MATH  Google Scholar 

  32. Sharma M, Achuth PV, Pachori RB, Gadre V (2017) A parametrization technique to design joint time–frequency optimized discrete-time biorthogonal wavelet bases. Signal Process 135:107–120

    Article  Google Scholar 

  33. Sharma M, Bhati D, Pillai S, Pachori RB, Gadre V (2016) Design of time–frequency localized filter banks: transforming non-convex problem into convex via semidefinite relaxation technique. Circuits Syst Signal Process 35(10):3716–3733

    Article  MathSciNet  Google Scholar 

  34. Sharma M, Dhere A, Pachori RB, Acharya UR (2017) An automatic detection of focal EEG signals using new class of time–frequency localized orthogonal wavelet filter banks. Knowl-Based Syst 118:217–227

    Article  Google Scholar 

  35. Sharma M, Dhere A, Pachori RB, Gadre V (2017) Optimal duration-bandwidth localized antisymmetric biorthogonal wavelet filters. Signal Process 134:87–99

    Article  Google Scholar 

  36. Sharma M, Gadre V, Porwal S (2014) An eigenfilter-based approach to the design of time-frequency localization optimized two-channel linear phase biorthogonal filter banks. Circuits, Systems, and Signal Processing

  37. Sharma M, Kolte R, Patwardhan P, Gadre V (2010) Time-frequency localization optimized biorthogonal wavelets. In: International conference on signal processing and communication (SPCOM), 2010, pp 1–5

    Google Scholar 

  38. Sharma M, Pachori RB, Acharya UR (2017) A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognition Letters. doi:10.1016/j.patrec.2017.03.023. http://www.sciencedirect.com/science/article/pii/S0167865517300995

  39. Sharma M, Singh T, Bhati D, Gadre V (2014) Design of two-channel linear phase biorthogonal wavelet filter banks via convex optimization. In: 2014 international conference on signal processing and communications (SPCOM), pp 1–6. doi:10.1109/SPCOM.2014.6983931

    Google Scholar 

  40. Sharma M, Vanmali AV, Gadre V (2013) Wavelets and fractals in earth system sciences, chap. Construction of Wavelets. CRC Press, Taylor and Francis Group

  41. Strutz T (2009) Design of three-channel filter banks for lossless image compression. In: 2009 16th IEEE international conference on image processing (ICIP). IEEE, pp 2841–2844

    Google Scholar 

  42. Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9 (3):293–300

    Article  MATH  Google Scholar 

  43. Tcheslavski GV, Gonen FF (2012) Alcoholism-related alterations in spectrum, coherence, and phase synchrony of topical electroencephalogram. Comput Biol Med 42(4):394–401

    Article  Google Scholar 

  44. Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York

    Book  MATH  Google Scholar 

  45. Vaidyanathan P (1987) Quadrature mirror filter banks, m-band extensions and perfect-reconstruction techniques. IEEE ASSP Mag 4(3):4–20

    Article  Google Scholar 

  46. Vaidyanathan PP (1993) Multirate systems and filter banks. Prentice-Hall signal processing series. N.J. Prentice Hall, Englewood Cliffs

    Google Scholar 

  47. Vetterli M (1987) A theory of multirate filter banks. IEEE Trans Acoust Speech Signal Process 35(3):356–372

    Article  Google Scholar 

  48. Vetterli M, Herley C (1992) Wavelets and filter banks: theory and design. IEEE Trans Signal Process 40 (9):2207–2232

    Article  MATH  Google Scholar 

  49. Zhao P, Zhao C (2013) Three-channel symmetric tight frame wavelet design method. Inf Technol J 12 (4):623

    Article  Google Scholar 

  50. Zhu W, Wang X, Ma Y, Rao M, Glimm J, Kovach JS (2003) Detection of cancer-specific markers amid massive mass spectral data. Proc Natl Acad Sci 100(25):14,666–14,671

    Article  MathSciNet  MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, 380026, India

    Manish Sharma & Dipankar Deb

  2. Nagee Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore

    U. Rajendra Acharya

  3. Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore

    U. Rajendra Acharya

  4. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaya, Malaysia

    U. Rajendra Acharya

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  1. Manish Sharma
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  2. Dipankar Deb
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  3. U. Rajendra Acharya
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Correspondence to Manish Sharma.

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Sharma, M., Deb, D. & Acharya, U.R. A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals. Appl Intell 48, 1368–1378 (2018). https://doi.org/10.1007/s10489-017-1042-9

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  • DOI: https://doi.org/10.1007/s10489-017-1042-9

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