Grant J. Carroll

A multistage system to detect epileptiform activity in the EEG

A PC-based system has been developed to automatically detect epileptiform activity in 16-channel bipolar EEGs. The system consists of 3 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 16-channel bipolar EEGs, 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 9 false detections per hour. Importantly, the highest detection rates were achieved on EEGs containing little epileptiform activity and no false detections were made on normal EEGs.<<ETX>>

Detection of epileptiform discharges in the EEG by a hybrid system comprising mimetic, self-organized artificial neural network, and fuzzy logic stages

OBJECTIVE A multi-stage system for automated detection of epileptiform activity in the EEG has been developed and tested on pre-recorded data from 43 patients. METHODS The system is centred on the use of an artificial neural network, known as the self-organising feature map (SOFM), as a novel pattern classifier. The role of the SOFM is to assign a probability value to incoming candidate epileptiform discharges (on a single channel basis). The multi-stage detection system consists of three major stages: mimetic, SOFM, and fuzzy logic. Fuzzy logic is introduced in order to incorporate spatial contextual information in the detection process. Through fuzzy logic it has been possible to develop an approximate model of the spatial reasoning performed by the electroencephalographer. RESULTS The system was trained on 35 epileptiform EEGs containing over 3000 epileptiform events and tested on a different set of eight EEGs containing 190 epileptiform events (including one normal EEG). Results show that the system has a sensitivity of 55.3% and a selectivity of 82% with a false detection rate of just over seven per hour. CONCLUSIONS Based on these initial results the overall performance is favourable when compared with other leading systems in the literature. This encourages us to further test the system on a larger population base with the ultimate aim of introducing it into routine clinical use.

Comparison of median and radial nerve sensory latencies in the electrophysiological diagnosis of carpal tunnel syndrome

An electrophysiological diagnosis of carpal tunnel syndrome (CTS) was made on the basis of the median sensory nerve action potential (SNAP) alone in 79 of 161 (49.1%) symptomatic hands without electrophysiological evidence of a generalised peripheral neuropathy. Comparison of distal sensory latencies (DSLs) for the median and radial nerves yielded abnormal results in 17 of the remaining hands with normal median nerve DSLs, increasing the electrodiagnostic yield to 59.6%. Carpal tunnel decompression has been performed in seven of these hands, with abnormal intraoperative findings reported in two, while all improved clinically following surgery, substantiating the diagnosis of CTS. Although the technique described here would not appear to increase the electrodiagnostic yield more than comparison of DSLs for the median and ulnar nerves, which has been reported previously, it remains an effective, quick and simple procedure for increasing the sensitivity of the nerve conduction studies.

Frequent lapses of responsiveness during an extended visuomotor tracking task in non-sleep-deprived subjects

We investigated the occurrence of lapses of responsiveness (lapses) in 15 non‐sleep‐deprived 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 subjects’ 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.

Expert system approach to detection of epileptiform activity in the EEG

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 thedetected 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.

Multireference adaptive noise canceling applied to the EEG

The technique of multireference adaptive noise canceling (MRANC) is applied to enhance transient nonstationarities in the electroencephalogram (EEG), with the adaptation implemented by means of a multilayer perceptron artificial neural network (ANN). The method was applied to recorded EEG segments and the performance on documented nonstationarities recorded. The results show that the neural network (nonlinear) gives an improvement in performance (i.e., signal-to-noise ratio (SNR) of the nonstationarities) compared to a linear implementation of MRANC. In both cases an improvement in the SNR was obtained. The advantage of the spatial filtering aspect of MRANC is highlighted when the performance of MRANC is compared to that of the inverse auto-regressive filtering of the EEG, a purely temporal filter.

Detection of focal epileptiform events in the EEG by spatio-temporal dipole clustering

OBJECTIVE Methods for the detection of epileptiform events can be broadly divided into two main categories: temporal detection methods that exploit the EEGs temporal characteristics, and spatial detection methods that base detection on the results of an implicit or explicit source analysis. We describe how the framework of a spatial detection method was extended to improve its performance by including temporal information. This results in a method that provides (i) automated localization of an epileptogenic focus and (ii) detection of focal epileptiform events in an EEG recording. For the detection, only one threshold value needs to be set. METHODS The method comprises five consecutive steps: (1) dipole source analysis in a moving window, (2) automatic selection of focal brain activity, (3) dipole clustering to arrive at the identification of the epileptiform cluster, (4) derivation of a spatio-temporal template of the epileptiform activity, and (5) template matching. Routine EEG recordings from eight paediatric patients with focal epilepsy were labelled independently by two experts. The method was evaluated in terms of (i) ability to identify the epileptic focus, (ii) validity of the derived template, and (iii) detection performance. The clustering performance was evaluated using a leave-one-out cross validation. Detection performance was evaluated using Precision-Recall curves and compared to the performance of two temporal (mimetic and wavelet based) and one spatial (dipole analysis based) detection methods. RESULTS The method succeeded in identifying the epileptogenic focus in seven of the eight recordings. For these recordings, the mean distance between the epileptic focus estimated by the method and the region indicated by the labelling of the experts was 8mm. Except for two EEG recordings where the dipole clustering step failed, the derived template corresponded to the epileptiform activity marked by the experts. Over the eight EEGs, the method showed a mean sensitivity and selectivity of 92 and 77%, respectively. CONCLUSIONS The method allows automated localization of the epileptogenic focus and shows good agreement with the region indicated by the labelling of the experts. If the dipole clustering step is successful, the method allows a detection of the focal epileptiform events, and gave a detection performance comparable or better to that of the other methods. SIGNIFICANCE The identification and quantification of epileptiform events is of considerable importance in the diagnosis of epilepsy. Our method allows the automatic identification of the epileptic focus, which is of value in epilepsy surgery. The method can also be used as an offline exploration tool for focal EEG activity, displaying the dipole clusters and corresponding time series.

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.

Slow-wave activity arising from the same area as epileptiform activity in the EEG of paediatric patients with focal epilepsy

OBJECTIVE The aim of this study was to investigate the presence and characteristics of apparent non-epileptiform activity arising in the same brain area as epileptiform activity in the EEG of paediatric patients with focal epilepsy. METHODS The EEG from eight patients was analysed by an automated method which detects epochs with a single underlying source having a dipolar potential distribution. The EEG with the highlighted detections was then rated by a clinical neurophysiologist (EEGer) with respect to epileptiform activity. RESULTS Although EEGer-marked events and computer detections often coincided, in five out of the eight patients, a substantial number of other detections were found to arise from the same area as the marked events. The morphology of a high proportion of these other detections did not resemble typical epileptiform activity and had a frequency content mainly in the delta and theta ranges. CONCLUSIONS This is, to our knowledge, the first study to use an automated technique to demonstrate the presence of non-epileptiform activity arising from the same area as the epileptiform activity in the EEG of paediatric patients with focal epilepsy. This slow wave activity is likely to be related to the underlying epileptogenic process. SIGNIFICANCE This paper suggests a technique for automated detection of focal activity arising from epileptogenic foci. It also provides a new perspective on extracting clinical useful information from slow-wave background EEG activity.

Lapses of responsiveness: Characteristics, detection, and underlying mechanisms

Lapses in responsiveness (‘lapses’), particularly microsleeps and attention lapses, are complete disruptions in performance from ∼0.5–15 s. They are of particular importance in the transport sector in which there is a need to maintain sustained attention for extended periods and in which lapses can lead to multiple-fatality accidents.