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iESPnet published on Epilepsia


Check out our deep learning approach for the identification and onset prediction of electrographic seizure patterns (ESPs) in brain data recorded with the responsive neurostimulation device. iESPnet is a convolutional and recurrent neural network able to predict the presence of ESPs and their onset across patients and data cohort with an accuracy of about 90% and time onset error in between 3.6 and 5 seconds.  

The code is open and available at GitHub: https://github.com/Brain-Modulation-Lab/Paper_iESPnet 




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