Skip to main content
SpringerLink
Log in

A new design of epileptic seizure detection using hybrid heuristic-based weighted feature selection and ensemble learning

  • Regular Paper
  • Published:
International Journal of Intelligent Robotics and Applications Aims and scope Submit manuscript

Abstract

The neurological disorder named Epilepsy is diagnosed through visual inspection and manual understanding based on the Electroencephalogram (EEG) signals. Several improvements in the deep learning methods result in solving the complex problem by end to end learning. However, the current deep learning models have not utilized the EEG data for seizure detection. The main intention of this paper is to develop novel seizure detection in EEG signal using a new feature extraction and classification approaches. The first phase of the proposed model is pre-processing of signal. Further, the combination of Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) is performed for the signal decomposition. Then, the feature selection is done by the concatenation of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). With this selected features, an enhancement is introduced with the weighted feature selection approach, in which the weight is optimized by the hybrid meta-heuristic algorithm named Jaya-Cat Swarm Optimization (J-CSO).This hybrid meta-heuristic algorithm is easy to implement with no algorithm-specific parameters dependence. It is it easily applicable to real-world optimization problems. Due to these advantages, the performance of the proposed model is superior to the conventional models. Then, the J-CSO-based heuristically Improved Ensemble Learning Model (I-ELM) is introduced in the detection phase, which is proposed by three different classifiers like Fuzzy classifier, Deep Neural Network (DNN), and Long Short Term Memory (LSTM). This is comparable to the accuracy achieved by other contemporary seizure classification approaches, and this approach could serve as an effective tool in the hands of medical practitioners for analyzing bulk data and for speeding up seizure diagnosis.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Abdelhameed, A.M., Bayoumi, M.: Semi-supervised EEG signals classification system for epileptic seizure detection. IEEE Signal Process. Lett. 26(12), 1922–1926 (2019)

    Article  Google Scholar 

  • Abdelhameed, A.M., Daoud, H.G., Bayoumi, M.: Deep convolutional bidirectional LSTM recurrent neural network for epileptic seizure detection. In: IEEE international new circuits and systems conference (NEWCAS), pp. 139–143, 2018

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

    Article  Google Scholar 

  • Akyol, K.: Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection. Expert Syst. Appl. 148, 113239 (2020)

    Article  Google Scholar 

  • Atal, D.K., Mukhtiar, S.: A hybrid feature extraction and machine learning approaches for epileptic seizure detection. Multidimens. Syst. Signal Process. 31, 503–525 (2020)

    Article  MATH  Google Scholar 

  • Bouzidi, A., Riffi, M.E., Barkatou, M.: Cat swarm optimization for solving the open shop scheduling problem. J. Ind. Eng. Int. 15, 367–378 (2019)

    Article  Google Scholar 

  • Burrello, A., Benatti, S., Schindler, K., Benini, L., Rahimi, A.: An ensemble of hyperdimensional classifiers: hardware-friendly short-latency seizure detection with automatic iEEG electrode selection. IEEE J. Biomed. Health Inform. 25(4), 935–946 (2021)

    Article  Google Scholar 

  • Chen, D., Wan, S., Bao, F.S.: Epileptic focus localization using discrete wavelet transform based on interictal intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 25(5), 413–425 (2017)

    Article  Google Scholar 

  • Chen, Z., Lu, G., Xie, Z., Shang, W.: A unified framework and method for EEG-based early epileptic seizure detection and epilepsy diagnosis. IEEE Access 8, 20080–20092 (2020)

    Article  Google Scholar 

  • Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals. IEEE Sens. J. 21(7), 9377–9388 (2021)

    Article  Google Scholar 

  • Duanpo, Wu., Wang, Z., Jiang, L., Dong, F., Xunyi, Wu., Wang, S., Ding, Y.: Automatic epileptic seizures joint detection algorithm based on improved multi-domain feature of cEEG and spike feature of aEEG. IEEE Access 7, 41551–41564 (2019)

    Article  Google Scholar 

  • Fraiwan, L., Alkhodari, M.: Classification of focal and non-focal epileptic patients using single channel EEG and long short-term memory learning system. IEEE Access 8, 77255–77262 (2020)

    Article  Google Scholar 

  • Geng, M., Zhou, W., Liu, G., Li, C., Zhang, Y.: Epileptic seizure detection based on stockwell transform and bidirectional long short-term memory. IEEE Trans. Neural Syst. Rehabil. Eng. 28(3), 573–580 (2020)

    Article  Google Scholar 

  • Gupta, A., Rai, A.A.B.: Feature extraction of intra-pulse modulated LPI waveforms using STFT. In: 2019 4th International conference on recent trends on electronics, information, communication and technology (RTEICT), pp. 742–746, 2019

  • Hanosh, O., Ansari, R., Younis, K., Cetin, A.E.: Real-time epileptic seizure detection during sleep using passive infrared sensors. IEEE Sens. J. 19(15), 6467–6476 (2019)

    Article  Google Scholar 

  • Houssein, E.H., Saber, E., Ali, A.A., Wazery, Y. M.: Opposition-based learning tunicate swarm algorithm for biomedical classification. In: 2021 17th International Computer Engineering Conference (ICENCO), pp. 1–6 (2021)

  • Hu, D., Cao, J., Lai, X., Wang, Y., Wang, S., Ding, Y.: epileptic state classification by fusing hand-crafted and deep learning EEG features. IEEE Trans. Circuits Syst. II Express Briefs 68(4), 1542–1546 (2021)

