Home Variational mode decomposition and binary grey wolf optimization-based automated epilepsy seizure classification framework
Article
Licensed
Unlicensed Requires Authentication

Variational mode decomposition and binary grey wolf optimization-based automated epilepsy seizure classification framework

  • Vipin Prakash Yadav EMAIL logo and Kamlesh Kumar Sharma
Published/Copyright: December 30, 2022
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Biomedical Engineering / Biomedizinische Technik
From the journal Biomedical Engineering / Biomedizinische Technik Volume 68 Issue 2

Abstract

This work proposes a variational mode decomposition (VMD) and binary grey wolf optimization (BGWO) based seizure classification framework. VMD decomposes the EEG signal into band-limited intrinsic mode function (BL-IMFs) non-recursively. The frequency domain, time domain, and information theory-based features are extracted from the BL-IMFs. Further, an optimal feature subset is selected using BGWO. Finally, the selected features were utilized for classification using six different supervised machine learning algorithms. The proposed framework has been validated experimentally by 58 test cases from the CHB-MIT scalp EEG and the Bonn University database. The proposed framework performance is quantified by average sensitivity, specificity, and accuracy. The selected features, along with Bayesian regularized shallow neural networks (BR-SNNs), resulted in maximum accuracy of 99.53 and 99.64 for 1 and 2 s epochs, respectively, for database 1. The proposed framework has achieved 99.79 and 99.84 accuracy for 1 and 2 s epochs, respectively, for database 2.

Keywords: binary grey wolf optimization; data augmentation; electroencephalogram; feature selection; seizure classification; variational mode decomposition
Corresponding author: Vipin Prakash Yadav, Department of Electronics & Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India; and Department of Electronics and Telecommunication, School of Electrical Engineering, MIT Academy of Engineering, Pune, Maharashtra, 412105, India, E-mail:

Acknowledgment

This research was supported in part by TEQIP-III project PhD scheme under National Project Implementation Unit (NPIU), MHRD Government of India and World Bank.

  1. Research funding: None declared.

  2. Author contributions: Vipin Prakash Yadav: Conceptualization, Methodology, Investigation, Software, Writing – review & editing. Kamlesh Kumar Sharma: Supervision.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Informed consent: Not applicable.

  5. Ethical approval: Not applicable.

References

1. 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. https://doi.org/10.1109/tbme.2010.2046417.Search in Google Scholar PubMed

2. Sheela, KG, Deepa, SN. Review on methods to fix number of hidden neurons in neural networks. Math Probl Eng 2013;2013:425740.10.1155/2013/425740Search in Google Scholar

3. Vidyaratne, LS, Iftekharuddin, KM. Real-time epileptic seizure detection using EEG. IEEE Trans Neural Syst Rehabil Eng 2017;25:2146–56. https://doi.org/10.1109/tnsre.2017.2697920.Search in Google Scholar

4. Patel, DC, Tewari, BP, Chaunsali, L, Sontheimer, HJNRN. Neuron–glia interactions in the pathophysiology of epilepsy. Nat Rev Neurosci 2019;20:282–97. https://doi.org/10.1038/s41583-019-0126-4.Search in Google Scholar PubMed PubMed Central

5. Sadanandan, N, Saft, M, Gonzales-Portillo, B, Borlongan, CVJFP. Multipronged attack of stem cell therapy in treating the neurological and neuropsychiatric symptoms of epilepsy. Front Pharmacol 2021;12:124. https://doi.org/10.3389/fphar.2021.596287.Search in Google Scholar PubMed PubMed Central

6. Berg, AT, Kaiser, K, Dixon-Salazar, T, Elliot, A, McNamara, N, Meskis, MA, et al.. Seizure burden in severe early-life epilepsy: perspectives from parents. Epilepsia Open 2019;4:293–301. https://doi.org/10.1002/epi4.12319.Search in Google Scholar PubMed PubMed Central

