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Epileptic Focus Localization Based on iEEG Plot Images by Using Convolutional Neural Network

9 pagesPublished: March 11, 2020

Abstract

Patients with epilepsy need to locate the lesion before surgery. Currently, clinical experts diagnose the lesions through visual judgment. In order to reduce the workload of clinical experts, many automatic diagnostic methods have been proposed. Usually, the automatic diagnostic methods often use only one feature as the basis for diagnosis, which has certain limitations. In this paper, we use multiple feature fusion methods for automatic diagnosis. For the cause of epilepsy: abnormal discharge, we use the filter and entropy to capture the energy features of epilepsy discharge. Due to the epilepsy brain waves contain spike and shape waveforms, short time Fourier transform (STFT) is used to analysis the time-frequency features. In feature fusion, we plot the color map of entropy and spectrogram get from STFT together to combine the different types of features. After the feature extraction and fusion steps, each brain signal is converted into an image. Next, we use the visual analysis capabilities of the convolutional neural network (CNN) to classify the plot image. With the visual recognition ability of CNN, in the experiment, we got a classification accuracy of 88.77%. By using automatic diagnostic methods, the workload of clinical experts is greatly reduced in actual clinical practice.

Keyphrases: Epileptic focus localization, feature fusion, iEEG

In: Qin Ding, Oliver Eulenstein and Hisham Al-Mubaid (editors). Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, vol 70, pages 173--181

Links:
BibTeX entry
@inproceedings{BICOB2020:Epileptic_Focus_Localization_Based,
  author    = {Xuyang Zhao and Linfeng Sui and Toshihisa Tanaka and Jianting Cao and Qibin Zhao},
  title     = {Epileptic Focus Localization Based on iEEG Plot Images by Using Convolutional Neural Network},
  booktitle = {Proceedings of the 12th International Conference on Bioinformatics and Computational Biology},
  editor    = {Qin Ding and Oliver Eulenstein and Hisham Al-Mubaid},
  series    = {EPiC Series in Computing},
  volume    = {70},
  pages     = {173--181},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/MnXs},
  doi       = {10.29007/9jmg}}
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