    Google Scholar 

  • Hussein, R., Palangi, H., Ward, R.K., Wang, Z.J.: Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals. Clin. Neurophysiol. 130(1), 25–37 (2019)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Jha, C.K., Kolekar, M.H.: Empirical mode decomposition and wavelet transform based ECG data compression scheme. IRBM 42, 65–72 (2020)

    Article  Google Scholar 

  • Kadrolkar, A., Frank, C.: Intent recognition of torso motion using wavelet transform feature extraction and linear discriminant analysis ensemble classification. Biomed. Signal Process. Control 38, 250–264 (2017)

    Article  Google Scholar 

  • Li, Y., Wang, X.-D., Luo, M.-L., Li, K., Yang, X.-F., Guo, Q.: Epileptic seizure classification of EEGs using time-frequency analysis based multiscale radial basis functions. IEEE J. Biomed. Health Inform. 22(2), 386–397 (2018)

    Article  Google Scholar 

  • Li, Y., Liu, Y., Cui, W.-G., Guo, Y.-Z., Huang, H., Hu, Z.-Y.: Epileptic seizure detection in eeg signals using a unified temporal-spectral squeeze-and-excitation network. IEEE Trans. Neural Syst. Rehabil. Eng. 28(4), 782–794 (2020)

    Article  Google Scholar 

  • Liu, Y., Huang, Y.-X., Zhang, X., Qi, W., Guo, J., Yingbai, Hu., Zhang, L., Hang, Su.: Deep C-LSTM neural network for epileptic seizure and tumor detection using high-dimension EEG signals. IEEE Access 8, 37495–37504 (2020)

    Article  Google Scholar 

  • Nakisa, B., Rastgoo, M.N., Rakotonirainy, A., Maire, F., Chandran, V.: Long short term memory hyperparameter optimization for a neural network based emotion recognition framework. IEEE Access 6, 49325–49338 (2018)

    Article  Google Scholar 

  • Niknazar, M., Mousavi, S.R., Vahdat, B.V., Sayyah, M.: A new framework based on recurrence quantification analysis for epileptic seizure detection. IEEE J. Biomed. Health Inform. 17(3), 572–578 (2013)

    Article  Google Scholar 

  • Orhan, U., Hekim, M., Ozer, M.: EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst. Appl. 38(10), 13475–13481 (2011)

    Article  Google Scholar 

  • Rabcan, J., Levashenko, V., Zaitseva, E., Kvassay, M.: Review of methods for EEG signal classification and development of new fuzzy classification-based approach. IEEE Access 8, 189720–189734 (2020)

    Article  Google Scholar 

  • Rao, R.V.: Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7, 19–34 (2016)

    Google Scholar 

  • Rashed-Al-Mahfuz, M., Moni, M.A., Uddin, S., Alyami, S.A., Summers, M.A., Eapen, V.: A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data. IEEE J. Transl. Eng. Health Med. 9, 1–12 (2021)

    Google Scholar 

  • Salem, O., Alsubhi, K., Mehaoua, A., Boutaba, R.: Nocturnal epileptic seizures detection using inertial and muscular sensors. IEEE Trans. Mobile Comput. 18(12), 2913–2925 (2019)

    Article  Google Scholar 

  • Sam, L.Z., Maarof, M.A., Selamat, A.: Automated web pages classification with integration of principal component analysis (PCA) and independent component analysis (ICA) as feature reduction. In: Proceedings of international conference on man-machine systems, 2006

  • Tian, X., et al.: Deep multi-view feature learning for EEG-based epileptic seizure detection. IEEE Trans. Neural Syst. Rehabil. Eng. 27(10), 1962–1972 (2019)

    Article  Google Scholar 

  • Verma, S., Kaur, S., Rawat, D.B., Xi, C., Alex, L.T., Jhanjhi, N.Z.: Intelligent framework using IoT-based WSNs for wildfire detection. IEEE Access 9, 48185–48196 (2021)

    Article  Google Scholar 

  • Xu, S.S., Mak, M.-W., Cheung, C.-C.: I-vector-based patient adaptation of deep neural networks for automatic heartbeat classification. IEEE J. Biomed. Health Inform. 24(3), 717–727 (2020)

    Article  Google Scholar 

  • Yavuz, E., Kasapbaşı, M.C., Eyüpoğlu, C., Yazıcı, R.: An epileptic seizure detection system based on cepstral analysis and generalized regression neural network. Biocybern. Biomed. Eng. 38(2), 201–216 (2018)

    Article  Google Scholar 

  • Yuan, Y., Xun, G., Jia, K., Zhang, A.: A multi-context learning approach for EEG epileptic seizure detection. BMC Syst. Biol. 12, 107 (2018)

    Article  Google Scholar 

  • Yuan, Y., Xun, G., Jia, K., Zhang, A.: A multi-view deep learning framework for eeg seizure detection. IEEE J. Biomed. Health Inform. 23(1), 83–94 (2019)

    Article  Google Scholar 

  • Zhang, X., Yao, L., Dong, M., Liu, Z., Zhang, Y., Li, Y.: Adversarial representation learning for robust patient-independent epileptic seizure detection. IEEE J. Biomed. Health Inform. 24(10), 2852–2859 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Mangalore University, Mangalore, Karnataka, India

    Vedavati Bhandari & Manjaiah Doddaghatta Huchaiah

Authors
  1. Vedavati Bhandari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manjaiah Doddaghatta Huchaiah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vedavati Bhandari.

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

Bhandari, V., Huchaiah, M.D. A new design of epileptic seizure detection using hybrid heuristic-based weighted feature selection and ensemble learning. Int J Intell Robot Appl 6, 668–693 (2022). https://doi.org/10.1007/s41315-022-00233-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41315-022-00233-3

Keywords

Search

Navigation