Automated seizure onset detection for accurate onset time determination in intracranial EEG.

Objective: A novel algorithm for automated seizure onset detection is presented. The method allows for precise identification of electrographic seizure onset times within large databases of electrographic data.

Methods: The patient-specific algorithm extracts salient spectral and temporal features in five frequency bands within a sliding window of an electrographic recording. Feature windows are classified as containing or not containing a seizure onset via support vector machines. A clustering and regression analysis is utilized to accurately localize seizure onsets in time. User-adjustable parameters allow for tuning of detection sensitivity, false positive rate, and latency. The method was tested on intracranial electrographic data recorded from six patients with a total of 1792 recorded seizure onsets from 8246 total electrographic recordings.

Results: Testing of algorithm performance via cross-validation resulted in sensitivities between 80% and 98%, false positive rates from 0.002 to 0.046 per minute (0.12-2.8 per hour), and median detection time within 100ms of the electrographic onset for all patients. In five of the six patients, more than 90% of all detected onsets were less than 3s from the electrographic onset.

Conclusions: The detection system was able to detect seizure onset times in a temporally unbiased fashion with low latency while maintaining reasonable sensitivities and false positive rates. The regression algorithm for temporal localization of onsets confers a considerable benefit in terms of detection latency.

Significance: With the use of our algorithm, large databases of electrographic data can be rapidly processed and seizure onset times accurately marked, facilitating research and analyses of peri-onset events that require precise seizure onset alignment.