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Computers and Computing

Effective Epileptic Seizure Detection Using Enhanced Salp Swarm Algorithm-based Long Short-Term Memory Network

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References

  • C. Sun, H. Cui, W. Zhou, W. Nie, X. Wang, and Q. Yuan, “Epileptic seizure detection with EEG textural features and imbalanced classification based on EasyEnsemble learning,” Int. J. Neural Syst., Vol. 29, no. 10, pp. 1950021, Sep. 2019. DOI:10.1142/S0129065719500217.
  • M. Sharma, A. A. Bhurane, and U. R. Acharya, “MMSFL-OWFB: A novel class of orthogonal wavelet filters for epileptic seizure detection,” Knowl. Based. Syst., Vol. 160, pp. 265–277, Nov. 2018. DOI:10.1016/j.knosys.2018.07.019.
  • D. Wang, D. Ren, K. Li, Y. Feng, D. Ma, X. Yan, and G. Wang, “Epileptic seizure detection in long-term EEG recordings by using wavelet-based directed transfer function,” IEEE Trans. Biomed. Eng., Vol. 65, no. 11, pp. 2591–2599, Nov. 2018. DOI:10.1109/TBME.2018.2809798.
  • X. Zhang, L. Yao, M. Dong, Z. Liu, Y. Zhang, and Y. Li, “Adversarial representation learning for robust patient-independent epileptic seizure detection,” IEEE J. Biomed. Health. Inform., Vol. 24, no. 10, pp. 2852–2859, Oct. 2020. DOI:10.1109/JBHI.2020.2971610.
  • M. S. J. Solaija, S. Saleem, K. Khurshid, S. A. Hassan, and A. M. Kamboh, “Dynamic mode decomposition based epileptic seizure detection from scalp EEG,” IEEE. Access., Vol. 6, pp. 38683–38692, Jul. 2018. DOI:10.1109/ACCESS.2018.2853125.
  • C. Park, et al. “Epileptic seizure detection for multi-channel EEG with deep convolutional neural network,” International Conference on Electronics, Information, and Communication (ICEIC), IEEE, Honolulu, HI, USA, 24–27 Jan. 2018, pp. 1–5. DOI:10.23919/ELINFOCOM.2018.8330671.
  • Y. Wang, Z. Li, L. Feng, H. Bai, and C. Wang, “Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection,” IET Circuits Devices Syst., Vol. 12, no. 1, pp. 108–115, Jan. 2018. DOI:10.1049/iet-cds.2017.0216.
  • R. Sarić, D. Jokić, N. Beganović, L. G. Pokvić, and A. Badnjević, “FPGA-based real-time epileptic seizure classification using artificial neural network,” Biomed. Signal. Process. Control., Vol. 62, pp. 102106, Sep. 2020. DOI:10.1016/j.bspc.2020.102106.
  • H. Al-Hadeethi, S. Abdulla, M. Diykh, R. C. Deo, and J. H. Green, “Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications,” Expert. Syst. Appl., Vol. 161, pp. 113676, Dec. 2020. DOI:10.1016/j.eswa.2020.113676.
  • S. K. Rout, and P. K. Biswal, “An efficient error-minimized random vector functional link network for epileptic seizure classification using VMD,” Biomed. Signal. Process. Control., Vol. 57, pp. 101787, Mar. 2020. DOI:10.1016/j.bspc.2019.101787.
  • B. Büyükçakır, F. Elmaz, and A. Y. Mutlu, “Hilbert vibration decomposition-based epileptic seizure prediction with neural network,” Comput. Biol. Med., Vol. 119, pp. 103665, Apr. 2020. DOI:10.1016/j.compbiomed.2020.103665.
  • A. Burrello, S. Benatti, K. Schindler, L. Benini, and A. Rahimi, “An ensemble of hyperdimensional classifiers: hardware-friendly short-latency seizure detection with automatic iEEG electrode selection,” IEEE. J. Biomed. Health. Inform., Vol. 25, no. 4, pp. 935–946, Apr. 2020. DOI:10.1109/JBHI.2020.3022211.
  • Aayesha, M. B. Qureshi, M. Afzaal, M. S. Qureshi, and M. Fayaz, “Machine learning-based EEG signals classification model for epileptic seizure detection,” Multimed. Tools. Appl., Vol. 80, no. 12, pp. 17849–17877, May 2021. DOI:10.1007/s11042-021-10597-6.
