RETRACTED ARTICLE: A novel approach for automated detection of focal EEG signals using empirical wavelet transform

Neural Computing and Applications - Tập 29 Số 8 - Trang 47-57 - 2018
Abhijit Bhattacharyya1, Manish Sharma1, Ram Bilas Pachori1, Pradip Sircar2, U. Rajendra Acharya3
1Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
2Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
3Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore

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Tài liệu tham khảo

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

Andrzejak RG, Schindler K, Rummel C (2012) Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys Rev E 86(4):046206

Bajaj V, Pachori RB (2012) Separation of rhythms of EEG signals based on Hilbert–Huang transformation with application to seizure detection. In: Convergence and hybrid information technology, pp 493–500

Cohen ME, Hudson DL, Deedwania PC (1996) Applying continuous chaotic modeling to cardiac signal analysis. IEEE Eng Med Biol Mag 15(5):97–102

Das AB, Bhuiyan MIH (2016) Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed Signal Process Control 29:11–21

Daubechies I et al (1992) Ten lectures on wavelets, vol 61. SIAM, Philadelphia

Freund RJ, Wilson WJ, Mohr DL (2010) Statistical methods, 3rd ed. Academic Press, Burlington, MA, USA  

Ghorbani MA, Kisi O, Aalinezhad M (2010) A probe into the chaotic nature of daily streamflow time series by correlation dimension and largest Lyapunov methods. Appl Math Model 34(12):4050–4057

Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61(16):3999–4010

Heal K, Navarro K, Wollner M, Gilles EYJ, Kerr W, Douglas PK, Meyer T (2013) Epilepsy classification, EEG analysis, and EEG-FMRI fusion. Technical report. http://www.math.ucla.edu/~bertozzi/WORKFORCE/REU%202013/Epilepsy/epilepsy_eeg_fmri_report.pdf

Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the royal society of London A: mathematical, physical and engineering sciences, vol 454. The Royal Society, pp 903–995

Kantz H, Schreiber T (2004) Nonlinear time series analysis, vol 7. Cambridge University Press, Cambridge

Kohavi R et al (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: 14th international joint conference on artificial intelligence,  pp 1137–1145  

Kroemer KHE, Kroemer HJ (1997) Engineering physiology: bases of human factors/ergonomics. Wiley, London

Newton MR et al (1995) SPECT in the localisation of extratemporal and temporal seizure foci. J Neurol Neurosurg Psychiatry 59(1):26–30

Pachori RB, Sharma R, Patidar S (2015) Classification of normal and epileptic seizure EEG signals based on empirical mode decomposition. In: Zhu Q, Azar AT (eds) Complex system modelling and control through intelligent soft computations. Studies in fuzziness and soft computing, vol 319. Springer International Publishing, Switzerland, pp 367–388

Pachori RB, Sircar P (2008) EEG signal analysis using FB expansion and second-order linear TVAR process. Sig Process 88(2):415–420

Patidar S, Pachori RB (2014) Classification of cardiac sound signals using constrained tunable-Q wavelet transform. Expert Syst Appl 41(16):7161–7170

Patidar S, Pachori RB, Garg N (2015) Automatic diagnosis of septal defects based on tunable-Q wavelet transform of cardiac sound signals. Expert Syst Appl 42(7):3315–3326

Pachori RB, Hewson D, Snoussi H, Duchêne J (2009) Postural time-series analysis using empirical mode decomposition and second-order difference plots. In: IEEE International conference on acoustics, speech and signal processing, pp 537–540

Roulston MS (1999) Estimating the errors on measured entropy and mutual information. Phys D 125(3):285–294

Salisbury JI, Sun Y (2004) Assessment of chaotic parameters in nonstationary electrocardiograms by use of empirical mode decomposition. Ann Biomed Eng 32(10):1348–1354

Savic I, Thorell JO, Roland P (1995) [11C] Flumazenil positron emission tomography visualizes frontal epileptogenic regions. Epilepsia 36(12):1225–1232

Schiff SJ, Aldroubi A, Unser M, Sato S (1994) Fast wavelet transformation of EEG. Electroencephalogr Clin Neurophysiol 91(6):442–455

Seeck M et al (1998) Non-invasive epileptic focus localization using EEG-triggered functional MRI and electromagnetic tomography. Electroencephalogr Clin Neurophysiol 106(6):508–512

Shah M, Saurav S, Sharma R, Pachori RB (2014) Analysis of epileptic seizure EEG signals using reconstructed phase space of intrinsic mode functions. In: 9th International conference on industrial and information systems, pp 1–6

Sharma R, Pachori RB (2015) Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl 42(3):1106–1117

Sharma R, Pachori RB, Acharya UR (2015) An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures. Entropy 17(8):5218–5240

Sharma R, Pachori RB, Acharya UR (2015) Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17(2):669–691

Sharma R, Pachori RB, Gautam S (2014) Empirical mode decomposition based classification of focal and non-focal EEG signals. In: International conference on medical biometrics, pp 135–140

Sircar P, Pachori RB, Kumar R (2009) Analysis of rhythms of EEG signals using orthogonal polynomial approximation. In: Proceedings of the 2009 international conference on hybrid information technology, pp 176–180

Snoussi H, Amoud H, Doussot M, Hewson D, Duchêne J (2006) Reconstructed phase spaces of intrinsic mode functions. Application to postural stability analysis. In: 28th Annual international conference of the IEEE engineering in medicine and biology society, pp 4584–4589

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

Thakor NV, Xin-Rong G, Yi-Chun S, Hanley DF (1993) Multiresolution wavelet analysis of evoked potentials. IEEE Trans Biomed Eng 40(11):1085–1094

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

Wang N, Lyu MR (2015) Extracting and selecting distinctive EEG features for efficient epileptic seizure prediction. IEEE J Biomed Health Inform 19(5):1648–1659

Zhang L, Zhou W, Jiao L (2004) Wavelet support vector machine. IEEE Trans Syst Man Cybern Part B Cybern 34(1):34–39

Zhong J, Shuren Q, Chenglin P (2008) Study on separation for the frequency bands of EEG signal and frequency band relative intensity analysis based upon EMD. In: 7th WSEAS international conference on signal processing, robotics and automation, University of Cambridge, UK, pp 20–22

Zhu G, Li Y, Wen PP, Wang S, Xi M Epileptogenic focus detection in intracranial EEG based on delay permutation entropy. In: Proceedings of AIP conference, vol 1559. American Institute of Physics, pp 31–36