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Epilepsy Detection with Multi-channel EEG Signals Utilizing AlexNet

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Abstract

In this work, we investigate epilepsy seizure detection using machine learning. In the literature, a machine learning model is usually trained to help automate the epileptic detection process, eliminating the need for human intervention. Typically, the dataset is split into training and test sets in a way to maximize the detection accuracy. This requires the training set to include enough EEG samples for every possible patient in order to improve the accuracy numbers during the prediction. However, this might not be easy or practical in real life. A new patient might not have a previous record in the training set, and hence, the prediction for this particular patient might not meet the expected accuracy. The main contribution in this work is to study the impact of the training and test datasets selection from practical point of view on the accuracy and efficacy of the CNN prediction. In this work, a CNN model, namely AlexNet, is trained to detect epileptic states, namely preictal, interictal and ictal, in subjects using electroencephalogram (EEG) signals. The dataset includes the three epileptic zones of subjects taken from three medical centers, collected by the Fragility Multi-Center Retrospective Study. Furthermore, we propose a framework to utilize a feature extraction technique that exploits the available multiple channels of EEG signals to minimize information loss. As part of the main contribution, three different approaches are proposed to split the EEG sample dataset into the training and test sets. Thus, the prediction performance is evaluated based on the prior knowledge extracted from the particular samples picked for the training set. The results show that the proposed framework achieves an overall accuracy of 94.44% when the training contained samples from each patient. The accuracy is reduced to 92.98% when the training set contained a subset of the patient pool. A binary classification is also performed with up to 98% accuracy for both scenarios.

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References

  1. U.R. Acharya, Y. Hagiwara, H. Adeli, Automated seizure prediction. Epilepsy Behav. 88, 251–261 (2018). https://doi.org/10.1016/j.yebeh.2018.09.030

    Article  Google Scholar 

  2. U.R. Acharya, S.L. Oh, Y. Hagiwara, J.H. Tan, H. Adeli, Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270–278 (2018). https://doi.org/10.1016/j.compbiomed.2017.09.017

    Article  Google Scholar 

  3. U.R. Acharya, Y. Hagiwara, S.N. Deshpande, S. Suren, J.E.W. Koh, S.L. Oh, N. Arunkumar, E.J. Ciaccio, C.M. Lim, Characterization of focal EEG signals: a review. Futur. Gener. Comput. Syst. 91, 290–299 (2019). https://doi.org/10.1016/j.future.2018.08.044

    Article  Google Scholar 

  4. T. Åkerstedt, M. Gillberg, Sleep duration and the power spectral density of the EEG. Electroencephalogr. Clin. Neurophysiol. 64(2), 2520 (1986). https://doi.org/10.1016/0013-4694(86)90106-9

    Article  Google Scholar 

  5. F. Al-Ali, T.D. Gamage, H.W.T.S. Nanayakkara, F. Mehdipour, S.K. Ray, Novel casestudy and benchmarking of AlexNet for edge AI: from CPU and GPU to FPGA. In: Canadian Conference on Electrical and Computer Engineering, vol. 2020-Augus (2020). https://doi.org/10.1109/CCECE47787.2020.9255739

  6. E. Alickovic, J. Kevric, A. Subasi, Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed. Signal Process. Control 39, 94–102 (2018). https://doi.org/10.1016/J.BSPC.2017.07.022

    Article  Google Scholar 

  7. K.M. Almustafa, Classification of epileptic seizure dataset using different machine learning algorithms. Inf. Med. Unlocked 21, 100444 (2020). https://doi.org/10.1016/j.imu.2020.100444

    Article  Google Scholar 

  8. R.G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, C.E. Elger, 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. Phys. Plasmas Fluids Relat. Interdiscip. Topics 64(6), 8 (2001). https://doi.org/10.1103/PhysRevE.64.061907

    Article  Google Scholar 

  9. S. Appelhoff, M. Sanderson, T. Brooks, M. Vliet, R. Quentin, C. Holdgraf, M. Chaumon, E. Mikulan, K. Tavabi, R. Höchenberger, D. Welke, C. Brunner, A. Rockhill, E. Larson, A. Gramfort, M. Jas, MNE-BIDS: organizing electrophysiological data into the BIDS format and facilitating their analysis. J. Open Source Softw. 4(44), 1896 (2019). https://doi.org/10.21105/joss.01896