  • Y. Li, W. G. Cui, H. Huang, Y. Z. Guo, K. Li, and T. Tan, “Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the fisher vector approach,” Knowl. Based. Syst., Vol. 164, pp. 96–106, Jan. 2019. DOI:10.1016/j.knosys.2018.10.029.
  • H. Wang, W. Shi, and C. S. Choy, “Hardware design of real time epileptic seizure detection based on STFT and SVM,” IEEE. Access., Vol. 6, pp. 67277–67290, Sep. 2018. DOI:10.1109/ACCESS.2018.2870883.
  • W. Hussain, M. T. Sadiq, S. Siuly, and A. U. Rehman, “Epileptic seizure detection using 1 D-convolutional long short-term memory neural networks,” Appl. Acoust., Vol. 177, pp. 107941, Jun. 2021. DOI:10.1016/j.apacoust.2021.107941.
  • M. Mursalin, Y. Zhang, Y. Chen, and N. V. Chawla, “Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier,” Neurocomputing, Vol. 241, pp. 204–214, Jun. 2017. DOI:10.1016/j.neucom.2017.02.053.
  • Z. Zainuddin, K. H. Lai, and P. Ong, “An enhanced harmony search based algorithm for feature selection: applications in epileptic seizure detection and prediction,” Comput. Electr. Eng., Vol. 53, pp. 143–162, Jul. 2016. DOI:10.1016/j.compeleceng.2016.02.009.
  • M. K. I. Molla, K. M. Hassan, M. R. Islam, and T. Tanaka, “Graph eigen decomposition-based feature-selection method for epileptic seizure detection using electroencephalography,” Sensors, Vol. 20, no. 16, pp. 4639, Aug. 2020. DOI:10.3390/s20164639.
  • K. Akyol, “Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection,” Expert. Syst. Appl., Vol. 148, pp. 113239, Jun. 2020. DOI:10.1016/j.eswa.2020.113239.
  • D. K. Thara, B. G. PremaSudha, and F. Xiong, “Epileptic seizure detection and prediction using stacked bidirectional long short term memory,” Pattern Recognit. Lett., Vol. 128, pp. 529–535, Dec. 2019. DOI:10.1016/j.patrec.2019.10.034.
  • M. R. Kumar, and Y. S. Rao, “Epileptic seizures classification in EEG signal based on semantic features and variational mode decomposition,” Cluster. Comput., Vol. 22, no. 6, pp. 13521–13531, Nov. 2019. DOI:10.1007/s10586-018-1995-4.
  • Y. Jiang, W. Chen, and M. Li, “Symplectic geometry decomposition-based features for automatic epileptic seizure detection,” Comput. Biol. Med., Vol. 116, pp. 103549, Jan. 2020. DOI:10.1016/j.compbiomed.2019.103549.
  • E. Yavuz, M. C. Kasapbaşı, C. Eyüpoğlu, and R. Yazıcı, “An epileptic seizure detection system based on cepstral analysis and generalized regression neural network,” Biocybernetics Biomed. Eng, Vol. 38, no. 2, pp. 201–216, Jan. 2018. DOI:10.1016/j.bbe.2018.01.002.
  • R. San-Segundo, M. Gil-Martín, L. F. D'Haro-Enríquez, and J. M. Pardo, “Classification of epileptic EEG recordings using signal transforms and convolutional neural networks,” Comput. Biol. Med., Vol. 109, pp. 148–158, Jun. 2019. DOI:10.1016/j.compbiomed.2019.04.031.
  • J. Prasanna, M. S. P. Subathra, M. A. Mohammed, M. S. Maashi, B. Garcia-Zapirain, N. J. Sairamya, and S. T. George, “Detection of focal and non-focal electroencephalogram signals using fast walsh-hadamard transform and artificial neural network,” Sensors, Vol. 20, no. 17, pp. 4952, Sep. 2020. DOI:10.3390/s20174952.
  • W. Zhao, et al., “A novel deep neural network for robust detection of seizures using EEG signals,” Comput. Math. Methods. Med., Vol. 2020, 9689821, Apr. 2020. DOI:10.1155/2020/9689821.
  • Ö Yıldırım, U. B. Baloglu, and U. R. Acharya, “A deep convolutional neural network model for automated identification of abnormal EEG signals,” Neural Comput. Appl., Vol. 32, no. 20, pp. 15857–15868, Oct. 2020. DOI:10.1007/s00521-018-3889-z.
