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Machine learning based classification of EEG signal for detection of child epileptic seizure without snipping

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Abstract

The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. Therefore, detecting epileptic activity is a very demanding process that requires a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper evaluates machine learning classifiers' performance with their paradigms for classification of raw EEG signals into two classes, i.e., seizure and non-seizure. Here, the 13 descriptive features are taken into consideration and fed to the classifiers. Here, CHB-MIT Scalp EEG Database is used, which comprises paediatric subjects of 24 records. The performance of classifiers is evaluated categorically concerning gender and in total. The results confirmed that the fine KNN is the best classifier in males, females, and all subjects.

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References

  • Aghazadeh, R., Frounchi, J., Montagna, F., & Benatti, S. (2020). Scalable and energy-efficient seizure detection based on direct use of compressively-sensed EEG data on an ultra low power multi-core architecture. Computers in Biology and Medicine 125, 104004.

  • Amengual-Gual, M., Ulate-Campos, A., & Loddenkemper, T. (2019). Status epilepticus prevention, ambulatory monitoring, early seizure detection and prediction in at-risk patients. Seizure, 68, 31–37.

    Article  Google Scholar 

  • Anuragi, A., & Sisodia, D. S. (2019). Alcohol use disorder detection using EEG Signal features and flexible analytical wavelet transform. Biomedical Signal Processing and Control, 52, 384–393.

    Article  Google Scholar 

  • Bandarabadi, M., Teixeira, C. A., Rasekhi, J., & Dourado, A. (2015). Epileptic seizure prediction using relative spectral power features. Clinical Neurophysiology, 126(2), 237–248.

    Article  Google Scholar 

  • Crochiere, R. E., & Rabiner, L. R. (1981). Interpolation and decimation of digital signals—A tutorial review. Proceedings of the IEEE, 69(3), 300–331.

    Article  Google Scholar 

  • Engel, J. (2006). ILAE classification of epilepsy syndromes. EpilepsyResearch, 70(1), 5–10.

    Google Scholar 

  • Fisher, R. S., Scharfman, H. E., & deCurtis, M. (2014). How can we identify Ictal and interictal abnormal activity? Advances in Experimental Medicine and Biology. https://doi.org/10.1007/978-94-017-8914-1_1

    Article  Google Scholar 

  • Gadhoumi, K., Lina, J., & Gotman, J. (2012). Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG. Clinical Neurophysiology, 123(10), 1906–1916.

    Article  Google Scholar 

  • Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), e215–e220.

    Article  Google Scholar 

  • Hosseinzadeh, M., Koohpayehzadeh, J., Bali, A. O., Asghari, P., Souri, A., Mazaherinezhad, A., Bohlouli, M., & Rawassizadeh, R. (2020). A diagnostic prediction model for chronic kidney disease in the internet of things platform. Multimedia Tools and Applications 1–18.

  • Kemp, B., & Olivan, J. (2013). European data format “plus” (EDF+), an EDF alike standard format for the exchange of physiological data. Clinical Neurophysiology, 114(9), 1755–1761.

    Article  Google Scholar 

  • Klotz, K. A., Sag, Y., Schönberger, J., & Jacobs, J. (2020). Scalp ripples can predict development of epilepsy after first unprovoked seizure in childhood. Annals of Neurology.

  • Kumar, N., Kumar, R., Murmu, G., & Sethy, P. K. (2021). Extraction of melody from polyphonic music using modified morlet wavelet. Microprocessors and Microsystems 80, 103612.

  • Kumar, S., Singh, S., Agarwal, P., Acharya, U. K., Sethy, P. K., & Pandey, C. (2020). Speech quality evaluation for different pitch detection algorithms in LPC speech analysis–synthesis system. International Journal of Speech Technology 1–7.

  • Lu, D., & Triesch, J. (2019). Residual deep convolutional neural network for EEG signal classification in epilepsy. arXiv:1903.08100.

  • Parvizi, J., & Kastner, S. (2018). Promises and limitations of human intracranial electroencephalography. Nature Neuroscience, 21(4), 474–483. https://doi.org/10.1038/s41593-018-0108-2

    Article  Google Scholar 

  • Rajaguru, H., & Prabhakar, S. K. (2017, October). Time-frequency analysis (dB2 and dB4) for Epilepsy classification with LDA classifier. In 2017 2nd international conference on communication and electronics systems (ICCES) (pp. 708–711). IEEE.

  • Rasekhi, J., Mollaei, M. R. K., Bandarabadi, M., Teixeira, C. A., & Dourado, A. (2013). Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods. Journal of Neuroscience Methods, 217(1–2), 9–16.

