Real-time detection of epileptiform activity in the EEG: a blinded clinical trial.
The aim of this study was to determine the performance of a PC-based system for real-time detection and topographical mapping of epileptiform activity (EA) in the EEG during routine clinical recordings. The system incorporates a mimetic stage to locate candidate spikes (including sharp-waves) followed by two expert-system-based stages, which utilize spatial and wide-temporal contextual information in deciding whether candidate events are epileptiform or not. The data comprised 521 consecutive routine clinical EEG recordings (173 hours). Performance was evaluated by comparison with three independent electroencephalographers (EEGers-I). A second group of two EEGers (EEGers-II) separately interpreted the spike topographical maps and, for EEGs categorized as containing only questionable EA by the detection system, reviewed 6 sec segments of raw EEG centered on each questionable event. Thirty-eight of the EEGs were considered to contain definite EA by at least two of EEGers-I. The false detection rate of the system was 0.41 per hour. The system was found to have a sensitivity of 76% and a selectivity of 41% for EEGs containing definite EA. However, it only missed detection of EA in 5% of the recordings. EEGers-II agreed with EEGers-I on the distribution (generalized, lateralized, focal, multifocal) of EA in 79% of cases. This is by far the largest clinical evaluation of computerized spike detection reported in the literature and the only one to apply this in routine clinical recordings. The false detection rate is the lowest ever reported, suggesting that this multi-stage rule-based system is a powerful and practical tool in clinical electroencephalography and long-term EEG monitoring.
Black
M A
MA
Department of Medical Physics & Bioengineering, Christchurch Hospital, New Zealand.
Jones
R D
RD
Carroll
G J
GJ
Dingle
A A
AA
Donaldson
I M
IM
Parkin
P J
PJ
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016477:Artifacts; D001185:Artificial Intelligence; D001931:Brain Mapping / Q000295:instrumentation; D002540:Cerebral Cortex / Q000503:physiopathology; D002648:Child; D002675:Child, Preschool; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation; D004569:Electroencephalography / Q000295:instrumentation*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology; D005071:Evoked Potentials / Q000502:physiology; D005103:Expert Systems; D005260:Female; D006801:Humans; D008297:Male; D008838:Microcomputers; D008875:Middle Aged; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*