Skip to main content
SpringerLink
Log in

Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is tedious, laborious and subjective. To overcome such disadvantages, numerous seizure detection techniques involving signal processing and machine learning tools have been developed. However, there still remain the problems of automatic detection with high efficiency and accuracy in distinguishing normal, interictal and ictal electroencephalogram (EEG) signals. In this study we propose a novel method for automatic identification of epileptic seizures in singe-channel EEG signals based upon time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into a series of proper rotation components (PRCs) and a baseline signal by using the ITD method. The first two PRCs of the EEG signals are extracted, which contain most of the EEG signals’ energy and are considered to be the predominant PRCs. Second, four levels DWT is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies (db3) wavelet function is selected for analysis. Third, phase space of the PRCs is reconstructed based on db3, in which the properties associated with the nonlinear EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between normal, interictal and ictal EEG signals. Fourth, neural networks are then used to model, identify and classify EEG system dynamics between normal (healthy), interictal and ictal EEG signals. Finally, experiments are carried out on the University of Bonn’s widely used and publicly available epilepsy dataset to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved average classification accuracy for eleven cases is reported to be 98.15%. Compared with many state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of seizure EEG signals in the clinical application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Aarabi A, He B (2017) Seizure prediction in patients with focal hippocampal epilepsy. Clin Neurophysiol 128(7):1299–1307

    Google Scholar 

  • Acharya UR, Sree SV, Alvin APC, Suri JS (2012) Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework. Expert Syst Appl 39(10):9072–9078

    Google Scholar 

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

    Google Scholar 

  • Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh JE (2015) Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl Based Syst 88:85–96

    Google Scholar 

  • Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278

    Google Scholar 

  • Ahmedt-Aristizabal D, Fookes C, Nguyen K, Sridharan S (2018) Deep classification of epileptic signals. arXiv preprint arXiv:1801.03610

  • An X, Jiang D, Chen J, Liu C (2012) Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing. J Vib Control 18(2):240–245

    Google Scholar 

  • Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) 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 64(6):061907

    Google Scholar 

  • Azar AT, El-Said SA (2014) Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 24:1163–1177

    Google Scholar 

  • Bajaj V, Pachori RB (2012) Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 16(6):1135–1142

    Google Scholar 

  • Bajaj V, Pachori RB (2013) Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals. Biomed Eng Lett 3(1):17–21

    Google Scholar 

  • Bhati D, Pachori RB, Gadre VM (2017a) A novel approach for time-frequency localization of scaling functions and design of three-band biorthogonal linear phase wavelet filter banks. Digit Signal Process 69:309–322

    Google Scholar 

  • Bhati D, Sharma M, Pachori RB, Gadre VM (2017b) 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 62:259–273

    Google Scholar 

  • Bhattacharyya A, Pachori RB (2017) A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform. IEEE Trans Biomed Eng 64(9):2003–2015

    Google Scholar 

  • Bhattacharyya A, Pachori R, Upadhyay A, Acharya U (2017) Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl Sci 7(4):385

    Google Scholar 

  • Chen B, He Z, Chen X, Cao H, Cai G, Zi Y (2011) A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis. Meas Sci Technol 22(5):055704

    Google Scholar 

  • Chen M, Fang Y, Zheng X (2014) Phase space reconstruction for improving the classification of single trial EEG. Biomed Signal Process Control 11:10–16

    Google Scholar 

  • Chu K (1999) An introduction to sensitivity, specificity, predictive values and likelihood ratios. Emerg Med Australas 11(3):175–181

    Google Scholar 

  • Diykh M, Li Y, Wen P (2017) Classify epileptic EEG signals using weighted complex networks based community structure detection. Expert Syst Appl 90:87–100

    Google Scholar 

  • Djemili R, Bourouba H, Korba MA (2016) Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals. Biocybern Biomed Eng 36(1):285–291

    Google Scholar 

  • Du X, Dua S, Acharya RU, Chua CK (2012) Classification of epilepsy using high-order spectra features and principle component analysis. J Med Syst 36(3):1731–1743

    Google Scholar 

  • Feng Z, Lin X, Zuo MJ (2016) Joint amplitude and frequency demodulation analysis based on intrinsic time-scale decomposition for planetary gearbox fault diagnosis. Mech Syst Signal Process 72:223–240

