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Automated epilepsy seizure detection from EEG signal based on hybrid CNN and LSTM model

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Abstract

Epilepsy is a neurological disorder that affects the normal functioning of the brain. More than 10% of the population across the globe is affected by this disorder. Electroencephalogram (EEG) is prominently employed to accumulate information about the brain’s electrical activity. This study proposes an end-to-end system using a combination of two deep learning models Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTM) for the classification of EEG signals of epilepsy disordered subjects into three classes, namely preictal, normal, and seizure. The experimental results are obtained using the publicly available and popular Bonn University dataset. In this CNN–LSTM classification model the feature extraction, selection, and classification tasks are performed automatically without using handcrafted feature extraction methods. The performance of the CNN–LSTM model is examined and evaluated in terms of specificity, sensitivity, and accuracy using the tenfold cross-validation approach. The experiments performed and the obtained results show the accuracy of 99.33%, sensitivity of 99.33%, and specificity of 99.66%, respectively. Our results highlight that deep learning methods are best suited for classification in comparison to other existing state-of-the-art methods.

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References

  1. American Epilepsy Society, Facts and figures. https://www.aesnet.org/for_patients/facts_figures. Accessed 15 Jan 2021

  2. Blümcke, I., Aronica, E., Becker, A., Capper, D., Coras, R., Honavar, M., Jacques, T.S., Kobow, K., Miyata, H., Mühlebner, A., Pimentel, J., Söylemezoğlu, F., Thom, M.: Low-grade epilepsy-associated neuroepithelial tumours—the 2016 WHO classification. Nat. Rev. Neurol. 12(12), 732–740 (2016)

    Article  Google Scholar 

  3. Tatum, W.O., Rubboli, G., Kaplan, P.W., Mirsatari, S.M., Radhakrishnan, K., Gloss, D., Caboclo, L.O., Drislane, F.W., Koutroumanidis, M., Schomer, D.L., Trenite, D.K.N.: Clinical utility of EEG in diagnosing and monitoring epilepsy in adults. Clin. Neurophysiol. 129(5), 1056–1082 (2018)

    Article  Google Scholar 

  4. Mirowski, P.W., LeCun, Y., Madhavan, D., Kuzniecky, R.: Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG. In: 2008 IEEE Workshop on Machine Learning for Signal Processing, pp. 244–249. IEEE (2008)

  5. Ahirwal, M.K., Kose, M.R.: Audio-visual stimulation-based emotion classification by correlated EEG channels. Health Technol. 10, 7–23 (2020)

    Article  Google Scholar 

  6. Rai, A.A., Ahirwal, M.K.: EEG based cognitive load classification during mental arithmetic task. In: Proceedings of 26th (Virtual) Annual International Conference on Advanced Computing and Communications (ADCOM 2020)

  7. Ahirwal, M.K., Kumar, A., Singh, G.K.: A new approach for utilisation of single ERP to control multiple commands in BCI. Int. J. Electron. Lett. 2(3), 166–171 (2016)

    Article  Google Scholar 

  8. Ahirwal, M.K., Londhe. N.D.: Offline study of brain computer interfacing for hand movement using OpenVIBE. In: 2011 International Conference on Process Automation, Control and Computing. IEEE (2011)

  9. Kallenberg, M., Petersen, K., Nielsen, M., Ng, A.Y., Diao, P., Igel, C., Vachon, C.M., Holland, K., Winkel, R.R., Karssemeijer, N., Lillholm, M.: Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016)

    Article  Google Scholar 

  10. Siddique, N., Adeli, H.: Synergies of fuzzy logic, neural networks and evolutionary computing (2013)

  11. Hema, C.R., Paulraj, M.P., Yaacob, S., Adom, A.H., Nagarajan, R.: Motor imagery signal classification for a four state brain machine interface. Int. J. Comput. Inf. Eng. 1(5), 1375–1380 (2007)

    Google Scholar 

  12. Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 80(3), 187–194 (2005)

    Article  Google Scholar 

  13. Ko. D.Y., Benbadis. S.R.: Epilepsy and seizures. Medscape (2016)

  14. Harvard Health Publications, Harvard Medical School (2014) Seizure overview

  15. Harvard Health Publishing. http://www.health.harvard.edu/mind-and-mood/seizure-overview. Accessed 20 Jan 2021

  16. 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 

  17. Jaafar, S.T., Mohammadi, M.: Epileptic seizure detection using deep learning approach. UHD J. Sci. Technol. 3(2), 41–50 (2019)

    Article  Google Scholar 

  18. Hussein, R., Palangi, H., Ward, R., Wang, Z.J.: Epileptic seizure detection: a deep learning approach. arXiv preprint arXiv:1803.09848 (2018)

  19. Yao, X., Li, X., Ye, Q., Huang, Y., Cheng, Q., Zhang, G.: A robust deep learning approach for automatic seizure detection. arXiv preprint arXiv:1812.06562 (2018)

  20. Ullah, I., Hussain, M., Aboalsamh, H.: An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst. Appl. 107, 61–71 (2018)

    Article  Google Scholar 

  21. Lin, Q., Ye, S.Q., Huang, X.M., Li, S.Y., Zhang, M.Z., Xue, Y., Chen, W.S.: Classification of epileptic EEG signals with stacked sparse autoencoder based on deep learning. In: International Conference on Intelligent Computing, pp. 802–810. Springer, Cham (2016)

