Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum

• Autorzy: Karabiber Cura Ozlem , Akan Aydin

Identyfikatory

DOI

10.1016/j.bbe.2020.11.002

• Języki publikacji: EN

Abstrakty

EN

Dynamic mode decomposition (DMD) is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. Recently, DMD algorithm has successfully been applied to the analysis of non-stationary signals such as neural recordings. In this study, we propose single-channel, and multi-channel EEG based DMD approaches for the analysis of epileptic EEG signals. We investigate the possibility of utilizing the ‘‘DMD Spectrum’’ for the classification of pre-seizure and seizure EEG segments. We introduce higher-order DMD spectral moments and DMD sub-band powers, and extract them as features for the classification of epileptic EEG signals. Experiments are conducted on multi-channel EEG signals collected from 16 epilepsy patients. Single-channel, and multi-channel EEG based DMD approaches have been tested on epileptic EEG data recorded from only right, only left, and both brain hemisphere channels. Performance of various classifiers using the proposed DMD-Spectral based features are compared with that of traditional spectral features. Experimental results reveal that the higher order DMD spectral moments and DMD sub-band power features introduced in this study, outperform the analogous spectral features calculated from traditional power spectrum.

Słowa kluczowe

EN

dynamic mode decomposition; DMD; electroencephalogram; EEG; epilepsy; epileptic seizure classification; machine learning

PL

elektroencefalogram; EEG; epilepsja; uczenie maszynowe

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 1
• Strony: 28--44
• Opis fizyczny: Bibliogr. 53 poz., rys., tab., wykr.

Twórcy

autor

Karabiber Cura Ozlem

• Department of Biomedical Engineering, Faculty of Eng. and Architecture, Izmir Katip Celebi University, Cigli, Izmir, Turkey

autor

Akan Aydin

• Department of Electrical and Electronics Engineering, Faculty of Engineering, Izmir University of Economics, Balcova, Izmir, Turkey