    Article  Google Scholar 

  10. E. Bou Assi, D.K. Nguyen, S. Rihana, M. Sawan, Towards accurate prediction of epileptic seizures: a review. Biomed. Signal Process. Control 34, 144–157 (2017). https://doi.org/10.1016/j.bspc.2017.02.001

    Article  Google Scholar 

  11. CHB-MIT: CHB-MIT scalp EEG database. (2000). https://physionet.org/content/chbmit/1.0.0/

  12. Freiburg: Freiburg seizure prediction project. Freiburg, Germany. https://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project/eeg-database

  13. K. Gadhoumi, J.-M. Lina, F. Mormann, J. Gotman, Seizure prediction for therapeutic devices: a review. J. Neurosci. Methods 260, 270–282 (2016). https://doi.org/10.1016/j.jneumeth.2015.06.010

    Article  Google Scholar 

  14. Y. Gao, B. Gao, Q. Chen, J. Liu, Y. Zhang, Deep convolutional neural network-based epileptic electroencephalogram (EEG) signal classification. Front. Neurol. 11, 375 (2020). https://doi.org/10.3389/fneur.2020.00375

    Article  Google Scholar 

  15. A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.-K. Peng, H.E. Stanley, PhysioBank, physioToolkit and physioNet. Circulation 101(23), 215 (2000). https://doi.org/10.1161/01.cir.101.23.e215

    Article  Google Scholar 

  16. C. Holdgraf, S. Appelhoff, S. Bickel, K. Bouchard, S. D’Ambrosio, O. David, O. Devinsky, B. Dichter, A. Flinker, B. L. Foster, K. J. Gorgolewski, I. Groen, D. Groppe, A. Gunduz, L. Hamilton, C. J. Honey, M. Jas, R. Knight, J.-P. Lachaux, J. C. Lau, C. Lee-Messer, B. N. Lundstrom, K. J. Miller, J.G. Ojemann, R. Oostenveld, N. Petridou, G. Piantoni, A. Pigorini, N. Pouratian, N. F. Ramsey, A. Stolk, N. C. Swann, F. Tadel, B. Voytek, B. A. Wandell, J. Winawer, K. Whitaker, L. Zehl, D. Hermes, BIDS-iEEG: an extension to the brain imaging data structure (BIDS) specification for human intracranial electrophysiology. Sci. Data 6(102), 26 (2019)

  17. W. Hu, J. Cao, X. Lai, J. Liu, Mean amplitude spectrum based epileptic state classification for seizure prediction using convolutional neural networks. J. Ambient Intell. Hum. Comput. (2019). https://doi.org/10.1007/s12652-019-01220-6

    Article  Google Scholar 

  18. S. Ibrahim, S. Majzoub, Adaptive epileptic seizure prediction based on EEG synchronization. J. Biomim. Biomater. Biomed. Eng. 33, 52 (2017) https://doi.org/10.4028/www.scientific.net/JBBBE.33.52

  19. E.M. Imah, A. Widodo, A comparative study of machine learning algorithms for epileptic seizure classification on EEG signals. In: 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 401–408 (2017). https://doi.org/10.1109/ICACSIS.2017.8355065

  20. P. Jahankhani, V. Kodogiannis, K. Revett, EEG Signal classification using wavelet feature extraction and neural networks. In: IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA’06), pp. 120–124 (2006). https://doi.org/10.1109/JVA.2006.17

  21. H. Khan, L. Marcuse, M. Fields, K. Swann, B. Yener, Focal onset seizure prediction using convolutional networks. IEEE Trans. Biomed. Eng. 65(9), 2109–2118 (2018). https://doi.org/10.1109/TBME.2017.2785401

    Article  Google Scholar 

  22. M.A. Kramer, E.D. Kolaczyk, H.E. Kirsch, Emergent network topology at seizure onset in humans. Epilepsy Res. 79(2–3), 173–186 (2008). https://doi.org/10.1016/j.eplepsyres.2008.02.002

