Deep net detection and onset prediction of electrographic seizure patterns in responsive neurostimulation

Abstract

ObjectiveManaging the progress of drug‐resistant epilepsy patients implanted with the Responsive Neurostimulation (RNS) System requires the manual evaluation of hundreds of hours of intracranial recordings. The generation of these large amounts of data and the scarcity of experts' time for evaluation necessitate the development of automatic tools to detect intracranial electroencephalographic (iEEG) seizure patterns (iESPs) with expert‐level accuracy. We developed an intelligent system for identifying the presence and onset time of iESPs in iEEG recordings from the RNS device.MethodsAn iEEG dataset from 24 patients (36 293 recordings) recorded by the RNS System was used for training and evaluating a neural network model (iESPnet). The model was trained to identify the probability of seizure onset at each sample point of the iEEG. The reliability of the net was assessed and compared to baseline methods, including detections made by the device. iESPnet performance was measured using balanced accuracy and the F1 score for iESP detection. The prediction time was assessed via both the error and the mean absolute error. The model was evaluated following a hold‐one‐out strategy, and then validated in a separate cohort of 26 patients from a different medical center.ResultsiESPnet detected the presence of an iESP with a mean accuracy value of 90% and an onset time prediction error of approximately 3.4 s. There was no relationship between electrode location and prediction outcome. Model outputs were well calibrated and unbiased by the RNS detections. Validation on a separate cohort further supported iESPnet applicability in real clinical scenarios. Importantly, RNS device detections were found to be less accurate and delayed in nonresponders; therefore, tools to improve the accuracy of seizure detection are critical for increasing therapeutic efficacy.SignificanceiESPnet is a reliable and accurate tool with the potential to alleviate the time‐consuming manual inspection of iESPs and facilitate the evaluation of therapeutic response in RNS‐implanted patients.

Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants

“…We optimized these parameters for sample-wise seizure classification to maximize the F1-score. We focused on F1 scores instead of balanced accuracy, because ‘seizure present’ (true positive) predictions are more critical for clinical scenarios than correct ‘seizure absent’ (true negative) predictions, which is why they are commonly used as a metric for RNS seizure prediction performance 33 .…”