    Google Scholar 

  • Frei MG, Osorio I (2007) Intrinsic time-scale decomposition: time-frequency-energy analysis and real-time filtering of non-stationary signals. In: Proceedings of the royal society of London A: mathematical, physical and engineering sciences, vol 463, no 2078. The Royal Society, pp 321–342

  • Goceri E (2018) Formulas behind deep learning success. In: International conference on applied analysis and mathematical modeling, p 156

  • Goceri E, Gooya A (2018) On the importance of batch size for deep learning. Paper presented at the international conference on mathematics. Istanbul, Turkey

  • Goceri E, Songul C (2018) Mobile health technologies for patients with mental illness. In: International conference on advanced technologies, Antalya, Turkey, p 146

  • Hassan AR, Siuly S, Zhang Y (2016) Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput Methods Programs Biomed 137:247–259

    Google Scholar 

  • Huang B, Kunoth A (2013) An optimization based empirical mode decomposition scheme. J Comput Appl Math 240:174–183

    MathSciNet  MATH  Google Scholar 

  • Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Liu HH (1998) The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A Math Phys Eng Sci R Soc 454(1971):903–995

    MathSciNet  MATH  Google Scholar 

  • Isik H, Sezer E (2012) Diagnosis of epilepsy from electroencephalography signals using multilayer perceptron and Elman artificial neural networks and wavelet transform. J Med Syst 36(1):1–13

    Google Scholar 

  • Jaiswal AK, Banka H (2017) Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomed Signal Process Control 34:81–92

    Google Scholar 

  • Jia J, Goparaju B, Song J, Zhang R, Westover MB (2017) Automated identification of epileptic seizures in EEG signals based on phase space representation and statistical features in the CEEMD domain. Biomed Signal Process Control 38:148–157

    Google Scholar 

  • Joshi V, Pachori RB, Vijesh A (2014) Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed Signal Process Control 9:1–5

    Google Scholar 

  • Kafashan M, Ryu S, Hargis MJ, Laurido-Soto O, Roberts DE, Thontakudi A, Ching S (2017) EEG dynamical correlates of focal and diffuse causes of coma. BMC Neurol 17(1):197

    Google Scholar 

  • Kaya Y, Uyar M, Tekin R, Yildirim S (2014) 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Appl Math Comput 243:209–219

    MathSciNet  MATH  Google Scholar 

  • Kumar Y, Dewal ML, Anand RS (2014) Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 133:271–279

    Google Scholar 

  • Kumar TS, Kanhangad V, Pachori RB (2015) Classification of seizure and seizure-free EEG signals using local binary patterns. Biomed Signal Process Control 15:33–40

    Google Scholar 

  • Lahmiri S (2018) Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients. Physica A Stat Mech Appl 490:378–385

    Google Scholar 

  • Lee SH, Lim JS, Kim JK, Yang J, Lee Y (2014) Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance. Comput Methods Programs Biomed 116(1):10–25

    Google Scholar 

  • Li S, Zhou W, Yuan Q, Geng S, Cai D (2013) Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 43(7):807–816

    Google Scholar 

  • Li Y, Xu M, Wei Y, Huang W (2015) Rotating machine fault diagnosis based on intrinsic characteristic-scale decomposition. Mech Mach Theory 94:9–27

    Google Scholar 

  • Li M, Chen W, Zhang T (2017a) Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble. Biomed Signal Process Control 31:357–365

    Google Scholar 

  • Li M, Chen W, Zhang T (2017b) Automatic epileptic EEG detection using DT-CWT-based non-linear features. Biomed Signal Process Control 34:114–125

    Google Scholar 

  • Li Y, Cui WG, Huang H, Guo YZ, Li K, Tan T (2019) Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach. Knowl Based Syst 164:96–106

    Google Scholar 

  • Martis RJ, Tan JH, Chua CK, Loon TC, Yeo SWJ, Tong L (2015) Epileptic EEG classification using nonlinear parameters on different frequency bands. J Mech Med Biol 15(03):1550040

    Google Scholar 

  • Merigó JM, Casanovas M (2011) Induced aggregation operators in the Euclidean distance and its application in financial decision making. Expert Syst Appl 38:7603–7608

    Google Scholar 

  • Michael S (2005) Applied nonlinear time series analysis: applications in physics, physiology and finance, vol 52. World Scientific, Singapore