Bibliografia

• [1] Schmid PJ. Dynamic mode decomposition of numerical and experimental data. J Fluid Mech 2010;656:5–28.
• [2] Tirunagari S. Dynamic mode decomposition for computer vision and signal processing [Ph.D. dissertation]. United Kingdom: University of Surrey; 2016.
• [3] Kutz JN, Brunton SL, Brunton BW, Proctor JL. Dynamic mode decomposition: data-driven modeling of complex systems. SIAM; 2016.
• [4] World Health Organization, Global Campaign against Epilepsy, Programme for Neurological Diseases, Neuroscience (World Health Organization), International Bureau for Epilepsy, World Health Organization. Department of Mental Health, Substance Abuse, International Bureau of Epilepsy, International League against Epilepsy. Atlas: epilepsy care in the world. World Health Organization; 2005.
• [5] Teplan M. Fundamentals of EEG measurement. Meas Sci Rev 2002;2(2):1–11.
• [6] Mahmoodian N, Boese A, Friebe M, Haddadnia J. Epileptic seizure detection using cross-bispectrum of electroencephalogram signal. Seizure 2019;66:4–11.
• [7] Raghu S, Sriraam N, Hegde AS, Kubben PL. A novel approach for classification of epileptic seizures using matrix determinant. Expert Syst Appl 2019;127:323–41.
• [8] Yuan Q, Zhou W, Zhang L, Zhang F, Xu F, Leng Y, et al. Epileptic seizure detection based on imbalanced classification and wavelet packet transform. Seizure 2017;50:99–108.
• [9] Bandil MK, Wadhwani A. Multi-resolution EEG and EEG sub-band features optimization for epileptic classification using hybrid evolutionary computing technique. Procedia Comput Sci 2019;152:243–51.
• [10] Riaz F, Hassan A, Rehman S, Niazi IK, Dremstrup K. EMD-based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Trans Neural Syst Rehabil Eng 2015;24(1):28–35.
• [11] Oppenheim AV. Discrete-time signal processing. Pearson Education India; 1999.
• [12] Kiymik MK, Güler I, Dizibüyük A, Akin M. Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput Biol Med 2005;35(7):603–16.
• [13] Adeli H, Zhou Z, Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 2003;123(1):69–87.
• [14] Bajaj V, Pachori RB. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 2011;16(6):1135–42.
• [15] Alickovic E, Kevric J, Subasi A. Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed Signal Process Control 2018;39:94–102.
• [16] Moctezuma LA, Molinas M. Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD. J Biomed Res 2020;34(3):178–88.
• [17] Wu J, Zhou T, Li T. Detecting epileptic seizures in EEG signals with complementary ensemble empirical mode decomposition and extreme gradient boosting. Entropy 2020;22(2):140.
• [18] Cao H, Wang X, He D, Chen X. An improvement of time-reassigned synchrosqueezing transform algorithm and its application in mechanical fault diagnosis. Measurement 2020;155:107538.
• [19] Li X, Ma Z, Kang D, Li X. Fault diagnosis for rolling bearing based on VMD-FRFT. Measurement 2020;155:107554.
• [20] Li T, Qian Z, He T. Short-term load forecasting with improved CEEMDAN and GWO-based multiple kernel ELM. Complexity 2020;2020.
• [21] Kumar MR, Rao YS. Epileptic seizures classification in EEG signal based on semantic features and variational mode decomposition. Cluster Comput 2019;22(6):13521–3.
• [22] Pachori RB, Sharma R, Patidar S. Classification of normal and epileptic seizure EEG signals based on empirical mode decomposition. Complex system modelling and control through intelligent soft computations. Springer; 2015. p. 367–88.
• [23] Hassan AR, Subasi A, Zhang Y. Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise. Knowl Based Syst 2020;191:105333.
• [24] Correa AG, Orosco L, Diez P, Laciar E. Automatic detection of epileptic seizures in long-term EEG records. Comput Biol Med 2015;57:66–73.
• [25] Zhang T, Chen W, Li M. Generalized stockwell transform and SVD-based epileptic seizure detection in EEG using random forest. Biocybern Biomed Eng 2018;38(3):519–34.
• [26] Quintero-Rinc ón A, Pereyra M, d'Giano C, Risk M, Batatia H. Fast statistical model-based classification of epileptic EEG signals. Biocybern Biomed Eng 2018;38(4):877–89.
• [27] Ibrahim S, Djemal R, Alsuwailem A. Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis. Biocybern Biomed Eng 2018;38(1):16–26.
• [28] Yamaguchi C. Fourier and wavelet analyses of normal and epileptic electroencephalogram (EEG). First International IEEE EMBS Conference on Neural Engineering 2003 Conference Proceedings 2003;406–9.
• [29] 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.
• [30] Moctezuma LA, Molinas M. EEG channel-selection method for epileptic-seizure classification based on multi-objective optimization. Front Neurosci 2020;14:593.
• [31] Solaija MSJ, Saleem S, Khurshid K, Hassan SA, Kamboh AM. Dynamic mode decomposition based epileptic seizure detection from scalp EEG. IEEE Access 2018;6:38683–92.
• [32] Bilal M, Rizwan M, Saleem S, Khan MM, Alkatheir MS, Alqarni M. Automatic seizure detection using multi- resolution dynamic mode decomposition. IEEE Access 2019;7:61180–94.
• [33] Brunton BW, Johnson LA, Ojemann JG, Kutz JN. Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition. J Neurosci Methods 2016;258:1–15.
• [34] Seo J-H, Tsuda I, Lee YJ, Ikeda A, Matsuhashi M, Matsumoto R, et al. Pattern recognition in epileptic EEG signals via dynamic mode decomposition. Mathematics 2020;8(4):481.
• [35] Rout SK, Biswal PK. An efficient error-minimized random vector functional link network for epileptic seizure classification using VMD. Biomed Signal Process Control 2020;57:101787.
• [36] Yuan Q, Zhou W, Zhang L, Zhang F, Xu F, Leng Y, et al. Epileptic seizure detection based on imbalanced classification and wavelet packet transform. Seizure 2017;50:99–108.
• [37] Colak OH. Preprocessing effects in time-frequency distributions and spectral analysis of heart rate variability. Digit Signal Process 2009;19(4):731–9.
• [38] Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS. Automated EEG analysis of epilepsy: a review. Knowl Based Syst 2013;45:147–65.
• [39] Tawfik NS, Youssef SM, Kholief M. A hybrid automated detection of epileptic seizures in EEG records. Comput Electr Eng 2016;53:177–90.
• [40] Xiang J, Li C, Li H, Cao R, Wang B, Han X, et al. The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods 2015;243:18–25.
• [41] Acharya UR, Molinari F, Sree SV, Chattopadhyay S, Ng K-H, Suri JS. Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 2012;7(4):401–8.
• [42] Tzallas AT, Tsipouras MG, Fotiadis DI. Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans Inf Technol Biomed 2009;13(5):703–10.
• [43] Alkan A, Koklukaya E, Subasi A. Automatic seizure detection in EEG using logistic regression and artificial neural network. J Neurosci Methods 2005;148(2):167–76.
• [44] Gajic D, Djurovic Z, Gligorijevic J, Di Gennaro S, Savic-Gajic I. Detection of epileptiform activity in EEG signals based on time- frequency and non-linear analysis. Front Comput Neurosci 2015;9:38.
• [45] Gajic D, Djurovic Z, Di Gennaro S, Gustafsson F. Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomed Eng: Appl Basis Commun 2014;26(2):1450021.
• [46] Public Data set: CHB-MIT EEG dataset. Available from: https://physionet.org/content/chbmit/1.0.0/. [Accessed 16 May 2020].
• [47] Alotaiby TN, Alshebeili SA, Alotaibi FM, Alrshoud SR. Epileptic seizure prediction using CSP and LDA for scalp EEG signals. Comput Intell Neurosci 2017;2017.
• [48] Cui S, Duan L, Qiao Y, Xiao Y. Learning EEG synchronization patterns for epileptic seizure prediction using bag-of-wave features. J Ambient Intell Humaniz Comput 2018;1–16.
• [49] Ayodele K, Ikezogwo W, Komolafe M, Ogunbona P. Supervised domain generalization for integration of disparate scalp EEG datasets for automatic epileptic seizure detection. Comput Biol Med 2020;103757.
• [50] Fu R, Tian Y, Shi P, Bao T. Automatic detection of epileptic seizures in EEG using sparse CSP and fisher linear discrimination analysis algorithm. J Med Syst 2020;44(2):1–13.
• [51] Cura OK, Atli SK, Türe HS, Akan A. Epileptic seizure classifications using empirical mode decomposition and its derivative. BioMed Eng OnLine 2020;19(1):1–22.
• [52] Wang Y-H, Yeh C-H, Young H-WV, Hu K, Lo M-T. On the computational complexity of the empirical mode decomposition algorithm. Physica A: Stat Mech Appl 2014;400:159–67.
• [53] Erichson NB, Brunton SL, Kutz JN. Compressed dynamic mode decomposition for background modeling. J Real-Time Image Process 2019;16(5):1479–92.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).