H. Aurlien

Unified EEG terminology and criteria for nonconvulsive status epilepticus

The diagnosis of nonconvulsive status epilepticus (NCSE) relies largely on electroencephalography (EEG) findings. The lack of a unified EEG terminology, and of evidence‐based EEG criteria, leads to varying criteria for and ability to diagnose NCSE. We propose a unified terminology and classification system for NCSE, using, as a template, the Standardised Computer‐based Organised Reporting of EEG (SCORE). This approach integrates the terminology recently proposed for the rhythmic and periodic patterns in critically ill patients, the electroclinical classification of NCSE (type of NCSE) and the context for the pathologic conditions and age‐related epilepsy syndromes. We propose flexible EEG criteria that employ the SCORE system to assemble a database for determining evidence‐based EEG criteria for NCSE.

EEG background activity described by a large computerized database

OBJECTIVE To show how our newly developed software for classification and storage of visually routinely assessed EEGs are used to evaluate the general background activity (GBA) and the alpha rhythm (AR) in a large number of prospective EEGs. METHODS EEGs from 4651 consecutive patients were visually assessed using a computerized description system connected to an EEG database. The AR and the GBA apart from the AR were described separately for frequency and amplitude. RESULTS AR frequencies declined from the age of 45 years and slowed with increasing age independently of non-AR pathology and gender. Females had higher AR frequencies than males. EEGs with non-GBA pathology had lower GBA frequencies and higher GBA amplitudes. Higher GBA amplitudes were associated with lower GBA frequencies in normal EEGs for all age groups. EEG interpretations by 4 independent electroencephalographers showed the same trends, but differed in exact assessment of frequencies and amplitudes. CONCLUSIONS EEG interpretations stored in a categorized database with easy access to data have successfully been used to evaluate interobserver variation and other quality control measurements. Statistical analysis of the data has at the same time produced new information regarding the development of AR and GBA throughout life.

Standardized computer-based organized reporting of EEG: SCORE.

The electroencephalography (EEG) signal has a high complexity, and the process of extracting clinically relevant features is achieved by visual analysis of the recordings. The interobserver agreement in EEG interpretation is only moderate. This is partly due to the method of reporting the findings in free‐text format. The purpose of our endeavor was to create a computer‐based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video‐EEG recordings). A working group of EEG experts took part in consensus workshops in Dianalund, Denmark, in 2010 and 2011. The faculty was approved by the Commission on European Affairs of the International League Against Epilepsy (ILAE). The working group produced a consensus proposal that went through a pan‐European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer‐based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, “episodes” (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization. For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer‐assisted analysis into the report, it will make possible the build‐up of a multinational database, and it will help in training young neurophysiologists.

A new way of building a database of EEG findings

Whereas computer-based electroencephalography (EEG) is widely applied, the EEG interpretations are usually not stored in a way that favours exploitation of modern computer technology. This paper reports an EEG description system facilitating categorization of EEG data in a computerized database. The system interactively communicates with the digital EEG system and also with the general patient administrative system. The main new quality of this system is the methods for data input and automatic data retrieval from several systems, rather than the establishment of a database of EEG data itself. The EEGs are visually analysed and categorized. Manually marked EEG events are automatically transferred to the database and such events as well as defined electrode positions within these epochs are directly linked to their corresponding descriptions. The database is updated without demand for filling in the events in the database in a second operation. Thereby, the EEG interpreter builds the database while analysing the EEG. This system provides an improved accessibility of EEG data for clinical, normative, educational and scientific use.

EEG Spectral Features Discriminate between Alzheimer's and Vascular Dementia.

Alzheimer’s disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n = 77) and VaD (n = 77) and healthy elderly normal controls (NC) (n = 77). We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria.

Standardized computer-based organized reporting of EEG: SCORE – Second version

Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted in the second, revised version of SCORE (Standardized Computer-based Organized Reporting of EEG), which is presented in this paper. The revised terminology was implemented in a software package (SCORE EEG), which was tested in clinical practice on 12,160 EEG recordings. Standardized terms implemented in SCORE are used to report the features of clinical relevance, extracted while assessing the EEGs. Selection of the terms is context sensitive: initial choices determine the subsequently presented sets of additional choices. This process automatically generates a report and feeds these features into a database. In the end, the diagnostic significance is scored, using a standardized list of terms. SCORE has specific modules for scoring seizures (including seizure semiology and ictal EEG patterns), neonatal recordings (including features specific for this age group), and for Critical Care EEG Terminology. SCORE is a useful clinical tool, with potential impact on clinical care, quality assurance, data-sharing, research and education.

Regularized linear discriminant analysis of EEG features in dementia patients

The present study explores if EEG spectral parameters can discriminate between healthy elderly controls (HC), Alzheimer’s disease (AD) and vascular dementia (VaD) using. We considered EEG data recorded during normal clinical routine with 114 healthy controls (HC), 114 AD, and 114 VaD patients. The spectral features extracted from the EEG were the absolute delta power, decay from lower to higher frequencies, amplitude, center and dispersion of the alpha power and baseline power of the entire frequency spectrum. For discrimination, we submitted these EEG features to regularized linear discriminant analysis algorithm with a 10-fold cross-validation. To check the consistency of the results obtained by our classifiers, we applied bootstrap statistics. Four binary classifiers were used to discriminate HC from AD, HC from VaD, AD from VaD, and HC from dementia patients (AD or VaD). For each model, we measured the discrimination performance using the area under curve (AUC) and the accuracy of the cross-validation (cv-ACC). We applied this procedure using two different sets of predictors. The first set considered all the features extracted from the 22 channels. For the second set of features, we automatically rejected features poorly correlated with their labels. Fairly good results were obtained when discriminating HC from dementia patients with AD or VaD (AUC = 0.84). We also obtained AUC = 0.74 for discrimination of AD from HC, AUC = 0.77 for discrimination of VaD from HC, and finally AUC = 0.61 for discrimination of AD from VaD. Our models were able to separate HC from dementia patients, and also and to discriminate AD from VaD above chance. Our results suggest that these features may be relevant for the clinical assessment of patients with dementia.