    Article  Google Scholar 

  • Roberts, S. J., Husmeier, D., Rezek, I., & Penny, W. (1998). Bayesian approaches to gaussian mixture modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1133–1142.

    Article  Google Scholar 

  • Satapathy, S. K., Jagadev, A. K., & Dehuri, S. (2017). Weighted majority voting based ensemble of classifiers using different machine learning techniques for classification of EEG signal to detect an epileptic seizure. Informatica, 41(1), 99.

    MathSciNet  Google Scholar 

  • Sethy, P. K., Barpanda, N. K., Rath, A. K., & Behera, S. K. (2020b). Nitrogen deficiency prediction of rice crop based on convolutional neural network. Journal of Ambient Intelligence and Humanized Computing, 11(11), 5703–5711.

    Article  Google Scholar 

  • Sethy, P. K., Barpanda, N. K., Rath, A. K., & Behera, S. K. (2020). Deep feature based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture, 175, 105527.

  • Sreeja, S. R., & Samanta, D. (2020). Distance-based weighted sparse representation to classify motor imagery EEG signals for BCI applications. Multimedia Tools and Applications 1–19.

  • Subasi, A., & Gursoy, M. I. (2010). EEG signal classification using PCA, ICA, LDA, and support vector machines. Expert Systems with Applications, 37(12), 8659–8666.

    Article  Google Scholar 

  • Sujatha, K. (2020). Automatic epilepsy detection using hybrid decomposition with multiclass support vector method. Multimedia Tools Application, 79, 9871–9890. https://doi.org/10.1007/s11042-019-08359-6

    Article  Google Scholar 

  • Teixeira, C. A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valderrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., & Dourado, A. (2014a). Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine, 114(3), 324–336.

    Article  Google Scholar 

  • Türk, Ö., & Özerdem, M. S. (2019). Epilepsy detection by using scalogram based convolutional neural network from EEG signals. Brain Sciences, 9(5), 115.

    Article  Google Scholar 

  • Usman, S. M., Usman, M., & Fong, S. (2017). Epileptic seizures prediction using machine learning methods. Computational and Mathematical Methods in Medicine, 2017, 1–10.

    Article  MathSciNet  Google Scholar 

  • Vadera, S., Mullin, J., Bulacio, J., Najm, I., Bingaman, W., & Gonzalez-Martinez, J. (2013). Stereo electroencephalography following subdural grid placement for difficult to localize epilepsy. Neurosurgery, 72, 723–729.

    Article  Google Scholar 

  • Venkataraman, V., Vlachos, I., Faith, A., Krishnan, B., Tsakalis, K., Treiman, D., & Iasemidis, L. (2014). 36th annual international conference of the IEEE engineering in medicine and biology society. Brain Dynamics Based Automated Epileptic Seizure Detection (pp. 946–949)

  • Wang, H., & Zhang, Y. (2016). Detection of motor imagery EEG signals employing Naïve Bayes based learning process. Measurement, 86, 148–158.

    Article  Google Scholar 

  • Wu, J., Zhou, T., & Li, T. (2020). Detecting epileptic seizures in EEG signals with complementary ensemble empirical mode decomposition and extreme gradient boosting. Entropy, 22(2), 140.

    Article  Google Scholar 

  • Zandi, S., Tafreshi, R., Javidan, M., & Dumont, G. A. (2013). Predicting epileptic seizures in scalp EEG based on a variational bayesian gaussian mixture model of zero-crossing intervals. IEEE Transactions on Biomedical Engineering, 60(5), 1401–1413.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics, Sambalpur University, Sambalpur, Odisha, India

    Prabira Kumar Sethy

  2. Department of ETC, Trident Academy of Technology, Bhubaneswar, Odisha, India

    Millee Panigrahi

  3. Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, OMR, Chennai, India

    K. Vijayakumar

  4. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, India

    Santi Kumari Behera

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  1. Prabira Kumar Sethy
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  2. Millee Panigrahi
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  3. K. Vijayakumar
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  4. Santi Kumari Behera
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Correspondence to Prabira Kumar Sethy.

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Sethy, P.K., Panigrahi, M., Vijayakumar, K. et al. Machine learning based classification of EEG signal for detection of child epileptic seizure without snipping. Int J Speech Technol 26, 559–570 (2023). https://doi.org/10.1007/s10772-021-09855-7

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  • DOI: https://doi.org/10.1007/s10772-021-09855-7

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