    Article  Google Scholar 

  23. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386

    Article  Google Scholar 

  24. L. Kuhlmann, K. Lehnertz, M.P. Richardson, B. Schelter, H.P. Zaveri, Seizure prediction—ready for a new era. Nat. Rev. Neurol. 14(10), 618–630 (2018). https://doi.org/10.1038/s41582-018-0055-2

    Article  Google Scholar 

  25. Y. Kumar, M.L. Dewal, R.S. Anand, Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. Signal Image Video Process. 8(7), 1323–1334 (2014). https://doi.org/10.1007/s11760-012-0362-9

    Article  Google Scholar 

  26. A. Li, C. Huynh, Z. Fitzgerald, I. Cajigas, D. Brusko, J. Jagid, A.O. Claudio, A.M. Kanner, J. Hopp, S. Chen, J. Haagensen, E. Johnson, W. Anderson, N. Crone, S. Inati, K.A. Zaghloul, J. Bulacio, J. Gonzalez-Martinez, S.V. Sarma, Neural fragility as an EEG marker of the seizure onset zone. Nat. Neurosci. 24(10), 1465–1474 (2021). https://doi.org/10.1038/s41593-021-00901-w

    Article  Google Scholar 

  27. M. Moshinsky, Characterization of focal EEG signals: A review — Science direct (1959). https://www.sciencedirect.com/science/article/pii/S0167739X18318818

  28. S. Opałka, B. Stasiak, D. Szajerman, A. Wojciechowski, Multi-channel convolutional neural networks architecture feeding for effective EEG mental tasks classification. Sensors (Switzerland) 18(10), (2018) https://doi.org/10.3390/s18103451

  29. C.R. Pernet, S. Appelhoff, K.J. Gorgolewski, G. Flandin, C. Phillips, A. Delorme, R. Oostenveld, EEG-BIDS, an extension to the brain imaging data structure for electroencephalography (2019). https://doi.org/10.1038/s41597-019-0104-8

  30. F. Pisano, G. Sias, A. Fanni, B. Cannas, A. Dourado, B. Pisano, C.A. Teixeira, Convolutional neural network for seizure detection of nocturnal frontal lobe epilepsy. Complexity 2020, 4825767 (2020). https://doi.org/10.1155/2020/4825767

    Article  Google Scholar 

  31. T. Radüntz, J. Scouten, O. Hochmuth, B. Meffert, Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features. J. Neural Eng. 14, 46004 (2017). https://doi.org/10.1088/1741-2552/aa69d1

    Article  Google Scholar 

  32. M. Sharma, R.B. Pachori, U. Rajendra Acharya, A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognit. Lett. 94, 172–179 (2017). https://doi.org/10.1016/j.patrec.2017.03.023

    Article  Google Scholar 

  33. M. Sharma, R.B. Pachori, U. Rajendra Acharya, A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognit. Lett. 94, 172–179 (2017). https://doi.org/10.1016/J.PATREC.2017.03.023

    Article  Google Scholar 

  34. A. Shoeibi, M. Khodatars, N. Ghassemi, M. Jafari, P. Moridian, R. Alizadehsani, M. Panahiazar, F. Khozeimeh, A. Zare, H. Hosseini-Nejad, A. Khosravi, A.F. Atiya, D. Aminshahidi, S. Hussain, M. Rouhani, S. Nahavandi, U.R. Acharya, Epileptic seizures detection using deep learning techniques: a review. Int. J. Environ. Res. Pub. Health 2021, Vol. 18, Page 5780 18(11), 5780 (2021) https://doi.org/10.3390/IJERPH18115780, arXiv:2007.01276

  35. M.K. Siddiqui, R. Morales-Menendez, X. Huang, N. Hussain, A review of epileptic seizure detection using machine learning classifiers. Brain Inf. 7(1), 1–18 (2020). https://doi.org/10.1186/s40708-020-00105-1

