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- W4225264561 abstract "We propose a convolutional neural network (CNN) aided factor graphs assisted by mutual information features estimated by a neural network for seizure detection. Specifically, we use neural mutual information estimation to evaluate the correlation between different electroencephalogram (EEG) channels as features. We then use a 1D-CNN to extract extra features from the EEG signals and use both features to estimate the probability of a seizure event. Finally, learned factor graphs are employed to capture the temporal correlation in the signal. Both sets of features from the neural mutual estimation and the 1D-CNN are used to learn the factor nodes. We show that the proposed method achieves state-of-the-art performance using 6-fold leave-four-patients-out cross-validation." @default.
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- W4225264561 date "2022-05-23" @default.
- W4225264561 modified "2023-09-27" @default.
- W4225264561 title "CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection" @default.
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- W4225264561 doi "https://doi.org/10.1109/icassp43922.2022.9746730" @default.
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