7. Serdyuk, S, Davtyan, K, Burd, S, Drapkina, O, Boytsov, S, Gusev, E, et al.. Cardiac arrhythmias and sudden unexpected death in epilepsy: results of long-term monitoring. Heart Rhythm 2021;18:221–8. https://doi.org/10.1016/j.hrthm.2020.09.002.Search in Google Scholar PubMed

8. Tufail, AB, Ma, Y-K, Kaabar, MKA, Rehman, AU, Khan, R, Cheikhrouhou, O. Classification of initial stages of Alzheimer’s disease through pet neuroimaging modality and deep learning: quantifying the impact of image filtering approaches. Math 2021;9:3101. https://doi.org/10.3390/math9233101.Search in Google Scholar

9. Jose, JP, Sundaram, M, Jaffino, G. Adaptive rag-bull rider: a modified self-adaptive optimization algorithm for epileptic seizure detection with deep stacked autoencoder using electroencephalogram. Biomed Signal Process Control 2021;64:102322. https://doi.org/10.1016/j.bspc.2020.102322.Search in Google Scholar

10. Shellhaas, RA. Continuous long-term electroencephalography: the gold standard for neonatal seizure diagnosis. Sem Fetal Neonatal Med 2015;20:149–53. https://doi.org/10.1016/j.siny.2015.01.005.Search in Google Scholar PubMed

11. Hu, X, Yuan, S, Xu, F, Leng, Y, Yuan, K, Yuan, Q. Scalp EEG classification using deep Bi-LSTM network for seizure detection. Comput Biol Med 2020;124:103919. https://doi.org/10.1016/j.compbiomed.2020.103919.Search in Google Scholar PubMed

12. Duun-Henriksen, J, Baud, M, Richardson, MP, Cook, M, Kouvas, G, Heasman, JM, et al.. A new era in electroencephalographic monitoring? Subscalp devices for ultra-long-term recordings. Epilepsia 2020;61:1805–17. https://doi.org/10.1111/epi.16630.Search in Google Scholar PubMed

13. Anuragi, A, Sisodia, DS, Pachori, RB. Automated FBSE-EWT based learning framework for detection of epileptic seizures using time-segmented EEG signals. Comput Biol Med 2021;136:104708. https://doi.org/10.1016/j.compbiomed.2021.104708.Search in Google Scholar PubMed

14. Tufail, AB, Ma, YK, Kaabar, MKA, Martínez, F, Junejo, AR, Ullah, I, et al.. Deep learning in cancer diagnosis and prognosis prediction: a minireview on challenges, recent trends, and future directions. Comput Math Methods Med 2021;2021:9025470. https://doi.org/10.1155/2021/9025470.Search in Google Scholar PubMed PubMed Central

15. 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. https://doi.org/10.1016/j.seizure.2015.01.012.Search in Google Scholar PubMed

16. 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. https://doi.org/10.1016/j.bbe.2016.03.001.Search in Google Scholar

17. Jouny, CC, Franaszczuk, PJ, Bergey, GK. Improving early seizure detection. Epilepsy Behav 2011;22:S44–8. https://doi.org/10.1016/j.yebeh.2011.08.029.Search in Google Scholar PubMed PubMed Central

18. Tzallas, A, Tsipouras, M, Fotiadis, D. Epileptic seizure detection in EEGs using time–frequency analysis. IEEE Trans Inform Technol Biomed 2009;13:703–10. https://doi.org/10.1109/titb.2009.2017939.Search in Google Scholar PubMed

19. 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. https://doi.org/10.1109/tbme.2014.2360101.Search in Google Scholar

20. Ocak, H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 2009;36:2027–36. https://doi.org/10.1016/j.eswa.2007.12.065.Search in Google Scholar

21. 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 2010 International conference on systems in medicine and biology; 2010:405–8 pp.10.1109/ICSMB.2010.5735413Search in Google Scholar

22. Liu, Y, Zhou, W, Yuan, Q, Chen, S. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. IEEE Trans Neural Syst Rehabil 2012;20:749–55. https://doi.org/10.1109/tnsre.2012.2206054.Search in Google Scholar PubMed