    Article  Google Scholar 

  36. Y. Song, J. Crowcroft, J. Zhang, Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J. Neurosci. Methods 210(2), 132–146 (2012). https://doi.org/10.1016/j.jneumeth.2012.07.003

    Article  Google Scholar 

  37. A. Subasi, J. Kevric, M. Abdullah Canbaz, Epileptic seizure detection using hybrid machine learning methods. Neural Comput. Appl. 31(1), 317–325 (2019). https://doi.org/10.1007/s00521-017-3003-y

    Article  Google Scholar 

  38. D.K. Thara, B.G. PremaSudha, F. Xiong, Auto-detection of epileptic seizure events using deep neural network with different feature scaling techniques. Pattern Recog. Lett. 128, 544–550 (2019) https://doi.org/10.1016/j.patrec.2019.10.029

  39. P. Thodoroff, J. Pineau, A. Lim, Learning robust features using deep learning for automatic seizure detection. (2016) CoRR abs/1608.0

  40. N.D. Truong, A.D. Nguyen, L. Kuhlmann, M.R. Bonyadi, J. Yang, S. Ippolito, O. Kavehei, Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw. 105, 104–111 (2018). https://doi.org/10.1016/j.neunet.2018.04.018

    Article  Google Scholar 

  41. S. Yang, B. Deng, J. Wang, H. Li, M. Lu, Y. Che, X. Wei, K.A. Loparo, Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons. IEEE Trans. Neural Netw. Learn. Syst. 31(1), 148–162 (2020). https://doi.org/10.1109/TNNLS.2019.2899936

    Article  Google Scholar 

  42. S. Yang, J. Tan, B. Chen, Robust spike-based continual meta-learning improved by restricted minimum error entropy criterion. Entropy 24(4), 24040455 (2022). https://doi.org/10.3390/e24040455

    Article  MathSciNet  Google Scholar 

  43. S. Yang, B. Linares-Barranco, B. Chen, Heterogeneous ensemble-based spike-driven few-shot online learning. Front. Neurosci. 16, 850932 (2022). https://doi.org/10.3389/fnins.2022.850932

    Article  Google Scholar 

  44. S. Yang, T. Gao, J. Wang, B. Deng, M.R. Azghadi, T. Lei, B. Linares-Barranco, SAM: a unified self-adaptive multicompartmental spiking neuron model for learning With working memory. Front. Neurosci. 16, 850945 (2022). https://doi.org/10.3389/fnins.2022.850945

    Article  Google Scholar 

  45. S. Yang, J. Wang, B. Deng, M.R. Azghadi, B. Linares-Barranco, Neuromorphic context-dependent learning framework with fault-tolerant spike routing. IEEE Trans. Neural Netw. Learn. Syst. 33(12), 7126–7140 (2022). https://doi.org/10.1109/TNNLS.2021.3084250

    Article  Google Scholar 

  46. S. Yang, J. Wang, X. Hao, H. Li, X. Wei, B. Deng, K.A. Loparo, BiCoSS: toward large-scale cognition brain with multigranular neuromorphic architecture. IEEE Trans. Neural Netw. Learn. Syst. 33(7), 2801–2815 (2022). https://doi.org/10.1109/TNNLS.2020.3045492

    Article  Google Scholar 

  47. M. Zhou, C. Tian, R. Cao, B. Wang, Y. Niu, T. Hu, H. Guo, J. Xiang, Epileptic seizure detection based on EEG signals and CNN. Front. Neuroinform. 12, 95 (2018). https://doi.org/10.3389/fninf.2018.00095

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Sharjah, Sharjah, 27272, Sharjah, UAE

    Sohaib Majzoub, Ahmed Fahmy, Maha Diab & Soliman Mahmoud

  2. Electrical and Computer Engineering Department, Gulf University for Science and Technology, Mishref, Kuwait

    Fadi Sibai

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  1. Sohaib Majzoub
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Majzoub, S., Fahmy, A., Sibai, F. et al. Epilepsy Detection with Multi-channel EEG Signals Utilizing AlexNet. Circuits Syst Signal Process 42, 6780–6797 (2023). https://doi.org/10.1007/s00034-023-02423-1

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