Abstract
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.
Keywords
Supported by the science and technology department of Sichuan province (No. 18MZGC0127).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
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)
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)
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)
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). https://doi.org/10.1007/978-3-319-46723-8_49
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)
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)
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)
Gastaut, Y.: Un element deroutant de la semeiologie electroencephalographique: les pointes prerolandique sans signification focale. Rev. Neurol. 87, 408–490 (1952)
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)
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)
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)
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)
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)
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). https://doi.org/10.1007/978-3-319-10404-1_65
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)
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)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Yan, M., Liu, L., Chen, S., Pan, Y. (2018). A Deep Learning Method for Prediction of Benign Epilepsy with Centrotemporal Spikes. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-94968-0_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94967-3
Online ISBN: 978-3-319-94968-0
eBook Packages: Computer ScienceComputer Science (R0)