• Logo
  • HamaraJournals

Recognition of Epileptic Seizures in EEG Records: A Transfer Learning Approach

Elena Caires Silveira, Caio Fellipe Santos Corrêa
123

Views


Abstract

Introduction: Seizure is a transient phenomenon with genesis in excessive abnormal or synchronous neuronal electrical activity in the brain, while epilepsy is defined as a brain dysfunction characterized by persistent predisposition to generate seizures. The identification of epileptogenic electroencephalographic patterns can be performed using machine learning.the present study aimed to develop a transfer learning based classifier able to detect epileptic seizures in images generated from electroencephalographic data graphic representation.

Material and Methods: We used the Epileptic Seizure Recognition Data Set,which consists of 500 brain activity records for 23.6 seconds comprising 23 chunks of 178 data points, and transformed the resulting 11500 instances into images by graphically plotting its data points. Those images were then splitted in training and test set and used to build and assess, respectvely, a transfer learning-based deep neural network, which classified the images according the presence or absence of epileptic seizures.

Results: The model achieved 100% accuracy, sensitivity and specificity, with a AUC-score of 1.0, demonstrating the great potential of transfer learning for the analysis of graphically represented electroencephalographic data.

Conclusion: It is opportune to raise new studies involving transfer learning for the analysis of signal data, with the aim of improving, disseminating and validating its use for daily clinical practice.


References

Jameson JL, Fauci A, Kasper D, Hauser S, Longo D, Loscalzo J. Harrison’s principles of internal medicine. 20th ed. New York: The McGraw-Hill Companies, Inc.; 2016.

Fisher RS, Boas WVE, Blume W, Elger C, Genton P, Lee P, et al. Epileptic seizures and epilepsy: Definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia. 2005; 46(4): 470–2. PMID: 15816939 DOI: 10.1111/j.0013-9580.2005.66104.x

Gloor P. Neuronal generators and the problem of localization in electroencephalography: Application of volume conductor theory to electroencephalography. J Clin Neurophysiol. 1985; 2(4): 327–54. PMID: 4056020 DOI: 10.1097/00004691-198510000-00002

Browne TR, Holmes GL. Epilepsy: Definitions and background. In: Browne TR, Holmes GL [eds]. Handbook of epilepsy. Philadelphia: Lippincott-Raven; 1997.

Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018; 319(13): 1317-8. PMID: 29532063 DOI: 10.1001/jama.2017.18391

Gulshan V, Peng L, Coram M, Stump M, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016; 316(22): 2402-10. PMID: 27898976 DOI: 10.1001/jama.2016.17216

Stead WW. Clinical implications and challenges of artificial intelligence and deep learning. JAMA. 2018; 320(11): 1107-8. PMID: 30178025 DOI: 10.1001/jama.2018.11029

Weiss K, Khoshgoftaar M, Wang D. A survey of transfer learning. Journal of Big Data. 2016; 3(1): 9.

Raghu M, Zhang C, Kleinberg J, Bengio S. Transfusion: Understanding transfer learning for medical imaging. Advances in Neural Information Processing Systems. arXiv Prepint; 2019.

Andrzejak R, Lehnertz K, Rieke C, Mormann F, David P, Elger CE. 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 Nonlin Soft Matter Phys. 2001; 64(6 Pt 1): 061907. PMID: 11736210 DOI: 10.1103/PhysRevE.64.061907

Wu Q, Fokoue E. Epileptic seizure recognition data set [Internet]. 2017 [cited: 2020 Sep 15]. Avaliable from: https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition.

Dua D, Graff C. UCI machine learning repository [Internet]. 2001 [cited: 2019 Sep 15]. Available from: http://archive.ics.uci.edu/ml

Chollet F. Xception: Deep learning with depthwise separable convolutions. IEEE Conference on Computer Vision and Pattern Recognition. IEEE; 2017.

Kumar A, Kolekar MH. Machine learning approach for epileptic seizure detection using wavelet analysis of EEG signals. International Conference on Medical Imaging, m-Health and Emerging Communication Systems. IEEE; 2014.

Wang L, Xue W, Li Y, Luo M, Huang J, Cui W, et al. Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis. Entropy. 2017; 19(6): 222.

Subasi A, Kevric J, Abdullah M. Epileptic seizure detection using hybrid machine learning methods. Neural Computing and Applications. 2019; 31 :317-25.

Yuan Y, Xun G, Jia K, Zhang A. A novel wavelet-based model for EEG epileptic seizure detection using multi-context learning. International Conference on Bioinformatics and Biomedicine. IEEE; 2017.

Hussein R, Palangi H, Ward R, Wang ZJ. Epileptic seizure detection: A deep learning approach. arXiv Prepint. 2018.

Bizopoulos P, Lambrou G, Koutsouris D. Signal2image modules in deep neural networks for EEG classification. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2019.




DOI: http://dx.doi.org/10.30699/fhi.v10i1.268

Refbacks

  • There are currently no refbacks.