23. Zarei, A, Asl, BM. Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals. Comput Biol Med 2021;131:104250. https://doi.org/10.1016/j.compbiomed.2021.104250.Search in Google Scholar PubMed

24. 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. https://doi.org/10.1109/tbme.2017.2650259.Search in Google Scholar

25. 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. https://doi.org/10.1016/j.yebeh.2015.07.043.Search in Google Scholar PubMed

26. Chatterjee, S, Choudhury, NR, Bose, R. Detection of epileptic seizure and seizure-free EEG signals employing generalised S-transform. IET Sci Meas Technol 2017;11:847–55.10.1049/iet-smt.2016.0443Search in Google Scholar

27. Ambulkar, NK, Sharma, SN. Detection of epileptic seizure in EEG signals using window width optimized S-transform and artificial neural networks. In 2015 IEEE Bombay Section Symposium (IBSS); 2015:1–6 pp.10.1109/IBSS.2015.7456660Search in Google Scholar

28. Bajaj, V, Pachori, RB. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 2012;16:1135–42. https://doi.org/10.1109/titb.2011.2181403.Search in Google Scholar

29. 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. https://doi.org/10.1109/jbhi.2012.2237409.Search in Google Scholar PubMed

30. 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 2016;24:28–35. https://doi.org/10.1109/tnsre.2015.2441835.Search in Google Scholar

31. Zhang, T, Chen, W. LMD based features for the automatic seizure detection of EEG signals using SVM. IEEE Trans Neural Syst Rehabil 2017;25:1100–8. https://doi.org/10.1109/tnsre.2016.2611601.Search in Google Scholar

32. Smith, JS. The local mean decomposition and its application to EEG perception data. J R Soc Interface 2005;2:443–54. https://doi.org/10.1098/rsif.2005.0058.Search in Google Scholar PubMed PubMed Central

33. 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. https://doi.org/10.1109/access.2018.2853125.Search in Google Scholar

34. 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 Sig Process 2017;31:550–9. https://doi.org/10.1016/j.bspc.2016.10.001.Search in Google Scholar

35. Rout, SK, Biswal, PK. An efficient error-minimized random vector functional link network for epileptic seizure classification using VMD. Biomed Sig Process 2020;57:101787. https://doi.org/10.1016/j.bspc.2019.101787.Search in Google Scholar

36. Li, M, Sun, X, Chen, W, Jiang, Y, Zhang, T. Classification epileptic seizures in EEG using time-frequency image and block texture features. IEEE Access 2020;8:9770–81. https://doi.org/10.1109/access.2019.2960848.Search in Google Scholar

37. Mamli, S, Kalbkhani, H. Gray-level co-occurrence matrix of Fourier synchro-squeezed transform for epileptic seizure detection. Biocybern Biomed Eng 2019;39:87–99. https://doi.org/10.1016/j.bbe.2018.10.006.Search in Google Scholar

38. 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. https://doi.org/10.1016/j.neucom.2017.02.053.Search in Google Scholar

39. Harpale, V, Bairagi, V. An adaptive method for feature selection and extraction for classification of epileptic EEG signal in significant states. J King Saud Univ Comput Inform Sci 2021;33:668–76.10.1016/j.jksuci.2018.04.014Search in Google Scholar

40. Pippa, E, Zacharaki, EI, Mporas, I, Tsirka, V, Richardson, MP, Koutroumanidis, M, et al.. Improving classification of epileptic and non-epileptic EEG events by feature selection. Neurocomputing 2016;171:576–85. https://doi.org/10.1016/j.neucom.2015.06.071.Search in Google Scholar

41. Zainuddin, Z, Lai, KH, Ong, P. An enhanced harmony search based algorithm for feature selection: applications in epileptic seizure detection and prediction. Comput Electr Eng 2016;53:143–62. https://doi.org/10.1016/j.compeleceng.2016.02.009.Search in Google Scholar

