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.

SUMMAR Y We investigated the occurrence of lapses of responsiveness (lapses) in 15 non-sleepdeprived subjects performing a 1D continuous tracking task during normal working hours. Tracking behaviour, facial video, and electroencephalogram (EEG) were recorded simultaneously during two 1-h sessions. Rate and duration were estimated for lapses identified by a tracking flat spot and/or video sleep. Fourteen of the 15 subjects had one or more lapses, with an overall rate of 39.3 ± 12.9 lapses per hour (mean ± SE) and a lapse duration of 3.4 ± 0.5 s. We also found that subjectsÕ performance improved towards the end of the 1-h long session, even though no external temporal cues were available. Spectral power was found to be higher during lapses in the delta, theta, and alpha bands, and lower in the beta, gamma, and higher bands, but correlations between changes in EEG power and lapses were low. In conclusion, lapses are a frequent phenomenon in normal subjects -even when not sleep-deprived -engaged in an extended monotonous continuous visuomotor task. This is of particular importance to the transport sector in which there is a need to maintain sustained attention for extended periods of time and in which lapses can lead to multiple-fatality accidents.

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An expert system for the automated detection of spikes and sharp waves in the EEG has been developed. The system consists of two distinct stages. The first is a feature extractor, written in the conventional procedural language Fortran, which uses parts of previously published spike-detection algorithms to produce a list of all spike-like occurrences in the EEG. The second stage, written in the production system language OPS5, reads the list and uses rules incorporating knowledge elicited from an electroencephalographer (EEGer) to confirm or exclude each of the possible spikes. Information such as the time of occurrence, polarity and channel relationship are used in this process. A summary of the detected epileptiform events is produced which is available to the EEGer in interpreting the EEG. The performance of the expert system is compared with an EEGer using a 320s segment from an EEG containing epileptiform activity. The system detected 19 events and missed seven (false negative) which the EEGer considered epileptiform. There were no false positive detections.

This research was designed to clarify the role of cortical modulation in the coordination of respiration and swallowing. Time-locked recordings of submental surface electromyography, nasal airflow, and thyroid acoustics were used to evaluate nonnutritive breathing-swallowing coordination (BSC) and swallowing apnea duration (SAD) of 20 healthy adults during 3 conditions. These conditions represented a continuum of volitional through nonvolitional swallowing control on the basis of a decreasing level of cortical activation: voluntarily initiated swallows during wakefulness, nonvolitional awake swallows, and reflexively initiated swallows during sleep. Differing proportions of swallows at the cusps between inspiration and expiration were found between the volitional and nonvolitional conditions, irrespective of the level of arousal. SAD was unaltered by condition. In conclusion, BSC is influenced by degree of volition but not by level of arousal. This implies that cortical influence on BSC is limited to conditions in which swallowing is voluntarily initiated and indirectly implicates the recruitment of the supplementary motor or insular cortices. SAD remained stable across conditions and may therefore be considered relatively impervious to suprabulbar influence.

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.