1 code implementation • 6 Jun 2022 • Bahareh Salafian, Eyal Fishel Ben-Knaan, Nir Shlezinger, Sandrine de Ribaupierre, Nariman Farsad
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.
no code implementations • 11 Mar 2022 • Bahareh Salafian, Eyal Fishel Ben-Knaan, Nir Shlezinger, Sandrine de Ribaupierre, Nariman Farsad
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.
1 code implementation • 5 Aug 2021 • Bahareh Salafian, Eyal Fishel Ben, Nir Shlezinger, Sandrine de Ribaupierre, Nariman Farsad
We propose a computationally efficient algorithm for seizure detection.