An algorithm for XXXX onset detection using intracranial EEG.

@entity1 @entity410
This article addresses the problem of real-time @entity410 detection from intracranial EEG (IEEG). One difficulty in creating an approach that can be used for many @entity1 is the heterogeneity of @entity410 IEEG patterns across different @entity1 and even within a @entity1 . In addition, simultaneously maximizing sensitivity and minimizing latency and false detection rates has been challenging as these are competing objectives. Automated machine learning systems provide a mechanism for dealing with these hurdles. Here we present and evaluate an algorithm for real-time @entity410 onset detection from IEEG using a machine-learning approach that permits a @entity1 -specific solution. We extract temporal and spectral features across all intracranial EEG channels. A pattern recognition component is trained using these feature vectors and tested against unseen continuous data from the same @entity1 . When tested on more than 875 hours of IEEG data from 10 @entity1 , the algorithm detected 97% of 67 test @entity410 of several types with a median detection delay of 5 seconds and a median false alarm rate of 0.6 false alarms per 24-hour period. The sensitivity was 100% for 8 of 10 @entity1 . These results indicate that a sensitive, specific, and relatively short-latency detection system based on machine learning can be employed for @entity410 detection from EEG using a full set of intracranial electrodes to individual @entity1 . This article is part of a Supplemental Special Issue entitled The Future of Automated @entity410 Detection and Prediction.