The new ILAE seizure classification: 63 seizure types?

32 children with encephalopathy with status epilepticus during sleep, or ESES syndrome. Epilepsia 2010;51:2023–2032. 7. Kramer U, Sagi L, Goldberg-Stern H, et al. Clinical spectrum and medical treatment of children with electrical status epilepticus in sleep (ESES). Epilepsia 2009;50:1517–1524. 8. Lawrence A, Nicholls SK, Stansfield SH, et al. Characterization of the tail-specific protease (Tsp) from Legionella. J Gen Appl Microbiol 2014;60:95–100.

Standardisierter computer-basiert-organisierter Report des EEG

ZusammenfassungEs ist allgemein üblich, EEG-Befunde als freien Text zu schreiben. Dies ist mit dem Nachteil einer erheblichen Interobserver-Variabilität verbunden. Deshalb wurde von einer Expertengruppe, bestehend aus Neurophysiologen und Neurologen, im Rahmen einer europäischen Konsenserklärung ein Standard der EEG-Befundung publiziert: das „standardised computer-based organised reporting of the EEG“ (SCORE). Der EEG-Auswerter kann mit diesem Protokoll EEG beschreiben und befunden, indem er definierte Begriffe auswählt, die in thematischen Zusammenhängen präsentiert sind. Nach Dateneingabe wird der Befund automatisch erstellt. Das Programm SCORE ist zur EEG-Auswertung von Patienten sowohl im Kindes- als auch im Erwachsenenalter geeignet. Da das Neugeborenen-EEG eine andere Herangehensweise der Befundung erfordert, wurde für diese Altersgruppe eine spezifische Maske entwickelt. Es gibt inzwischen eine fertige frei verfügbare deutsche Version. Das Programm SCORE ermöglicht eine den klinischen Belangen angepasste Standardisierung der EEG-Befundung, kann im Rahmen der EEG-Ausbildung junger Neurologen sowie Neuropädiater eingesetzt werden und kann als ubiquitär verfügbare Datenbank außerdem die Basis für eine multinationale Zusammenarbeit in klinischer Forschung darstellen.AbstractIt is common practice to assess electroencephalographic (EEG) findings by visual analysis followed by report writing in free text format. This has the disadvantages of a moderate agreement in EEG interpretation at best and considerable interobserver variability. A working group of EEG experts consisting of neurologists and neurophysiologists produced a consensus proposal for the standardized reporting of EEGs: the standardized computer-based organized reporting of EEG (SCORE) system. For each EEG finding relevant features are scored using predefined terms and at the end a report is produced automatically. The SCORE system can be used for reporting EEGs from patients of any age, from preterm to adult EEG recordings. As neonatal EEGs require a unique approach, a special template was developed for this age group. A German translation is now freely available. The SCORE system can potentially improve the quality of EEG assessment and reporting, will help incorporate the results of computer-assisted analysis into a report, enables the creation of a multinational database and will help in training young neurophysiologists.

Prevalence of Parasomnias in Patients With Obstructive Sleep Apnea. A Registry-Based Cross-Sectional Study

Objective: To assess the prevalence of parasomnias in relation to presence and severity of obstructive sleep apnea (OSA). We hypothesized higher parasomnia prevalence with higher OSA severity. Methods: The sample comprised 4,372 patients referred to a Norwegian university hospital with suspicion of OSA (mean age 49.1 years, 69.8% males). OSA was diagnosed and categorized by standard respiratory polygraphy (type 3 portable monitor). The patients completed a comprehensive questionnaire prior to the sleep study, including questions about different parasomnias during the last 3 months. Pearson chi-square tests explored differences according to the presence and severity of OSA. Furthermore, logistic regression analyses with the parasomnias as dependent variables and OSA severity as predictor were conducted (adjusted for sex, age, marital status, smoking, and alcohol consumption). Results: In all, 34.7% had apnea-hypopnea index (AHI) <5 (no OSA), 32.5% had AHI 5-14.9 (mild OSA), 17.4% had AHI 15-29.9 (moderate OSA), and 15.3% had AHI ≥30 (severe OSA). The overall prevalence of parasomnias was 3.3% (sleepwalking), 2.5% (sleep-related violence), 3.1% (sexual acts during sleep), 1.7% (sleep-related eating), and 43.8% (nightmares). The overall parasomnia prevalence was highest in the no OSA group. In the chi-square analyses, including all OSA groups, the prevalence of sleep-related violence and nightmares were inversely associated with OSA severity, whereas none of the other parasomnias were significantly associated with OSA severity. In adjusted logistic regression analyses the odds of sleepwalking was significantly higher in severe compared to mild OSA (OR = 2.0, 95% CI = 1.12–3.55). The other parasomnias, including sleep-related violence and nightmares, were not associated with OSA presence or severity when adjusting for sex and age. Conclusions: We found no increase in parasomnias in patients with OSA compared to those not having OSA. With the exception of sleepwalking, the parasomnias were not associated with OSA severity.