A Deep Learning Method for Prediction of Benign Epilepsy with Centrotemporal Spikes

  • Ming Yan
  • Ling LiuEmail author
  • Sihan Chen
  • Yi PanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10847)


Benign epilepsy with centrotemporal spikes (BECT) is the most common epilepsy in the children. The research of BECT mainly focuses on the comparative analysis of the BECT patients and the healthy controls. Different from the existing methods, we proposed a 3D convolution neural network (3DCNN) that directly predicts the disease of BECT from raw magnetic resonance imaging (MRI). The experiment shows our 3DCNN model get an \(89.80\%\) accuracy in the five-fold cross-validation evaluation which is over a large margin than the benchmark method.




  1. 1.
    Adebimpe, A., Aarabi, A., Bourel-Ponchel, E., Mahmoudzadeh, M., Wallois, F.: EEG resting state functional connectivity analysis in children with benign epilepsy with centrotemporal spikes. Front. Neurosci. 10, 143 (2016)CrossRefGoogle Scholar
  2. 2.
    Baglietto, M.G., Battaglia, F.M., Nobili, L., Tortorelli, S., De Negri, E., Calevo, M.G., Veneselli, E., De Negri, M.: Neuropsychological disorders related to interictal epileptic discharges during sleep in benign epilepsy of childhood with centrotemporal or rolandic spikes. Dev. Med. Child Neurol. 43(6), 407–412 (2001)CrossRefGoogle Scholar
  3. 3.
    Beaussart, M.: Benign epilepsy of children with rolandic (centro-temporal) paroxysmal foci a clinical entity. Study of 221 cases. Epilepsia 13(6), 795–811 (1972)CrossRefGoogle Scholar
  4. 4.
    Boor, S., Vucurevic, G., Pfleiderer, C., Stoeter, P., Kutschke, G., Boor, R.: EEG-related functional MRI in benign childhood epilepsy with centrotemporal spikes. Epilepsia 44(5), 688–692 (2003)CrossRefGoogle Scholar
  5. 5.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). Scholar
  6. 6.
    Croona, C., Kihlgren, M., Lundberg, S., Eeg-Olofsson, O., Eeg-Olofsson, K.E.: Neuropsychological findings in children with benign childhood epilepsy with centrotemporal spikes. Dev. Med. Child Neurol. 41(12), 813–818 (1999)CrossRefGoogle Scholar
  7. 7.
    Doumlele, K., Friedman, D., Buchhalter, J., Donner, E.J., Louik, J., Devinsky, O.: Sudden unexpected death in epilepsy among patients with benign childhood epilepsy with centrotemporal spikes. JAMA Neurol. 74(6), 645–649 (2017)CrossRefGoogle Scholar
  8. 8.
    Garcia-Ramos, C., Jackson, D.C., Lin, J.J., Dabbs, K., Jones, J.E., Hsu, D.A., Stafstrom, C.E., Zawadzki, L., Seidenberg, M., Prabhakaran, V., et al.: Cognition and brain development in children with benign epilepsy with centrotemporal spikes. Epilepsia 56(10), 1615–1622 (2015)CrossRefGoogle Scholar
  9. 9.
    Gastaut, Y.: Un element deroutant de la semeiologie electroencephalographique: les pointes prerolandique sans signification focale. Rev. Neurol. 87, 408–490 (1952)Google Scholar
  10. 10.
    Gelisse, P., Corda, D., Raybaud, C., Dravet, C., Bureau, M., Genton, P.: Abnormal neuroimaging in patients with benign epilepsy with centrotemporal spikes. Epilepsia 44(3), 372–378 (2003)CrossRefGoogle Scholar
  11. 11.
    Liasis, A., Bamiou, D., Boyd, S., Towell, A.: Evidence for a neurophysiologic auditory deficit in children with benign epilepsy with centro-temporal spikes. J. Neural Transm. 113(7), 939–949 (2006)CrossRefGoogle Scholar
  12. 12.
    Neubauer, B., Fiedler, B., Himmelein, B., Kämpfer, F., Lässker, U., Schwabe, G., Spanier, I., Tams, D., Bretscher, C., Moldenhauer, K., et al.: Centrotemporal spikes in families with rolandic epilepsy linkage to chromosome 15q14. Neurology 51(6), 1608–1612 (1998)CrossRefGoogle Scholar
  13. 13.
    Yu, N., Li, Z., Yu, Z.: A survey on encoding schemes for genomic data representation and feature learning? From signal processing to machine learning. Big Data Min. Anal. 1(3), 23–40 (2018)CrossRefGoogle Scholar
  14. 14.
    Peng, S., You, R., Wang, H., Zhai, C., Mamitsuka, H., Zhu, S.: DeepMeSH: deep semantic representation for improving large-scale MeSH indexing. Bioinformatics 32(12), i70–i79 (2016)CrossRefGoogle Scholar
  15. 15.
    Roth, H.R., et al.: A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 520–527. Springer, Cham (2014). Scholar
  16. 16.
    Uliel-Sibony, S., Kramer, U.: Benign childhood epilepsy with centro-temporal spikes (BCECTSs), electrical status epilepticus in sleep (ESES), and academic decline? How aggressive should we be? Epilepsy Behav. 44, 117–120 (2015)CrossRefGoogle Scholar
  17. 17.
    Zeng, H., Ramos, C.G., Nair, V.A., Hu, Y., Liao, J., La, C., Chen, L., Gan, Y., Wen, F., Hermann, B., et al.: Regional homogeneity (ReHo) changes in new onset versus chronic benign epilepsy of childhood with centrotemporal spikes (BECTs): a resting state fMRI study. Epilepsy Res. 116, 79–85 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer ScienceSichuan UniversityChengduPeople’s Republic of China
  2. 2.Department of Computer ScienceGeorgia State UniversityAtlantaUSA
  3. 3.West China HospitalSichuan UniversityChengduPeople’s Republic of China

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