S127. Computer aided evaluation of intracerebral depth electrode recordings by automatic seizure detection

Abstract

Introduction Preoperative evaluation of patients with medically intractable epilepsies requires accurate localization of the epileptogenic zone. Intracerebral depth electrodes are used to asses regions of interest and to enclose the resection zone during presurgical planning. Often a high number of needles is needed to accurately define the resection volume. Manual evaluation of these prolonged recordings including a large number of channels which is highly time consuming. To raise efficiency we developed a computer algorithm for automatic detection of epileptic seizures in depth electrode recordings. Primary goal was to detect seizures with high sensitivity without the need to set patient specific parameters. Methods The automatic seizure detection algorithm for depth electrode data was based on an existing seizure detection algorithm for surface EEG. Evaluation of the frequency and amplitude range was extended to allow detection of ictal activity of up to 25 Hz and amplitudes of up to 1 mV. To reduce the number of false detections a method to recognize loose contacts was implemented. For clinical validation, recordings of 11 patients that underwent depth electrode investigation in the Academic Centre of Epileptology, Kempenhaeghe were utilized. Recordings were evaluated manually by clinical neurophysiologists and seizures were annotated. For 10 patients the first 24 h of their depth electrode recording were analyzed, yielding in total 23 seizures detected for five of the patients studied. For one patient the complete recording was analyzed with a duration of 138 h, yielding 36 seizures. Manual seizure annotations were compared to computer detections for assessment of sensitivity and false detection rate. Results The automatic seizure detection algorithm found 84% of all seizures on average. All or more than 80% of the seizures were detected in the first 24 h of 10 patients. Analysis of the complete patient recording showed suppressed seizure onset activity of less than 2 s duration followed by paroxysmal fast activity. In this patient 14 out of the 36 seizures that evolved into a rhythmic ictal pattern were detected. The average false detection rate was 15 false detections in 24 h. Validation showed that most of the false positives either point to interictal activity or to background alpha activity. Conclusion We proposed a computer aided workflow for evaluation of depth electrode recordings. Our automatic seizure detection algorithm was able to detect either all or more than 7 seizures of a patient which allows deduction of important medical findings. The low false detection rate facilitates fast review of data. Our proposed approach showed that automatic evaluation of seizure activity in depth electrode recordings is feasible and will raise the overall efficiency of diagnostic.