Variational mode decomposition-based seizure classification using Bayesian regularized shallow neural network

• Autorzy: Yadav Vipin Prakash , Sharma Kamlesh Kumar

Identyfikatory

DOI

10.1016/j.bbe.2021.02.003

• Języki publikacji: EN

Abstrakty

EN

This work presents a new epileptic seizures epoch classification scheme. Variational mode decomposition (VMD), has been explored for non-recursively decomposing the electroencephalogram (EEG) signals into fourteen band limited intrinsic mode functions (IMFs). Data augmentation (DA), has been used for handling unbalanced classification problem. Normalized energy, fractal dimension, number of peaks, and prominence parameters were computed from the band-limited IMFs for the discrimination of seizure and non-seizure epochs. Bayesian regularized shallow neural network (BR-SNNs) and six other well-known classifiers were tested. Sensitivity, specificity, and accuracy have been used as performance metrics. This study includes two different epoch lengths of 1-second and 2-seconds. A total of 32 test cases for both, class balanced and unbalanced classification problems have been taken for the performance evaluation. The best performance obtained is 100% for all the three metrics from the test cases of database-2 and 3. For database-1, average sensitivity, specificity, and accuracy of 99.71, 99.75, and 99.73% have been achieved, respectively for the 1-second epoch. The presented work shows better performance results compared to many previously reported works.

Słowa kluczowe

EN

electroencephalogram; epilepsy; seizure epoch classification; variational mode decomposition; data augmentation; shallow neural network

PL

elektroencefalogram; epilepsja; rozszerzanie danych; sieć neuronowa

• 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. 2
• Strony: 402--418
• Opis fizyczny: Bibliogr. 61 poz., rys., tab., wykr.

Twórcy

autor

Yadav Vipin Prakash

• Department of Electronics & Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India; Department of Electronics Engineering, Rajasthan Technical University, Kota, Rajasthan, India

autor

Sharma Kamlesh Kumar

• Department of Electronics & Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