    MATH  Google Scholar 

  • Mursalin M, Zhang Y, Chen Y, Chawla NV (2017) Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier. Neurocomputing 241:204–214

    Google Scholar 

  • Pachori RB (2008) Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res Lett Signal Process 2008:14

    Google Scholar 

  • Pachori RB, Bajaj V (2011) Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput Methods Programs Biomed 104(3):373–381

    Google Scholar 

  • Pachori RB, Patidar S (2014) Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Comput Methods Programs Biomed 113(2):494–502

    Google Scholar 

  • 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. Springer, Switzerland, pp 367–388

    Google Scholar 

  • Park C, Looney D, Van Hulle MM, Mandic DP (2011) The complex local mean decomposition. Neurocomputing 74(6):867–875

    Google Scholar 

  • Samiee K, Kovacs P, Gabbouj M (2015) Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE Trans Biomed Eng 62(2):541–552

    Google Scholar 

  • Sato Y, Doesburg SM, Wong SM, Ochi A, Otsubo H (2015) Dynamic preictal relations in FCD type II: potential for early seizure detection in focal epilepsy. Epilepsy Res 110:26–31

    Google Scholar 

  • Scheuer ML, Wilson SB (2004) Data analysis for continuous EEG monitoring in the ICU: seeing the forest and the trees. J Clin Neurophysiol 21(5):353–378

    Google Scholar 

  • 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

    Google Scholar 

  • Sharma RR, Pachori RB (2017a) Time-frequency representation using IEVDHM-HT with application to classification of epileptic EEG signals. IET Sci Meas Technol 12(1):72–82

    Google Scholar 

  • Sharma M, Pachori RB (2017b) A novel approach to detect epileptic seizures using a combination of tunable-Q wavelet transform and fractal dimension. J Mech Med Biol 17(07):1740003

    Google Scholar 

  • 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 Recognit Lett 94:172–179

    Google Scholar 

  • Sharma RR, Varshney P, Pachori RB, Vishvakarma SK (2018) Automated system for epileptic EEG detection using iterative filtering. IEEE Sens Lett 2(4):1–4

    Google Scholar 

  • Shayegh F, Sadri S, Amirfattahi R, Ansari-Asl K (2014) A model-based method for computation of correlation dimension, Lyapunov exponents and synchronization from depth-EEG signals. Comput Methods Programs Biomed 113(1):323–337

    MATH  Google Scholar 

  • Sheintuch L, Friedman A, Efrat N, Tifeeret C, Shorer Z, Neuman I, Shallom I (2014) O16: detection of epileptiform activity using multi-channel linear prediction coefficients and localization of epileptic foci based on EEG-fMRI data. Clin Neurophysiol 125:S33

    Google Scholar 

  • Sivakumar B (2002) A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. J Hydrol 258(1–4):149–162

    Google Scholar 

  • Som A, Krishnamurthi N, Venkataraman V, Turaga P (2016) Attractor-shape descriptors for balance impairment assessment in Parkinson’s disease. In: IEEE conference on engineering in medicine and biology society, pp 3096–3100

  • Song JL, Hu W, Zhang R (2016) Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine. Neurocomputing 175:383–391

    Google Scholar 

  • Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37(12):8659–8666

    Google Scholar 

  • Subasi A, Kevric J, Canbaz MA (2019) Epileptic seizure detection using hybrid machine learning methods. Neural Comput Appl 31(1):317–325

    Google Scholar 

  • Sun Y, Li J, Liu J, Chow C, Sun B, Wang R (2015) Using causal discovery for feature selection in multivariate numerical time series. Mach Learn 101(1–3):377–395

    MathSciNet  MATH  Google Scholar 

  • Takens F (1980) Detecting strange attractors in turbulence. In: Dynamical systems and turbulence, Warwick 1980, Springer, Berlin, 1981, pp 366–381

  • Tawfik NS, Youssef SM, Kholief M (2016) A hybrid automated detection of epileptic seizures in EEG records. Comput Electr Eng 53:177–190

    Google Scholar 

  • Timothy LT, Krishna BM, Nair U (2017) Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis. Int J Psychophysiol 120:86–95

