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Generalized Stockwell transform and SVD-based epileptic seizure detection in EEG using random forest

Autorzy
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: Visual inspection of electroencephalogram (EEG) records by neurologist is the main diagnostic method of epilepsy but it is particularly time-consuming and expensive. Hence, it is of great significance to develop automatic seizure detection technique. Methods: In this work, a seizure detection approach, synthesizing generalized Stockwell transform (GST), singular value decomposition (SVD) and random forest, was proposed. Utilizing GST, the raw EEG was transformed into a time–frequency matrix, then the global and local singular values were extracted by SVD from the holistic and partitioned matrices of GST, respectively. Subsequently, four local parameters were calculated from each vector of local singular values. Finally, the global singular value vectors and local parameters were respectively fed into two random forest classifiers for classification, and the final category of a testing EEG was voted based on sub-labels obtained from the trained classifiers. Results: Four most common but challenging classification tasks of Bonn EEG database were investigated. The highest accuracies of 99.12%, 99.63%, 99.03% and 98.62% were achieved using our presented technique, respectively. Conclusions: Our proposed technique is comparable or superior to other up-to-date methods. The presented method is promising and able to handle with kinds of epileptic seizure detection tasks with satisfactory accuracy.
Twórcy
autor
  • College of Communication Engineering, Jilin University, Changchun, China
autor
  • College of Communication Engineering, Jilin University, Changchun 130025, China
autor
  • College of Communication Engineering, Jilin University, Changchun, China
Bibliografia
  • [1] Ngugi AK, Kariuki SM, Bottomley C, Kleinschmidt I, Sander JW, Newton CR. Incidence of epilepsy: a systematic review and meta-analysis. Neurology 2011;77(10):1005–12.
  • [2] Zhang T, Chen W, Li M. AR based quadratic feature extraction in the VMD domain for the automated seizure detection of EEG using random forest classifier. Biomed Signal Process Control 2017;31:550–9.
  • [3] Jouny CC, Bergey GK. Characterization of early partial seizure onset: frequency, complexity and entropy. Clin Neurophysiol 2012;123(4):658–69.
  • [4] Tang Y, Durand DM. A tunable support vector machine assembly classifier for epileptic seizure detection. Expert Syst Appl 2012;39(4):3925–38.
  • [5] Gotman J. Automatic recognition of epileptic seizures in the EEG. Electroencephalogr Clin Neurophysiol 1982; 54(5):530–40.
  • [6] Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS. Automated EEG analysis of epilepsy: a review. Knowl Based Syst 2013;45(3):147–65.
  • [7] Şengür A, Guo Y, Akbulut Y. Time–frequency texture descriptors of EEG signals for efficient detection of epileptic seizure. Brain Inform 2016;3(2):101–8.
  • [8] Bajaj V, Rai K, Kumar A, Sharma D. Time–frequency image based features for classification of epileptic seizures from EEG signals. Biomed Phys Eng Express 2017;3(1):015012.
  • [9] Hyvärinen A, Ramkumar P, Parkkonen L, Hari R. Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis. Neuroimage 2010;49(1):257–71.
  • [10] 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(2):541–52.
  • [11] Bajaj V, Pachori RB. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 2012;16(6):1135–42.
  • [12] 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(2):494–502.
  • [13] 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(3):1106–17.
  • [14] Zhang T, Chen W. LMD based features for the automatic seizure detection of EEG signals using SVM. IEEE Trans Neural Syst Rehabil Eng 2017;25(8):1100–8.
  • [15] Hassan AR, Subasi A. Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Comput Methods Programs Biomed 2016; 136:65–77.
  • [16] Murugavel ASM, Ramakrishnan S. Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification. Med Biol Eng Comput 2016;54(1):149–61.
  • [17] Kumar Y, Dewal ML, Anand RS. Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 2014;133(8):271–9.
  • [18] Song JL, Hu W, Zhang R. Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine. Neurocomputing 2016;175(PA):383–91.
  • [19] Upadhyay R, Padhy PK, Kankar PK. A comparative study of feature ranking techniques for epileptic seizure detection using wavelet transform. Comput Electr Eng 2016;53:163–76.
  • [20] Zhang T, Chen W, Li M. Recognition of epilepsy electroencephalography based on AdaBoost algorithm. Acta Phys Sin 2015;64(12):128701.
  • [21] Zhang T, Chen W, Li M. Fuzzy distribution entropy and its application in automated seizure detection technique. Biomed Signal Process Control 2018;360–77.
  • [22] 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.
  • [23] Chen G. Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features. Expert Syst Appl 2014; 41(5):2391–4.
  • [24] Das AB, Bhuiyan MIH, Alam SMS. Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. Signal Image Video Process 2016;10(2):259–66.
