Anguelova_2024/datacite.yml

authors:
  -
    firstname: Galia
    lastname: Anguelova
    affiliation: 'SEIN - Stichting Epilepsie Instellingen Nederland'
    id: 'ORCID:0000-0001-7633-2018'
  -
    firstname: Patricia
    lastname: Baines
    affiliation: 'Delft University of Technology' 
    id: 'ORCID:0000-0002-1841-2551'

title: 'Post-processing deep neural network for performance improvement of interictal epileptiform discharges detection'

description: 'In this study we aimed to further improve interictal epileptiform discharges (IED) detection performance of a deep neural network based algorithm with a second-level post-processing deep learning network. Seventeen interictal ambulatory EEGs were used, 15 with focal and 2 with generalized epilepsy in patients of age range 4-80 years (median 19y, 25th-75th percentile 14-32y). The EEG data was split into 2s non-overlapping epochs. Epochs with an IED probability of at least 0.99, according to a previously developed VGG-C based convolutional neural network, were preselected for the second-level 2D convolutional neural network (CNN). The selected epochs were used as input for the second-level post-processing CNN in Python; these are uploaded as a dataset in this repository. Data was divided into an 80/20 training/validation, resulting in 3049 epochs for training/validation and 580 epochs for testing.'

keywords:
  - Neuroscience
  - 'deep learning'
  - EEG
  - 'interictal epileptiform discharges'

license:
  name: 'Creative Commons Attribution 4.0 International Public License'
  url: 'http://creativecommons.org/licenses/by/4.0/'

references:
  -
    id: 'doi:tba'
    reftype: IsSupplementTo
    citation: 'Anguelova GV, Baines PM. Technical note: Post-processing deep neural network for performance improvement of interictal epileptiform discharges detection. IEEE JTEHM. Submitted'

resourcetype: Dataset

templateversion: 1.2