42. Omidvar, M, Zahedi, A, Bakhshi, H. EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers. J Ambient Intell Human Comput 2021;12:10395–403. https://doi.org/10.1007/s12652-020-02837-8.Search in Google Scholar

43. Singh, G, Singh, B, Kaur, M. Grasshopper optimization algorithm–based approach for the optimization of ensemble classifier and feature selection to classify epileptic EEG signals. Med Biol Eng Comput 2019;57:1323–39. https://doi.org/10.1007/s11517-019-01951-w.Search in Google Scholar PubMed

44. Mirjalili, S, Mirjalili, SM, Lewis, A. Grey wolf optimizer. Adv Eng Softw 2014;69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007.Search in Google Scholar

45. Niu, P, Niu, S, liu, N, Chang, L. The defect of the Grey Wolf optimization algorithm and its verification method. Knowl Based Syst 2019;171:37–43. https://doi.org/10.1016/j.knosys.2019.01.018.Search in Google Scholar

46. Gao, Z-M, Zhao, J. An improved grey wolf optimization algorithm with variable weights. Comput Intell Neurosci 2019;2019:2981282. https://doi.org/10.1155/2019/2981282.Search in Google Scholar PubMed PubMed Central

47. Segera, D, Mbuthia, M, Nyete, A. An innovative excited-ACS-IDGWO algorithm for optimal biomedical data feature selection. BioMed Res Int 2020;2020:8506365. https://doi.org/10.1155/2020/8506365.Search in Google Scholar PubMed PubMed Central

48. Shen, C, Zhang, KJC, Systems, I. Two-stage improved Grey Wolf optimization algorithm for feature selection on high-dimensional classification. 2022;8:2769–89. https://doi.org/10.1007/s40747-021-00452-4.Search in Google Scholar

49. Singh, N, Singh, SJEB. A modified mean Gray Wolf optimization approach for benchmark and biomedical problems. Evol Bioinform 2017;13. https://doi.org/10.1177/1176934317729413.Search in Google Scholar PubMed PubMed Central

50. Momanyi, E, Segera, DJBRI. A master-slave binary grey wolf optimizer for optimal feature selection in biomedical data classification. BioMed Res Int 2021;2021.10.1155/2021/5556941Search in Google Scholar PubMed PubMed Central

51. Al-Tashi, Q, Rais, H, Jadid, S. Feature selection method based on grey wolf optimization for coronary artery disease classification. In: International conference of reliable information and communication technology. Springer; 2018:257–66 pp.10.1007/978-3-319-99007-1_25Search in Google Scholar

52. Chakraborty, C, Kishor, A, Rodrigues, JJPC. Novel enhanced-Grey Wolf Optimization hybrid machine learning technique for biomedical data computation. Comput Electr Eng 2022;99:107778. https://doi.org/10.1016/j.compeleceng.2022.107778.Search in Google Scholar

53. Khaire, UM, Dhanalakshmi, R. Stability of feature selection algorithm: a review. J King Saud Univ Comput Inform Sci 2019;34:1060–73. https://doi.org/10.1016/j.jksuci.2019.06.012.Search in Google Scholar

54. 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. 2000;101:e215–20. https://doi.org/10.1161/01.cir.101.23.e215.Search in Google Scholar PubMed

55. Andrzejak, RG, Lehnertz, K, Mormann, F, Rieke, C, David, P, Elger, CEJPRE. 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. https://doi.org/10.1103/physreve.64.061907.Search in Google Scholar

56. Gu, X, Angelov, PP, Soares, EAJIJIS. A self-adaptive synthetic over-sampling technique for imbalanced classification. Int J Intell Syst 2020;35:923–43. https://doi.org/10.1002/int.22230.Search in Google Scholar

57. Dragomiretskiy, K, Zosso, D. Variational mode decomposition. IEEE Trans Signal Process 2014;62:531–44. https://doi.org/10.1109/tsp.2013.2288675.Search in Google Scholar

