Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods

References

[1] Epilepsy – World Health Organization. Available from: http://www.who.int/news-room/fact-sheets/detail/epilepsy, 2019.Suche in Google Scholar

[2] Polat K, Güneş S. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput 2007;187:1017–26.10.1016/j.amc.2006.09.022Suche in Google Scholar

[3] Altunay S, Telatar Z, Erogul O. Epileptic EEG detection using the linear prediction error energy. Expert Syst Appl 2010;37:5661–5.10.1016/j.eswa.2010.02.045Suche in Google Scholar

[4] Joshi V, Pachori RB, Vijesh A. Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed Signal Process Control 2014;9:1–5.10.1016/j.bspc.2013.08.006Suche in Google Scholar

[5] Nicolaou N, Georgiou J. Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst Appl 2012;39:202–9.10.1016/j.eswa.2011.07.008Suche in Google Scholar

[6] Mahjoub C, Chaibi S, Lajnef T, Kachouri A. Classification of epileptic cerebral activity using robust features and support vector machines. In: 2nd Int Image Process Appl Syst Conf 2017:1–6.10.1109/IPAS.2016.7880118Suche in Google Scholar

[7] Tiwari AK, Pachori RB, Kanhangad V, Panigrahi BK. Automated diagnosis of epilepsy using key-point-based local binary pattern of EEG signals. IEEE J Biomed Health Informatics 2017;21:888–96.10.1109/JBHI.2016.2589971Suche in Google Scholar PubMed

[8] Kaya Y, Uyar M, Tekin R, Yıldırım S. 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Appl Math Comput 2014;243:209–19.10.1016/j.amc.2014.05.128Suche in Google Scholar

[9] Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 2009;36:2027–36.10.1016/j.eswa.2007.12.065Suche in Google Scholar

[10] Guo L, Rivero D, Pazos A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 2010;193:156–63.10.1016/j.jneumeth.2010.08.030Suche in Google Scholar PubMed

[11] Bhati D, Sharma M, Pachori RB, Gadre VM. Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification. Digit Signal Process 2017;62:259–73.10.1016/j.dsp.2016.12.004Suche in Google Scholar

[12] Bhati D, Pachori RB, Gadre VM. A novel approach for time-frequency localization of scaling functions and design of three-band biorthogonal linear phase wavelet filter banks. Digit Signal Process 2017;69:309–22.10.1016/j.dsp.2017.07.008Suche in Google Scholar

[13] Sharma M, Pachori RB, Acharya UR. A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognit Lett 2017;94:172–9.10.1016/j.patrec.2017.03.023Suche in Google Scholar

[14] Selesnick IW. Wavelet transform with tunable Q-factor. IEEE Trans Signal Process 2011;59:3560–75.10.1109/TSP.2011.2143711Suche in Google Scholar

[15] Patidar S, Panigrahi T. Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomed Signal Process Control 2017;34:74–80.10.1016/j.bspc.2017.01.001Suche in Google Scholar

[16] Sharma M, Pachori RB. A novel approach to detect epileptic seizures using a combination of tunable-Q wavelet transform and fractal dimension. J Mech Med Biol 2017;17:1–20.10.1142/S0219519417400036Suche in Google Scholar

[17] Hassan AR, Siuly S, Zhang Y. Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput Methods Programs Biomed 2016;137:247–59.10.1016/j.cmpb.2016.09.008Suche in Google Scholar PubMed

[18] Bhattacharyya A, Pachori RB, Upadhyay A, Acharya UR. Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl Sci 2017;7:1–18.10.3390/app7040385Suche in Google Scholar

[19] Rahman MM, Bhuiyan MIH, Hassan AR. Sleep stage classification using single-channel EOG. Comput Biol Med 2018;102:211–20.10.1016/j.compbiomed.2018.08.022Suche in Google Scholar PubMed

[20] Subasi A, Ahmed A, Aličković E, Hassan AR. Effect of photic stimulation for migraine detection using random forest and discrete wavelet transform. Biomed Signal Process Control 2019;49:231–9.10.1016/j.bspc.2018.12.011Suche in Google Scholar

