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Found 3 papers, 2 papers with code
MICAL: Mutual Information-Based CNN-Aided Learned Factor
Since the soft estimates obtained as the combined features from the neural MI estimator and the CNN do not capture the temporal correlation between different EEG blocks, we use them not as estimates of the seizure state, but to compute the function nodes of a factor graph.
CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection
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.
Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs
We propose a computationally efficient algorithm for seizure detection.
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