  • R. G. Andrzejak, K. Schindler, and C. Rummel, “Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients,” Phys. Rev. E, Vol. 86, no. 4, pp. 046206, Oct. 2012. DOI:10.1103/PhysRevE.86.046206.
  • S. Raghu, and N. Sriraam, “Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures,” Expert. Syst. Appl., Vol. 89, pp. 205–221, Dec. 2017. DOI:10.1016/j.eswa.2017.07.029.
  • I. Obeid, and J. Picone, “The temple university hospital EEG data corpus,” Front. Neurosci., Vol. 10, pp. 196, May 2016. DOI:10.3389/fnins.2016.00196.
  • R. Zhang, et al., “A new motor imagery EEG classification method FB-TRCSP+ RF based on CSP and random forest,” IEEE. Access., Vol. 6, pp. 44944–44950, Jul. 2018. DOI:10.1109/ACCESS.2018.2860633.
  • G. K. Apostolidis, and L. J. Hadjileontiadis, “Swarm decomposition: A novel signal analysis using swarm intelligence,” Signal. Process., Vol. 132, pp. 40–50, Mar. 2017. DOI:10.1016/j.sigpro.2016.09.004.
  • Y. Miao, M. Zhao, V. Makis, and J. Lin, “Optimal swarm decomposition with whale optimization algorithm for weak feature extraction from multicomponent modulation signal,” Mech. Syst. Signal. Process., Vol. 122, pp. 673–691, May 2019. DOI:10.1016/j.ymssp.2018.12.034.
  • M. K. Siddiqui, R. Morales-Menendez, X. Huang, and N. Hussain, “A review of epileptic seizure detection using machine learning classifiers,” Brain. Inform., Vol. 7, no. 1, pp. 5, Dec. 2020. DOI:10.1186/s40708-020-00105-1.
  • A. B. Das, and M. I. H. Bhuiyan, “Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain,” Biomed. Signal. Process. Control., Vol. 29, pp. 11–21, Aug. 2016. DOI:10.1016/j.bspc.2016.05.004.
  • C. Neuper, and G. Pfurtscheller, “Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates,” Int. J. Psychophysiol., Vol. 43, no. 1, pp. 41–58, Dec. 2001. DOI:10.1016/s0167-8760(01)00178-7.
  • C. Ieracitano, N. Mammone, A. Bramanti, A. Hussain, and F. C. Morabito, “A convolutional neural network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings,” Neurocomputing, Vol. 323, pp. 96–107, Jan. 2019. DOI:10.1016/j.neucom.2018.09.071.
  • V. Gupta, and M. Mittal, “R-Peak detection in ECG signal using yule–walker and principal component analysis,” IETE. J. Res., Vol. 67, no. 6, pp. 921–934, 2021. DOI:10.1080/03772063.2019.1575292.
  • C. Hinchliffe, M. Yogarajah, S. Elkommos, H. Tang, and D. Abasolo, “Entropy measures of electroencephalograms towards the diagnosis of psychogenic Non-epileptic seizures,” Entropy, Vol. 24, no. 10, pp. 1348, Oct. 2022. DOI:10.3390/e24101348.
  • I. Fernández-Varela, E. Hernández-Pereira, D. Álvarez-Estévez, and V. Moret-Bonillo, “Combining machine learning models for the automatic detection of EEG arousals,” Neurocomputing, Vol. 268, pp. 100–108, Dec. 2017. DOI:10.1016/j.neucom.2016.11.086.
  • S. Kanoga, A. Kanemura, and H. Asoh, “Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms,” Neurocomputing, Vol. 347, pp. 240–250, Jun. 2019. DOI:10.1016/j.neucom.2019.02.060.
  • Y. Yu, X. Si, C. Hu, and J. Zhang, “A review of recurrent neural networks: LSTM cells and network architectures,” Neural Comput., Vol. 31, no. 7, pp. 1235–1270, Jul. 2019. DOI:10.1162/neco_a_01199.
  • K. Smagulova, and A. P. James, “A survey on LSTM memristive neural network architectures and applications,” Eur Phys J. Spec. Top, Vol. 228, no. 10, pp. 2313–2324, Oct. 2019. DOI:10.1140/epjst/e2019-900046-x.

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