[21] Hassan AR, Bhuiyan MIH. Dual tree complex wavelet transform for sleep state identification from single channel electroencephalogram. In: IEEE International Conference on Telecommunications and Photonics (ICTP) 2015:1–5.10.1109/ICTP.2015.7427924Suche in Google Scholar

[22] Hassan AR, Haque MA. Computer-aided sleep apnea diagnosis from single-lead electrocardiogram using dual tree complex wavelet transform and spectral features. In: International Conference on Electrical & Electronic Engineering (ICEEE) 2015:49–52.10.1109/CEEE.2015.7428289Suche in Google Scholar

[23] Bashar SK, Hassan AR, Bhuiyan MIH. Identification of motor imagery movements from eeg signals using dual tree complex wavelet transform. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI) 2015:290–6.10.1109/ICACCI.2015.7275623Suche in Google Scholar

[24] Hassan AR, Subasi A. A decision support system for automated identification of sleep stages from single-channel EEG signals. Knowl Based Syst 2017;128:115–24.10.1016/j.knosys.2017.05.005Suche in Google Scholar

[25] Hassan AR, Bhuiyan MIH. A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features. J Neurosci Methods 2016;271:107–18.10.1016/j.jneumeth.2016.07.012Suche in Google Scholar PubMed

[26] Hassan AR, Bhuiyan MIH. An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting. Neurocomputing 2017;219:76–87.10.1016/j.neucom.2016.09.011Suche in Google Scholar

[27] Hassan AR. Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting. Biomed Signal Process Control 2016;29:22–30.10.1016/j.bspc.2016.05.009Suche in Google Scholar

[28] Hassan AR, Haque MA. An expert system for automated identification of obstructive sleep apnea from single-lead ECG using random under sampling boosting. Neurocomputing 2017;235:122–30.10.1016/j.neucom.2016.12.062Suche in Google Scholar

[29] Sharma RR, Pachori RB. A new method for non-stationary signal analysis using eigenvalue decomposition of the Hankel matrix and Hilbert transform. In: 4th International Conference on Signal Processing and Integrated Networks (SPIN) 2017:484–8.10.1109/SPIN.2017.8049998Suche in Google Scholar

[30] Sharma RR, Pachori RB. Time-frequency representation using IEVDHM-HT with application to classification of epileptic EEG signals. IET Sci Meas Technol 2017;12:72–82.10.1049/iet-smt.2017.0058Suche in Google Scholar

[31] Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc 1998;454:903–95.10.1098/rspa.1998.0193Suche in Google Scholar

[32] Pachori RB. Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res Lett Signal Process 2008;2008:1–5.10.1155/2008/293056Suche in Google Scholar

[33] Oweis RJ, Abdulhay EW. Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomed Eng Online 2011;38:1–15.10.1186/1475-925X-10-38Suche in Google Scholar PubMed PubMed Central

[34] Bajaj V, Pachori RB. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 2012;16:1135–42.10.1109/TITB.2011.2181403Suche in Google Scholar PubMed

[35] Li S, Zhou W, Yuan Q, Geng S, Cai D. Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 2013;43:807–16.10.1016/j.compbiomed.2013.04.002Suche in Google Scholar PubMed

[36] Pachori RB, Patidar S. Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Comput Methods Programs Biomed 2014;113: 494–502.10.1016/j.cmpb.2013.11.014Suche in Google Scholar PubMed

[37] Sharma R, Pachori RB. Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl 2015;42:1106–17.10.1016/j.eswa.2014.08.030Suche in Google Scholar

[38] Sharma RR, Varshney P, Pachori RB, Vishvakarma SK. Automated system for epileptic EEG detection using iterative filtering. IEEE Sensors Lett 2018;2:1–4.10.1109/LSENS.2018.2882622Suche in Google Scholar

[39] Hassan AR, Haque MA. Epilepsy and seizure detection using statistical features in the complete ensemble empirical mode decomposition domain. In: IEEE Region 10 Annu Int Conf Proceedings/TENCON 2015:1–6.10.1109/TENCON.2015.7373154Suche in Google Scholar

[40] Hassan AR, Subasi A. Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Comput Methods Programs Biomed 2016;136:65–77.10.1016/j.cmpb.2016.08.013Suche in Google Scholar PubMed