58. Yadav, VP, Sharma, KK. Variational mode decomposition-based seizure classification using Bayesian regularized shallow neural network. Biocybern Biomed Eng 2021;41:402–18. https://doi.org/10.1016/j.bbe.2021.02.003.Search in Google Scholar

59. Katz, MJ. Fractals and the analysis of waveforms. Comput Biol Med 1988;18:145–56. https://doi.org/10.1016/0010-4825(88)90041-8.Search in Google Scholar PubMed

60. 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. https://doi.org/10.1109/access.2019.2926515.Search in Google Scholar

61. 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. https://doi.org/10.1371/journal.pone.0065862.Search in Google Scholar PubMed PubMed Central

62. Li, S, Zhou, W, Yuan, Q, Liu, Y. Seizure prediction using spike rate of intracranial EEG. IEEE Trans Neural Syst Rehabil 2013;21:880–6. https://doi.org/10.1109/tnsre.2013.2282153.Search in Google Scholar

63. Yang, S, Li, B, Zhang, Y, Duan, M, Liu, S, Zhang, Y, et al.. Selection of features for patient-independent detection of seizure events using scalp EEG signals. Comput Biol Med 2020;119:103671. https://doi.org/10.1016/j.compbiomed.2020.103671.Search in Google Scholar PubMed

64. Hjorth, B. EEG analysis based on time domain properties. Electroencephalogr Clin Neurophysiol 1970;29:306–10. https://doi.org/10.1016/0013-4694(70)90143-4.Search in Google Scholar PubMed

65. Wu, J, Zhou, T, Li, T. Detecting epileptic seizures in EEG signals with complementary ensemble empirical mode decomposition and extreme gradient. Boosting 2020;22:140. https://doi.org/10.3390/e22020140.Search in Google Scholar PubMed PubMed Central

66. Gilmore, M, Yu, CX, Rhodes, TL, Peebles, WA. Investigation of rescaled range analysis, the Hurst exponent, and long-time correlations in plasma turbulence. Phys Plasmas 2002;9:1312–7. https://doi.org/10.1063/1.1459707.Search in Google Scholar

67. Panwar, LK, Reddy, KS, Verma, A, Panigrahi, BK, Kumar, R. Binary Grey Wolf Optimizer for large scale unit commitment problem. Swarm Evolution Comput 2018;38:251–66. https://doi.org/10.1016/j.swevo.2017.08.002.Search in Google Scholar

68. Hassan, S, Tariq, N, Naqvi, RA, Rehman, AU, Kaabar, MKA. Performance evaluation of machine learning-based channel equalization techniques: new trends and challenges. J Sens 2022;2022:2053086. https://doi.org/10.1155/2022/2053086.Search in Google Scholar

69. Al-Tashi, Q, Kadir, SJA, Rais, HM, Mirjalili, S, Alhussian, HJIA. Binary optimization using hybrid grey wolf optimization for feature selection. 2019;7:39496–508.10.1109/ACCESS.2019.2906757Search in Google Scholar

70. Kaleem, M, Guergachi, A, Krishnan, S. Patient-specific seizure detection in long-term EEG using wavelet decomposition. Biomed Sig Process 2018;46:157–65. https://doi.org/10.1016/j.bspc.2018.07.006.Search in Google Scholar

71. 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. https://doi.org/10.1088/1741-2552/aaceb1.Search in Google Scholar PubMed

72. Deng, Z, Xu, P, Xie, L, Choi, K, Wang, S. Transductive joint-knowledge-transfer TSK FS for recognition of epileptic EEG signals. IEEE Trans Neural Syst Rehabil 2018;26:1481–94. https://doi.org/10.1109/tnsre.2018.2850308.Search in Google Scholar PubMed

73. Chandel, G, Upadhyaya, P, Farooq, O, Khan, YU. Detection of seizure event and its onset/offset using orthonormal triadic wavelet based features. IRBM 2019;40:103–12. https://doi.org/10.1016/j.irbm.2018.12.002.Search in Google Scholar

74. 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. https://doi.org/10.1109/jbhi.2019.2906400.Search in Google Scholar

