Mark L. Scheuer

The ACNS subcommittee on research terminology for continuous EEG monitoring: proposed standardized terminology for rhythmic and periodic EEG patterns encountered in critically ill patients.

Continuous EEG monitoring is becoming a commonly usedtool in the assessment of brain function in critically illpatients. However, there is no uniformly accepted nomencla-ture for the EEG patterns frequently encountered in thesepatients, such as periodic discharges, fluctuating rhythmicpatterns, and combinations thereof. Similarly, there is noconsensus regarding which patterns are associated with on-going neuronal injury, which needs to be treated, or howaggressively to treat them. The first step in addressing theseissues is to standardize terminology to allow multicenterresearch projects and to facilitate communication. To thisend, we gathered a group of electroencephalographers withparticular expertise or interest in this area to develop stan-dardized terminology to be used primarily in the researchsetting. One of the main goals was to eliminate terms withclinical connotations, intended or not, such as “triphasicwaves,” a term that implies a metabolic encephalopathy withno relationship to seizures. We also decided to avoid the useof “ictal,” “interictal,” and “epileptiform” for the equivocalpatterns that are the primary focus of this report.A standardized method of quantifying interictal dis-charges is also included for the same reasons, with no attemptto alter the existing definition of epileptiform discharges(sharpwavesandspikes Noachtaretal.,1999 .Similarly,weare not necessarily suggesting abandonment of prior termssuch as periodic lateralized epileptiform discharges (PLEDs)and triphasic waves for clinical use.This is a proposal subject to future modifications basedon use and feedback from others.

Continuous EEG Monitoring in the Intensive Care Unit

Summary: Continuous EEG (CEEG) monitoring allows uninterrupted assessment of cerebral cortical activity with good spatial resolution and excellent temporal resolution. Thus, this procedure provides a means of constantly assessing brain function in critically ill obtunded and comatose patients. Recent advances in digital EEG acquisition, storage, quantitative analysis, and transmission have made CEEG monitoring in the intensive care unit (ICU) technically feasible and useful. This article summarizes the indications and methodology of CEEG monitoring in the ICU, and discusses the role of some quantitative EEG analysis techniques in near real‐time remote observation of CEEG recordings. Clinical examples of CEEG use, including monitoring of status epilepticus, assessment of ongoing therapy for treatment of seizures in critically ill pateints, and monitoring for cerebral ischemia, are presented. Areas requiring further development of CEEG monitoring techniques and indications are discussed.

Noninvasive continuous monitoring of cerebral oxygenation periictally using near-infrared spectroscopy: a preliminary report.

Summary: Purpose: To report on the use of near‐infrared spectroscopy (NIRS) to examine the changes in cerebral oxygenation in the periictal period in patients with seizures.

Spike detection II: automatic, perception-based detection and clustering.

OBJECTIVES We developed perception-based spike detection and clustering algorithms. METHODS The detection algorithm employs a novel, multiple monotonic neural network (MMNN). It is tested on two short-duration EEG databases containing 2400 spikes from 50 epilepsy patients and 10 control subjects. Previous studies are compared for database difficulty and reliability and algorithm accuracy. Automatic grouping of spikes via hierarchical clustering (using topology and morphology) is visually compared with hand marked grouping on a single record. RESULTS The MMNN algorithm is found to operate close to the ability of a human expert while alleviating problems related to overtraining. The hierarchical and hand marked spike groupings are found to be strikingly similar. CONCLUSIONS An automatic detection algorithm need not be as accurate as a human expert to be clinically useful. A user interface that allows the neurologist to quickly delete artifacts and determine whether there are multiple spike generators is sufficient.

Seizure detection: evaluation of the Reveal algorithm

OBJECTIVE The aim of this study is to evaluate an improved seizure detection algorithm and to compare with two other algorithms and human experts. METHODS 672 seizures from 426 epilepsy patients were examined with the (new) Reveal algorithm which utilizes 3 methods, novel in their application to seizure detection: Matching Pursuit, small neural network-rules and a new connected-object hierarchical clustering algorithm. RESULTS Reveal had a sensitivity of 76% with a false positive rate of 0.11/h. Two other algorithms (Sensa and CNet) were tested and had sensitivities of 35.4 and 48.2% and false positive rates of 0.11/h and 0.75/h, respectively. CONCLUSIONS This study validates the Reveal algorithm, and shows it to compare favorably with other methods. SIGNIFICANCE Improved seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit.

