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Epileptic seizure forecasting with long short-term memory (LSTM) neural networks
Monitoring the change in EEG features over time allowed our model to produce good results over a range of different window sizes, which is an improvement on previous models and raises the possibility of altering the forecast to meet individual patient needs.
Path Signatures for Seizure Forecasting
In computational neuroscience, the prediction of future epileptic seizures from brain activity measurements, using EEG data, remains largely unresolved despite much dedicated research effort.
Brain Model State Space Reconstruction Using an LSTM Neural Network
Approach An LSTM filter was trained on simulated EEG data generated by a neural mass model using a wide range of parameters.
Chronic iEEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states
We then compared SNS occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate.
Seizure pathways and seizure durations can vary independently within individual patients with focal epilepsy
However, it is unclear whether seizures following the same pathway will have the same duration or if these features can vary independently.