  • [25] Peker M, Sen B, Delen D. A novel method for automated diagnosis of epilepsy using complex-valued classifiers. IEEE J Biomed Health Inform 2015;20(1):108–18.
  • [26] Vidyaratne LS, Iftekharuddin KM. Real-time epileptic seizure detection using EEG. IEEE Trans Neural Syst Rehabil Eng 2017;25(11):2146–56.
  • [27] 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.
  • [28] 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(4):385.
  • [29] Li M, Chen W, Zhang T. Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble. Biomed Signal Process Control 2017;31:357–65.
  • [30] Li M, Chen W, Zhang T. Application of MODWT and lognormal distribution model for automatic epilepsy identification. Biocybern Biomed Eng 2017;37(4):679–89.
  • [31] Bhattacharyya A, Pachori RB. A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform. IEEE Trans Biomed Eng 2017; 64(9):2003–15.
  • [32] Sharma RR, Pachori RB. Time–frequency representation using IEVDHM-HT with application to classification of epileptic EEG signals. IET Sci Measure Technol 2018; 12(1):72–82.
  • [33] Stockwell RG, Mansinha L, Lowe RP. Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 1996;44(4):998–1001.
  • [34] Yan A, Zhou W, Yuan Q, Yuan S, Wu Q, Zhao X, et al. Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG. Epilepsy Behav 2015;45:8–14.
  • [35] Hamidia M, Amrouche A. A new robust double-talk detector based on the Stockwell transform for acoustic echo cancellation. Digit Signal Process 2016;60:99–112.
  • [36] Dash KS, Puhan NB, Panda G. Handwritten numeral recognition using non-redundant Stockwell transform and bio-inspired optimal zoning. IET Image Process 2015; 9(10):874–82.
  • [37] Burnos S, Hilfiker P, Sürücü O, Scholkmann F, Krayenbuhl N, Grunwald T, et al. Human intracranial high frequency oscillations (HFOs) detected by automatic time–frequency analysis. PLOS ONE 2014;9(4):e94381.
  • [38] Kalbkhani H, Shayesteh MG. Stockwell transform for epileptic seizure detection from EEG signals. Biomed Signal Process Control 2017;38:108–18.
  • [39] 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;64:061907.
  • [40] Wang H, Wang P, Liu T. Power quality disturbance classification using the S-transform and probabilistic neural network. Energies 2017;10(1):107.
  • [41] Dan K. A singularly valuable decomposition: the SVD of a matrix. Coll Math J 1996;27(1):2–23.
  • [42] Breiman L. Random forests. Mach Learn 2001;45(1):5–32.
  • [43] Prinzie A, Poel DVD. Random forests for multiclass classification: random multinomial logit. Expert Syst Appl 2008;34(3):1721–32.
  • [44] Archer KJ, Kimes RV. Empirical characterization of random forest variable importance measures. Comput Stat Data Anal 2008;52(4):2249–60.
  • [45] Caruana R, Niculescu-Mizil A. An empirical comparison of supervised learning algorithms. International Conference on Machine Learning; 2006.
  • [46] Zhang Y, Zhou W, Yuan S. Multifractal analysis and relevance vector machine-based automatic seizure detection in intracranial EEG. Int J Neural Syst 2015; 25(6):1550020.
  • [47] Mursalin M, Zhang Y, Chen Y, Chawla NV. Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier. Neurocomputing 2017;241:204–14.
  • [48] Jaiswal AK, Banka H. Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomed Signal Process Control 2017;34:81–92.
  • [49] Tiwari A, Pachori RB, Kanhangad V, Panigrahi BK. Automated diagnosis of epilepsy using key-point based local binary pattern of EEG signals. IEEE J Biomed Health Inform 2017;21(4):888–96.
  • [50] Jaiswal AK, Banka H. Local transformed features for epileptic seizure detection in EEG signal. J Med Biol Eng 2017;9:1–14.
  • [51] Guo L, Rivero D, Dorado J, Rabuñal RJ, Pazos A. Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J Neurosci Methods 2010;191(1):101–9.
  • [52] Liang SF, Wang HC, Chang WL. Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP J Adv Signal Process 2010; 2010(1):1–15.
  • [53] Martis RJ, Acharya UR, Tan JH, Petznick A, Yanti R, Chua CK, et al. Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals. Int J Neural Syst 2012;22(6):1250027.
  • [54] Acharya UR, Yanti R, Zheng JW, Krishnan MMR, Tan JH, Martis RJ, et al. Automated diagnosis of epilepsy using CWT, HOS and texture parameters. Int J Neural Syst 2013; 23(3):1350009.
  • [55] Li M, Chen W, Zhang T. Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM. Biocybern Biomed Eng 2016;36(4):708–18.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d8373fe6-a2f7-4e57-ac7a-88799c53490f
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