75. 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. https://doi.org/10.1016/j.compbiomed.2019.103571.Search in Google Scholar PubMed

76. 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 Trans Neural Syst Rehabil 2020;28:782–94. https://doi.org/10.1109/tnsre.2020.2973434.Search in Google Scholar PubMed

77. Tang, F-G, Liu, Y, Li, Y, Peng, Z-W. A unified multi-level spectral–temporal feature learning framework for patient-specific seizure onset detection in EEG signals. Knowl Based Syst 2020;205:106152. https://doi.org/10.1016/j.knosys.2020.106152.Search in Google Scholar

78. Jiang, Y, Chen, W, Li, M. Symplectic geometry decomposition-based features for automatic epileptic seizure detection. Comput Biol Med 2020;116:103549. https://doi.org/10.1016/j.compbiomed.2019.103549.Search in Google Scholar PubMed

79. Peng, H, Lei, C, Zheng, S, Zhao, C, Wu, C, Sun, J, et al.. Automatic epileptic seizure detection via Stein kernel-based sparse representation. Comput Biol Med 2021;132:104338. https://doi.org/10.1016/j.compbiomed.2021.104338.Search in Google Scholar PubMed

80. Gupta, A, Singh, P, Karlekar, M. A novel signal modeling approach for classification of seizure and seizure-free EEG signals. IEEE Trans Neural Syst Rehabil 2018;26:925–35. https://doi.org/10.1109/tnsre.2018.2818123.Search in Google Scholar PubMed

81. 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. https://doi.org/10.1049/iet-smt.2018.5358.Search in Google Scholar

82. Raghu, S, Sriraam, N, Hegde, AS, Kubben, PL. A novel approach for classification of epileptic seizures using matrix determinant. Expert Syst Appl 2019;127:323–41.10.1016/j.eswa.2019.03.021Search in Google Scholar

83. 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. https://doi.org/10.1016/j.eswa.2016.02.040.Search in Google Scholar

84. Sameer, M, Gupta, B. Detection of epileptical seizures based on alpha band statistical features. Wireless Pers Commun 2020;115:909–25. https://doi.org/10.1007/s11277-020-07542-5.Search in Google Scholar

85. Sharma, M, Pachori, RB, Rajendra Acharya, U. A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognit Lett 2017;94:172–9. https://doi.org/10.1016/j.patrec.2017.03.023.Search in Google Scholar
Received: 2022-03-07
Accepted: 2022-12-12
Published Online: 2022-12-30
Published in Print: 2023-04-25

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Safe design of surgical robots – a systematic approach to comprehensive hazard identification
  4. Concurrent validity and reliability of a smartphone-based application for the head repositioning and cervical range of motion
  5. Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer
  6. Variational mode decomposition and binary grey wolf optimization-based automated epilepsy seizure classification framework
  7. An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for leukocytes image classification
  8. A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images
  9. A deep learning network for Gleason grading of prostate biopsies using EfficientNet
  10. Open structure magnetic particle imaging by nonlinear back projection tomography reconstruction
  11. Development and verification of a novel blood viscoelastic monitoring method based on reciprocating motion of magnetic bead
https://doi.org/10.1515/bmt-2022-0098
Keywords for this article
binary grey wolf optimization; data augmentation; electroencephalogram; feature selection; seizure classification; variational mode decomposition

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Safe design of surgical robots – a systematic approach to comprehensive hazard identification
  4. Concurrent validity and reliability of a smartphone-based application for the head repositioning and cervical range of motion
  5. Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer
  6. Variational mode decomposition and binary grey wolf optimization-based automated epilepsy seizure classification framework
  7. An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for leukocytes image classification
  8. A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images
  9. A deep learning network for Gleason grading of prostate biopsies using EfficientNet
  10. Open structure magnetic particle imaging by nonlinear back projection tomography reconstruction
  11. Development and verification of a novel blood viscoelastic monitoring method based on reciprocating motion of magnetic bead
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 27.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/bmt-2022-0098/html
Scroll to top button