Anguelova_2024

Public_repository_Anguelova_2024_PLOSOne

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

Summary: 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, a method not previously applied in this field. 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 comprising of the following layers: visible, convolutional, max pooling, dropout, hidden dense (100 neurons) and output. Data was divided into an 80/20 training/validation, resulting in 3049 epochs for training/validation and 580 epochs for testing

Software: Matlab R2021a (The MathWorks, Inc., Natick, MA) Python 3.10 using Keras 2.6.0, Tensorflow 2.8.0 and scikit-learn 1.0.2

Content:

Code:

• create_dummy_var_2024_02_14.m Create the dummy EEG variable to test the second-level network.
• plot_EEGepoch_dummy_var_2024_02_18.m Plot the dummy EEG variable epochs.
• selection_EEGepochs_2024_02_14.m Selection of the EEG epochs by the first-level network with a specific probability of being an IED. The first-level network is published elsewhere by da Silva et al., 2021.
• plot_EEGepoch_2024_02_18.m Plot the EEG epochs, for scoring.
• App_2023_09_15.mlapp MATLAB App used for scoring the EEG epochs.
• modelAn_EEG_2024_02_14.py Deep leraning model of the second-level network. The first-level network is published elsewhere by da Silva et al., 2021.
• ROC_curve_2023_09_18.m Plotting the ROC curve for dummy EEG variable and the true EEG epochs.

Data:

We are not allowed to share the patients’ EEG data. However, the figures of the anonymous EEG epochs that were scored with MATLAB App are provided

• Figures -- Figuren_seizureLocation_selectie_deel1_nolegend -- Figuren_seizureLocation_selectie_deel2_nolegend

Authors: Galia Valentinova Anguelova MD MSc PhD and Patricia Margaret Baines MSc

Contact information: ganguelova@sein.nl

References

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