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.

A multistage system to detect epileptiform activity in the EEG.

A PC-based system has been developed to automatically detect epileptiform activity in sixteen-channel bipolar EEG's. The system consists of three stages: data collection, feature extraction, and event detection. The feature extractor employs a mimetic approach to detect candidate epileptiform transients on individual channels, while an expert system is used to detect focal and nonfocal multichannel epileptiform events. Considerable use of spatial and temporal contextual information present in the EEG aids both in the detection of epileptiform events and in the rejection of artifacts and background activity as events. Classification of events as definite or probable overcomes, to some extent, the problem of maintaining high detection rates while eliminating false detections. So far, the system has only been evaluated on development data but, although this does not provide a true measure of performance, the results are nevertheless impressive. Data from 11 patients, totaling 180 minutes of sixteen-channel bipolar EEG's, have been analyzed. A total of 45-71% (average 58%) of epileptiform events reported by the human expert in any EEG were detected as definite with no false detections (i.e., 100% selectivity) and 60-100% (average 80%) as either definite or probable but at the expense of up to nine false detections per hour. Importantly, the highest detection rates were achieved on EEG's containing little epileptiform activity and no false detections were made on normal EEG's.