ABSTRACT
PURPOSE: Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a more extensive database than that of a previous study and its suitability for safety/efficacy closed-loop studies to block seizures in humans. METHODS: Up to eight electrocorticography (EcoG) channels from 15 subjects were analyzed off-line. Visual and computerized analyses of the data were performed by different (blinded) investigators. Independent visual analysis also was performed for clinical seizures and for detections identified only by the algorithm. The following were computed: FP rate, number of FNs, latency to automated detection, warning rate for clinical onset and warning times, seizure duration/intensity, and interrater agreement. Adaptations to improve performance were performed when indicated. RESULTS: Fourteen subjects met inclusion criteria. Generic algorithm "relative sensitivity" for clinical seizures was 100%; two undetected subclinical seizures and two unclassified seizures were captured after adaptation. FPs/day were zero in seven and fewer than one in an additional three subjects. Adaptations for four subjects with greater than 1 FP/day (7.7-66.6/day) reduced the rate to 0 in one subject and to fewer than five FP/day (1.7-4.2/day) in the remainder. Generic latency to automated detection was <5 s in eight of 13 subjects, and in 12 of 13 after adaptation. Detections provided warning of clinical onset in three of four subjects in whom it always followed electrographic onset, and in four of four after adaptation. Interrater agreement was low for FPs and EDs. CONCLUSIONS: The generic algorithm demonstrated high sensitivity, specificity, and speed, characteristics further enhanced by adaptation. This algorithm is well suited for seizure detection/warning and use in safety/efficacy closed-loop therapy studies.
Subject(s)
Algorithms , Diagnosis, Computer-Assisted , Electroencephalography , Epilepsies, Partial/diagnosis , Epilepsy, Complex Partial/diagnosis , Epilepsy, Tonic-Clonic/diagnosis , Signal Processing, Computer-Assisted , Adolescent , Adult , Cerebral Cortex/physiopathology , Electrodes, Implanted , Epilepsies, Partial/physiopathology , Epilepsy, Complex Partial/physiopathology , Epilepsy, Tonic-Clonic/physiopathology , Evoked Potentials/physiology , Female , Fourier Analysis , Humans , Male , Middle Aged , Monitoring, Ambulatory , Observer Variation , Reaction Time/physiology , Sensitivity and SpecificityABSTRACT
UNLABELLED: Published reports have indicated that after callosotomy half or more of all patients will experience a 50% or greater reduction in seizure frequency. Those callosotomy patients whose seizures produce falls appear to have the best results. We studied the value of ictal EEG in 41 patients 18 years or older who had undergone either a total or partial callosotomy at our program. Ictal EEG's were separated into two categories: Type I: generalized slow spike wave, electrodecrement, non-evolving low amplitude fast activity; Type II: all other patterns. Types I and II were then compared to a defined one-year outcome for the targeted seizure type using Chi-square or Fishers Exact Test. Previously identified predictors of good or worthwhile outcome as defined by the literature were also evaluated. RESULTS: A significant association was noted for presence of specifically defined EEG patterns and a 90% reduction in seizure frequency but not for other factors analyzed. CONCLUSION: The ictal EEG but not other factors is able to identify a group of patients who have a better than 90% chance for total or nearly total resolution of seizures causing sudden falls.