Bibliografia

• [1] Iranmanesh S, Raikos G, Imtiaz SA, Rodriguez-Villegas E. A seizure-based power reduction SoC for wearable EEG in epilepsy. IEEE Access 2019;7:151682–91.
• [2] Blumenfeld H. Impaired consciousness in epilepsy. Lancet Neurol 2012;11:814–26.
• [3] Iasemidis LD. Epileptic seizure prediction and control. IEEE Trans Biomed Eng 2003;50:549–58.
• [4] Ocbagabir HT, Aboalayon KAI, Faezipour M. Efficient EEG analysis for seizure monitoring in epileptic patients. In: IEEE Long Island Systems, Applications and Technology Conference (LISAT). p. 1–6.
• [5] George ST, Subathra MSP, Sairamya NJ, Susmitha L, Joel PM. Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform. Biocybern Biomed Eng 2020;40:709–28.
• [6] Shahidi Zandi A, Javidan M, Dumont G, Tafrershi R. Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Trans Biomed Eng 2010;57:1639–51.
• [7] Sheela KG, Deepa SN. Review on methods to fix number of hidden neurons in neural networks. Math Probl Eng 2013:425740.
• [8] Vidyaratne LS, Iftekharuddin KM. Real-time epileptic seizure detection using EEG. IEEE T Neur Sys Reh 2017;25:2146–56.
• [9] Sirven JI. Epilepsy: A spectrum disorder. Cold Spring Harb Perspect Med. 2015;5. a022848-a.
• [10] Djemili R, Bourouba H, Amara Korba MC. Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals. Biocybern Biomed Eng 2016;36:285–91.
• [11] Zhou M, Tian C, Cao R, Wang B, Niu Y, Hu T, et al. Epileptic seizure detection based on EEG signals and CNN. Front Neuroinform 2018;12.
• [12] Geng D, Zhou W, Zhang Y, Geng S. Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG. Biocybern Biomed Eng 2016;36:375–84.
• [13] Faust O, Acharya UR, Adeli H, Adeli A. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 2015;26:56–64.
• [14] Jouny CC, Franaszczuk PJ, Bergey GK. Improving early seizure detection. Epilepsy Behav 2011;22:S44–8.
• [15] Tzallas A, Tsipouras M, Fotiadis D. Epileptic seizure detection in EEGs using time-frequency analysis. IEEE T Inf Technol B 2009;13:703–10.
• [16] 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.
• [17] Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 2009;36:2027–36.
• [18] Panda R, Khobragade PS, Jambhule PD, Jengthe SN, Pal PR, Gandhi TK. Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction. In: International Conference on Systems in Medicine and Biology. p. 405–8.
• [19] Liu Y, Zhou W, Yuan Q, Chen S. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. IEEE T Neur Sys Reh 2012;20:749–55.
• [20] Sharma M, Pachori RB, Rajendra AU. A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognit Lett 2017;94:172–9.
• [21] Bhattacharyya A, Pachori RB. A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform. IEEE Trans Biomed Eng 2017;64:2003–15.
• [22] Xia Y, Zhou W, Li C, Yuan Q, Geng S. Seizure detection approach using S-transform and singular value decomposition. Epilepsy Behav 2015;52:187–93.
• [23] Chatterjee S, Choudhury NR, Bose R. Detection of epileptic seizure and seizure-free EEG signals employing generalised S-transform. IET Sci Meas Technol: Institution of Engineering and Technology 2017:847–55.
• [24] Ambulkar NK, Sharma SN. Detection of epileptic seizure in EEG signals using window width optimized S-transform and artificial neural networks. In: IEEE Bombay Section Symposium (IBSS). p. 1–6.
• [25] 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 London, Ser. A 1998;454:903–95.
• [26] Bajaj V, Pachori RB. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 2012;16:1135–42.
• [27] Alam SMS, Bhuiyan MIH. Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE J Biomed Health 2013;17:312–8.
• [28] Riaz F, Hassan A, Rehman S, Niazi IK, Dremstrup K. EMDbased temporal and spectral features for the classification of EEG signals using supervised learning. IEEE T Neur Sys Reh 2016;24:28–35.
• [29] Smith JS. The local mean decomposition and its application to EEG perception data. J R Soc Interface 2005;2:443–54.
• [30] Zhang T, Chen W. LMD based features for the automatic seizure detection of EEG signals using SVM. IEEE T Neur Sys Reh 2017;25:1100–8.
• [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] Raghu S, Sriraam N, Kumar GP, Hegde AS. A novel approach for real-time recognition of epileptic seizures using minimum variance modified fuzzy entropy. IEEE Trans Biomed Eng 2018;65:2612–21.
• [33] Zhang T, ChenW, 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 2017;31:550–9.
• [34] Rout SK, Biswal PK. An efficient error-minimized random vector functional link network for epileptic seizure classification using VMD. Biomed Signal Process 2020;57 101787.
• [35] Wu Z, Huang N, Chen X. The multi-dimensional ensemble empirical mode decomposition method. Adv Adapt Data Anal 2009;1:339–72.
• [36] Peng ZK, Tse PW, Chu FL. A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing. Mech Syst Signal PR 2005;19:974–88.
• [37] Huang N, Shen Z, Long SR, Wu MLC, 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 London, Ser A 1998;454:903–95.
• [38] Dragomiretskiy K, Zosso D. Variational Mode Decomposition. IEEE T Signal Process 2014;62:531–44.
• [39] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101:e215–20.
• [40] 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 Stat Nonlin Soft Matter Phys 2001;64 061907.
• [41] Swami P, Panigrahi B, Nara S, Bhatia M, Gandhi T. EEG epilepsy datasets. 2016.
• [42] Li S, Zhou W, Yuan Q, Liu Y. Seizure prediction using spike rate of intracranial EEG. IEEE T Neur Sys Reh 2013;21:880–6.
• [43] Gu X, Angelov PP, Soares EA. A self-adaptive synthetic over-sampling technique for imbalanced classification. 2020;35:923-43.
• [44] Mannan MMN, Kamran MA, Kang S, Jeong MY. Effect of EOG signal filtering on the removal of ocular artifacts and EEG-based brain-computer interface: A comprehensive study. Complexity 2018;2018 4853741.
• [45] Wang S, Zhang J, Feng F, Qian X, Jiang L, Huang J, et al. Fractal analysis on artificial profiles and electroencephalography signals by roughness scaling extraction algorithm. IEEE Access 2019;7:89265–77.
• [46] Kesić S, Spasić SZ. Application of Higuchi’s fractal dimension from basic to clinical neurophysiology: A review. Comput Methods Programs Biomed 2016;133:55–70.
• [47] Katz MJ. Fractals and the analysis of waveforms. Comput Biol Med 1988;18:145–56.
• [48] Shen C-P, Liu S-T, Zhou W-Z, Lin F-S, Lam AY-Y, Sung H-Y, et al. A physiology-based seizure detection system for multichannel EEG. PLoS One. 2013;8:e65862-e.
• [49] Sheela KG, Deepa SN. Review on methods to fix number of hidden neurons in neural networks. Math Probl Eng 2013;2013 425740.
• [50] Zabihi M, Kiranyaz S, Rad AB, Katsaggelos AK, Gabbouj M, Ince T. Analysis of high-dimensional phase space via Poincaré section for patient-specific seizure detection. IEEE T Neur Sys Reh 2016;24:386–98.
• [51] Kaleem M, Guergachi A, Krishnan S. Patient-specific seizure detection in long-term EEG using wavelet decomposition. Biomed Signal Process 2018;46:157–65.
• [52] Zabihi M, Kiranyaz S, Jäntti V, Lipping T, Gabbouj M. Patient-specific seizure detection using nonlinear dynamics and nullclines. IEEE J Biomed Health 2020;24:543–55.
• [53] Kaleem M, Gurve D, Guergachi A, Krishnan S. Patient-specific seizure detection in long-term EEG using signal-derived empirical mode decomposition (EMD)-based dictionary approach. J Neural Eng 2018;15 056004.
• [54] Deng Z, Xu P, Xie L, Choi K, Wang S. Transductive joint-knowledge-transfer TSK FS for recognition of epileptic EEG signals. IEEE T Neur Sys Reh 2018;26:1481–94.
• [55] Dash DP, Kolekar MH, Jha K. Multi-channel EEG based automatic epileptic seizure detection using iterative filtering decomposition and Hidden Markov Model. Comput Biol Med 2020;116 103571.
• [56] Li Y, Liu Y, Cui W, Guo Y, Huang H, Hu Z. Epileptic seizure detection in EEG signals using a unified temporal-spectral squeeze-and-excitation network. IEEE T Neur Sys Reh 2020;28:782–94.
• [57] Gupta A, Singh P, Karlekar M. A novel signal modeling approach for classification of seizure and seizure-free EEG signals. IEEE T Neur Sys Reh 2018;26:925–35.
• [58] 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.
• [59] Siuly S, Alcin O, Bajaj V, Sengur A, Zhang Y. Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure. IET Sci Meas Technol 2018;13.
• [60] Sameer M, Gupta B. Detection of epileptical seizures based on alpha band statistical features. Wireless Pers Commun 2020.
• [61] Swami P, Gandhi TK, Panigrahi BK, Tripathi M, Anand S. A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst Appl 2016;56:116–30.

Uwagi

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