Summary
Objective
One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including EEG, continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra long-term subcutaneous EEG.
Methods
We analyzed a two-center cohort of ultra long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46 to 230 days with median 18 hours/day of recorded data, totaling over 11,000 hours of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient.
Three candidate subject-specific long short-term memory (LSTM) network deep learning classifiers were trained offline and pseudo-prospectively on preictal (1-hour before) and interictal (more than one day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation.
Results
Depending on each architecture, significant forecasting performance was achieved in three to five out of six patients, with overall mean area under the ROC curve of 0.65 to 0.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence.
Significance
This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally-invasive EEG.
ABR