  22. Brikell, I., Chen, Q., Kuja-Halkola, R., D’Onofrio, B.M., Wiggs, K.K., Lichtenstein, P., Almqvist, C., Quinn, P.D., Chang, Z., Larsson, H.: Medication treatment for attention-deficit/hyperactivity disorder and the risk of acute seizures in individuals with epilepsy. Epilepsia 60(2), 284–293 (2019)

    Article  Google Scholar 

  23. Kalilani, L., Faught, E., Kim, H., Burudpakdee, C., Seetasith, A., Laranjo, S., Friesen, D., Haeffs, K., Kiri, V., Thurman, D.J.: Assessment and effect of a gap between new-onset epilepsy diagnosis and treatment in the US. Neurology 92(19), e2197–e2208 (2019)

    Article  Google Scholar 

  24. Fu, R., Tian, Y., Shi, P., Bao, T.: Automatic detection of epileptic seizures in EEG using sparse CSP and fisher linear discrimination analysis algorithm. J. Med. Syst. 44(2), 1–13 (2020)

    Article  Google Scholar 

  25. Gautam, R., Sharma, M.: Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis. J. Med. Syst. 44(2), 49 (2020)

    Article  Google Scholar 

  26. Kaur, P., Sharma, M.: Diagnosis of human psychological disorders using supervised learning and nature-inspired computing techniques: a meta-analysis. J. Med. Syst. 43(7), 204 (2019)

    Article  Google Scholar 

  27. Yao, X., Li, X., Ye, Q., Huang, Y., Cheng, Q., Zhang, G.Q.: A robust deep learning approach for automatic classification of seizures against non-seizures. arXiv preprint arXiv:1812.06562 (2018)

  28. Yao, X., Cheng, Q., Zhang, G.Q.: Automated classification of seizures against nonseizures: a deep learning approach. arXiv preprint arXiv:1906.02745 (2019)

  29. Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: 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 64(6), 061907 (2001)

    Article  Google Scholar 

  30. Selvan, S., Srinivasan, R.: Removal of ocular artifacts from EEG using an efficient neural network based adaptive filtering technique. IEEE Signal Process. Lett. 6(12), 330–332 (1999)

    Article  Google Scholar 

  31. Wikipedia Contributors: Feature scaling. In: Wikipedia, The Free Encyclopedia (2018, June 24). Retrieved 11:42, February 28, 2020. https://en.wikipedia.org/w/index.php?title=Feature_scaling&oldid=847274325

  32. Agarap, A.F.: Deep learning using rectified linear units (RELU). arXiv preprint arXiv:1803.08375 (2018)

  33. Chowdhury, T.T., Hossain, A., Fattah, S.A., Shahnaz, C.: Seizure and non-seizure EEG signals detection using 1-D convolutional neural network architecture of deep learning algorithm. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1–4. IEEE (2019)

  34. Mandhouj, B., Cherni, M.A., Sayadi, M.: An automated classification of EEG signals based on spectrogram and CNN for epilepsy diagnosis. Analog Integr. Circuits Signal Process. 25, 1–10 (2021)

    Google Scholar 

  35. Wani, S.M., Sabut, S., Nalbalwar, S.L.: Detection of epileptic seizure using wavelet transform and neural network classifier. In: Computing, Communication and Signal Processing, pp. 739–747. Springer, Singapore (2019)

  36. Srinivasan, V., Eswaran, C., Sriraam, N.: Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans. Inf Technol. Biomed. 11(3), 288–295 (2007)

    Article  Google Scholar 

  37. Guo, L., Rivero, D., Dorado, J., Rabunal, J.R., Pazos, A.: Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J. Neurosci. Methods 191(1), 101–109 (2010)

    Article  Google Scholar 

  38. Song, Y., Liò, P.: A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J. Biomed. Sci. Eng. 3(06), 556 (2010)

    Article  Google Scholar 

  39. Acharya, U.R., Molinari, F., Sree, S.V., Chattopadhyay, S., Ng, K.H., Suri, J.S.: Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 7(4), 401–408 (2012)

    Article  Google Scholar 

  40. Chua, K.C., Chandran, V., Acharya, U.R., Lim, C.M.: Application of higher order spectra to identify epileptic EEG. J. Med. Syst. 35(6), 1563–1571 (2011)

    Article  Google Scholar 

  41. Tawfik, N.S., Youssef, S.M., Kholief, M.: A hybrid automated detection of epileptic seizures in EEG records. Comput. Electr. Eng. 53, 177–190 (2016)

    Article  Google Scholar 

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Acknowledgements

All authors are thankful to the University of Freiburg for providing the EEG database.

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Authors and Affiliations

  1. GLA University Mathura, Chaumuhan, India

    Saroj Kumar Pandey

  2. National Institute of Technology, Raipur, India

    Rekh Ram Janghel

  3. Rungta College of Engineering & Technology Bhilai, Bhilai, India

    Pankaj Kumar Mishra

  4. Maulana Azad National Institute of Technology Bhopal, Bhopal, India

    Mitul Kumar Ahirwal

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  1. Saroj Kumar Pandey
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  2. Rekh Ram Janghel
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  4. Mitul Kumar Ahirwal
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Correspondence to Saroj Kumar Pandey.

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Pandey, S.K., Janghel, R.R., Mishra, P.K. et al. Automated epilepsy seizure detection from EEG signal based on hybrid CNN and LSTM model. SIViP 17, 1113–1122 (2023). https://doi.org/10.1007/s11760-022-02318-9

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