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Convolutional Neural Networks for Early Seizure Alert System

  • T. Iešmantas
  • R. Alzbutas
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 66)

Abstract

A general framework of a system for early seizure detection and alert is presented. Many studies have shown high potential of electroencephalograms (EEG) when there are used together with machine learning algorithms for seizure/non-seizure classification task. In this paper, mainly guidelines will be presented on how to use convolutional neural networks for the purpose of highly accurate classification of non-invasive EEG for patients with epilepsy. Convolutional neural networks can be pre-trained on a sample data as described in this paper and then implemented into an application or a device, which readjusts its parameters according to the patient-specific EEG patterns and thus can be further used as a seizure monitoring and alert system. The paper also demonstrated how transfer learning can be applied to create a patient-specific classifier with high accuracy.

Keywords

EEG Epilepsy Convolutional neural networks Deep learning Classification 

Notes

Acknowledgements

T. Iesmantas was supported by the postdoctoral fellowship grant, provided by the Kaunas University of Technology, Faculty of Mathematics and Natural Sciences.

T. Iesmantas was partially supported by the postdoctoral fellowship grant, provided by the Kaunas University of Technology, Faculty of Mathematics and Natural Sciences. In addition, part of the research presented in this paper was based upon work from COST Action (ENJECT TD 1405), supported by COST (European Cooperation in Science and Technology).

Conflict of Interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Faculty of Mathematics and Natural Sciences, Department of Applied Mathematics KaunasKaunas University of TechnologyKaunasLithuania

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