[41] Rehman N, Mandic DP. Multivariate empirical mode decomposition. Proc R Soc A 2010;466:1291–302.10.1098/rspa.2009.0502Suche in Google Scholar

[42] Rehman NU, Xia Y, Mandic DP. Application of multivariate empirical mode decomposition for seizure detection in EEG signals. In: Annual International Conference of the IEEE Engineering in Medicine and Biology 2010:1650–3.10.1109/IEMBS.2010.5626665Suche in Google Scholar PubMed

[43] Zahra A, Kanwal N, Rehman NU, Ehsan S, McDonald-Maier KD. Seizure detection from EEG signals using multivariate empirical mode decomposition. Comput Biol Med 2017;88:132–41.10.1016/j.compbiomed.2017.07.010Suche in Google Scholar PubMed

[44] Hassan AR, Bhuiyan MIH. Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybern Biomed Eng 2016;36:248–55.10.1016/j.bbe.2015.11.001Suche in Google Scholar

[45] Hassan AR, Haque MA. Computer-aided obstructive sleep apnea identification using statistical features in the EMD domain and extreme learning machine. Biomed Phys Eng Express 2016;2:1–10.10.1088/2057-1976/2/3/035003Suche in Google Scholar

[46] Hassan AR. Automatic screening of obstructive sleep apnea from single-lead electrocardiogram. In: 2nd Int Conf on Electrical Engineering and Information & Communication Technology (lCEEICT) 2015:1–6.10.1109/ICEEICT.2015.7307522Suche in Google Scholar

[47] Hassan AR, Bhuiyan MIH. Computer methods and programs in biomedicine automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting. Comput Methods Programs Biomed 2017;140:201–10.10.1016/j.cmpb.2016.12.015Suche in Google Scholar PubMed

[48] Hassan AR, Bhuiyan MIH. Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed Signal Process Control 2016;24:1–10.10.1016/j.bspc.2015.09.002Suche in Google Scholar

[49] Hassan AR, Bhuiyan MIH. Automatic sleep stage classification. In: 2nd International Conference on Electrical Information and Communication Technologies (EICT) 2015:211–6.10.1109/EICT.2015.7391948Suche in Google Scholar

[50] Bashar SK, Hassan AR, Bhuiyan MIH. Motor imagery movements classification using multivariate EMD and short time Fourier transform. In: Annual IEEE India Conference (INDICON) 2015:1–6.10.1109/INDICON.2015.7443813Suche in Google Scholar

[51] Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E 2001;64:1–8.10.1103/PhysRevE.64.061907Suche in Google Scholar PubMed

[52] Huang NE, Wu MLC, Long SR, Shen SSP, Qu W, Gloersen P, et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc R Soc A Math Phys Eng Sci 2003;459:2317–45.10.1098/rspa.2003.1123Suche in Google Scholar

[53] Inouye T, Shinosaki K, Sakamoto H, Toi S, Ukai S, Iyama A, et al. Quantification of EEG irregularity by use of the entropy of the power spectrum. Electroencephalogr Clin Neurophysiol 1991;79:204–10.10.1016/0013-4694(91)90138-TSuche in Google Scholar PubMed

[54] Bandt C, Pompe B. Permutation entropy : a natural complexity measure for time series. Phys Rev Lett 2002;88:1–4.10.1103/PhysRevLett.88.174102Suche in Google Scholar PubMed

[55] Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273–97.10.1007/BF00994018Suche in Google Scholar

[56] Samiee K, Kovács P, Gabbouj M. Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE Trans Biomed Eng 2015;62:541–52.10.1109/TBME.2014.2360101Suche in Google Scholar PubMed

[57] Lajnef T, Chaibi S, Ruby P, Aguera PE, Eichenlaub JB, Samet M, et al. Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines. J Neurosci Methods 2015;250:94–105.10.1016/j.jneumeth.2015.01.022Suche in Google Scholar PubMed

[58] Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn 2002;46:389–422.10.1023/A:1012487302797Suche in Google Scholar

[59] Rehman NU, Park C, Huang NE, Mandic DP. EMD via MEMD: multivariate noise-aided computation of standard EMD. Adv Adapt Data Anal 2013;5:1–25.10.1142/S1793536913500076Suche in Google Scholar