Seizure detection: correlation of human experts

OBJECTIVE The description and application of a new, overlap-integral comparison method and the quantification of human vs. human accuracies that can be used as goals for algorithms. METHODS Four human experts marked ten 8 h electroencephalography (EEG) records from seizure patients. The seizures varied in origin and type, including complex partial, generalized absence, secondarily generalized and primary generalized tonic-clonic. The traditional any-overlap comparison method is used in addition to the overlap-integral method, which is sensitive to the correct placement of the seizure endpoints. RESULTS The number of events marked by each reader ranged from 57 to 77. The average any-overlap sensitivity and false positives per hour rate are 0.92 and 0.117. The average overlap-integral correlation, sensitivity and specificity are 0.80, 0.82 and 0.9926. As expected, the correspondence between readers is high, but confounding issues resulted in overlap-integral sensitivities less than 0.5 for 10% of the records. Seven percent of the any-overlap sensitivities are less than 0.5. A comparison of the methods by record shows that the overlap-integral specificity and the any-overlap false positive rate measure different features. CONCLUSIONS There was little variation between readers and they were essentially interchangeable. High seizure rate (many per hour), short seizure durations (<10 s) and long seizure durations (approximately 10 min) with ambiguous offsets can complicate the analysis and result in poor correlation. There may be any number of unmarked events in rigorously marked records and it may be preferable to use records from non-epilepsy patients to compute the false positive rate. The any-overlap and overlap-integral comparison methods are complementary. SIGNIFICANCE Correlation between expert human readers can be low on some records, which will complicate testing of seizure detection algorithms.

Planned ictal FDG PET imaging for localization of extratemporal epileptic foci

Summary: Purpose: This work demonstrates the feasibility of planned ictal positron emission tomography (PET) with [18F]fluoro‐2‐deoxy‐glucose (FDG) for localization of epileptic activity in patients with frequent partial seizures of extratem‐poral origin.

Evidence for Distinct Genetic Influences on Generalized and Localization-related Epilepsy

Summary:  Purpose: Determining the existence of syndrome‐specific genetic factors in epilepsy is essential for phenotype definition in genetic linkage studies, and informs research on basic mechanisms. Analysis of concordance of epilepsy syndromes in families has been used to assess shared versus distinct genetic influences on generalized epilepsy (GE) and localization‐related epilepsy (LRE). However, it is unclear how the results should be interpreted in relation to specific genetic hypotheses.

Decomposition of biomedical signals for enhancement of their time–frequency distributions ☆

Abstract Bilinear time–frequency distributions have been widely utilized in the analysis of nonstationary biomedical signals. A problem often arises where the time–frequency components with small-amplitude values cannot be displayed clearly. This problem results from a masking effect on these components caused by the presence of high-energy slow waves and sharp patterns in the input which produce large values in the time–frequency distribution. These large values often appear in the time–frequency plane as irregular patterns in the low-frequency range (due to slow waves), and as wide-band, impulsive components at certain points in time (due to sharp patterns). In this work we present an effective signal pre-processing method using a nonlinear operation on wavelet coefficients. This method equalizes the energy of different time–frequency components in the data so that the masking effect is greatly reduced, while the original time–frequency features of the input signal are preserved. Comparative experiments on electroencephalographic data with and without using this method have shown a clear improvement in the readability and sensitivity in bilinear time–frequency distributions.

Seizure detection with automated EEG analysis: A validation study focusing on periodic patterns

OBJECTIVE To evaluate an automated seizure detection (ASD) algorithm in EEGs with periodic and other challenging patterns. METHODS Selected EEGs recorded in patients over 1year old were classified into four groups: A. Periodic lateralized epileptiform discharges (PLEDs) with intermixed electrical seizures. B. PLEDs without seizures. C. Electrical seizures and no PLEDs. D. No PLEDs or seizures. Recordings were analyzed by the Persyst P12 software, and compared to the raw EEG, interpreted by two experienced neurophysiologists; Positive percent agreement (PPA) and false-positive rates/hour (FPR) were calculated. RESULTS We assessed 98 recordings (Group A=21 patients; B=29, C=17, D=31). Total duration was 82.7h (median: 1h); containing 268 seizures. The software detected 204 (=76.1%) seizures; all ictal events were captured in 29/38 (76.3%) patients; in only in 3 (7.7%) no seizures were detected. Median PPA was 100% (range 0-100; interquartile range 50-100), and the median FPR 0/h (range 0-75.8; interquartile range 0-4.5); however, lower performances were seen in the groups containing periodic discharges. CONCLUSION This analysis provides data regarding the yield of the ASD in a particularly difficult subset of EEG recordings, showing that periodic discharges may bias the results. SIGNIFICANCE Ongoing refinements in this technique might enhance its utility and lead to a more extensive application.