    Google Scholar 

  • Tiwari AK, Pachori RB, Kanhangad V, Panigrahi BK (2016) Automated diagnosis of epilepsy using key-point-based local binary pattern of EEG signals. IEEE J Biomed Health Inform 21(4):888–896

    Google Scholar 

  • Ullah I, Hussain M, Aboalsamh H (2018) An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst Appl 107:61–71

    Google Scholar 

  • Vavadi H, Ayatollahi A, Mirzaei A (2010) A wavelet-approximate entropy method for epileptic activity detection from EEG and its sub-bands. J Biomed Sci Eng 3(12):1182

    Google Scholar 

  • Venkataraman V, Turaga P (2016) Shape distributions of nonlinear dynamical systems for video-based inference. IEEE Trans Pattern Anal Mach Intell 38(12):2531–2543

    Google Scholar 

  • Wang C, Hill DJ (2006) Learning from neural control. IEEE Trans Neural Netw 17(1):130–146

    Google Scholar 

  • Wang C, Hill DJ (2007) Deterministic learning and rapid dynamical pattern recognition. IEEE Trans Neural Netw 18(3):617–630

    Google Scholar 

  • Wang C, Hill DJ (2009) Deterministic learning theory for identification, recognition and control. CRC Press, Boca Raton

    Google Scholar 

  • Wang D, Miao D, Xie C (2011) Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst Appl 38(11):14314–14320

    Google Scholar 

  • Wang L, Xue W, Li Y, Luo M, Huang J, Cui W, Huang C (2017) Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis. Entropy 19(6):222

    Google Scholar 

  • Xing Z, Qu J, Chai Y, Tang Q, Zhou Y (2017) Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine. J Mech Sci Technol 31(2):545–553

    Google Scholar 

  • Xu B, Jacquir S, Laurent G, Bilbault JM, Binczak S (2013) Phase space reconstruction of an experimental model of cardiac field potential in normal and arrhythmic conditions. In: 35th annual international conference of the IEEE engineering in medicine and biology society, pp 3274–3277

  • Yuan Q, Cai C, Xiao H, Liu X, Wen Y (2007) Diagnosis of breast tumours and evaluation of prognostic risk by using machine learning approaches. In: Huang DS, Heutte L, Loog M (eds) Advanced intelligent computing theories and applications. With aspects of contemporary intelligent computing techniques. Springer, Berlin, pp 1250–1260

    Google Scholar 

  • Zahra A, Kanwal N, ur Rehman N, Ehsan S, McDonald-Maier KD (2017) Seizure detection from EEG signals using multivariate empirical mode decomposition. Comput Biol Med 88:132–141

    Google Scholar 

  • Zhang T, Chen W (2016) LMD based features for the automatic seizure detection of EEG signals using SVM. IEEE Trans Neural Syst Rehabilit Eng 25(8):1100–1108

    Google Scholar 

  • Zhang Y, Zhou W, Yuan S, Yuan Q (2015) Seizure detection method based on fractal dimension and gradient boosting. Epilepsy Behav 43:30–38

    Google Scholar 

  • Zhang T, Chen W, Li M (2017) AR based quadratic feature extraction in the VMD domain for the automated seizure detection of EEG using random forest classifier. Biomed Signal Process Control 31:550–559

    Google Scholar 

  • Zhang T, Chen W, Li M (2018) Fuzzy distribution entropy and its application in automated seizure detection technique. Biomed Signal Process Control 39:360–377

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61773194, 61304084), by the Natural Science Foundation of Fujian Province (Grant No. 2018J01542), by Fujian Provincial Training Foundation For “Bai-Qian-Wan Talents Engineering”, by the Program for New Century Excellent Talents in Fujian Province University and by the Training Program of Innovation and Entrepreneurship for Undergraduates (Grant No. 201811312002).

Author information

Authors and Affiliations

  1. School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012, People’s Republic of China

    Wei Zeng, Mengqing Li, Qinghui Wang, Fenglin Liu & Ying Wang

  2. Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, 02881, USA

    Chengzhi Yuan

Authors
  1. Wei Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mengqing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chengzhi Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qinghui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fenglin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Zeng.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zeng, W., Li, M., Yuan, C. et al. Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. Artif Intell Rev 53, 3059–3088 (2020). https://doi.org/10.1007/s10462-019-09755-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-019-09755-y

Keywords

Search

Navigation