A new way of building a database of EEG findings

Tagging EEG features within exam reports to quickly generate databases for research purposes

OBJECTIVE

assess the effectiveness of a new method for classifying EEG recording features through the use of tags within reports. We present feature prevalence in a sample of patients with toxic-metabolic encephalopathy and discuss the advantages of this approach over existing classification systems.

METHODS

during EEG report creation, tags reflecting background activity, epileptiform features and periodic discharges were selected according to the findings of each recording. Reports including the tags have been collected and processed by the EEG report parser script written in PHP language. The resulting spreadsheet was analysed to calculate the prevalence and type of EEG features in a sample group of patients with toxic-metabolic encephalopathy.

RESULTS

tag checking and extraction were very little time-consuming processes. Considering 5784 EEG recordings performed either in inpatients or outpatients over 2 years, toxic-metabolic aetiology was tagged in 218 (3.8 %). The most frequent background feature was severe slowing (5-6 Hz frequency), occurring in 79 (36.2 %). Epileptiform abnormalities were rare, reaching a maximum of 10 (4.6 %). Triphasic waves were tagged in 43 (19.7 %) recordings.

CONCLUSIONS

tagging and parsing processes are very fast and integrated into the daily routine. Sample analysis in patients with toxic-metabolic encephalopathies showed EEG slowing as the prevalent feature, while triphasic waves occurred in a minority of recordings. Existing software such as "SCORE" (Holberg EEG) requires the replacement of the currently used software for EEG reporting, minimizing additional costs and training. EEG Report Parser is free and open-source software, so it can be freely adopted, modified and redistributed, allowing further improvement and adaptability.

Development and Feasibility Testing of a Critical Care EEG Monitoring Database for Standardized Clinical Reporting and Multicenter Collaborative Research

PURPOSE

The rapid expansion of the use of continuous critical care electroencephalogram (cEEG) monitoring and resulting multicenter research studies through the Critical Care EEG Monitoring Research Consortium has created the need for a collaborative data sharing mechanism and repository. The authors describe the development of a research database incorporating the American Clinical Neurophysiology Society standardized terminology for critical care EEG monitoring. The database includes flexible report generation tools that allow for daily clinical use.

METHODS

Key clinical and research variables were incorporated into a Microsoft Access database. To assess its utility for multicenter research data collection, the authors performed a 21-center feasibility study in which each center entered data from 12 consecutive intensive care unit monitoring patients. To assess its utility as a clinical report generating tool, three large volume centers used it to generate daily clinical critical care EEG reports.

RESULTS

A total of 280 subjects were enrolled in the multicenter feasibility study. The duration of recording (median, 25.5 hours) varied significantly between the centers. The incidence of seizure (17.6%), periodic/rhythmic discharges (35.7%), and interictal epileptiform discharges (11.8%) was similar to previous studies. The database was used as a clinical reporting tool by 3 centers that entered a total of 3,144 unique patients covering 6,665 recording days.

CONCLUSIONS

The Critical Care EEG Monitoring Research Consortium database has been successfully developed and implemented with a dual role as a collaborative research platform and a clinical reporting tool. It is now available for public download to be used as a clinical data repository and report generating tool.

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.

Computer-assisted interpretation of the EEG background pattern: a clinical evaluation

Objective

Interpretation of the EEG background pattern in routine recordings is an important part of clinical reviews. We evaluated the feasibility of an automated analysis system to assist reviewers with evaluation of the general properties in the EEG background pattern.

Methods

Quantitative EEG methods were used to describe the following five background properties: posterior dominant rhythm frequency and reactivity, anterior-posterior gradients, presence of diffuse slow-wave activity and asymmetry. Software running the quantitative methods were given to ten experienced electroencephalographers together with 45 routine EEG recordings and computer-generated reports. Participants were asked to review the EEGs by visual analysis first, and afterwards to compare their findings with the generated reports and correct mistakes made by the system. Corrected reports were returned for comparison.

Results

Using a gold-standard derived from the consensus of reviewers, inter-rater agreement was calculated for all reviewers and for automated interpretation. Automated interpretation together with most participants showed high (kappa > 0.6) agreement with the gold standard. In some cases, automated analysis showed higher agreement with the gold standard than participants. When asked in a questionnaire after the study, all participants considered computer-assisted interpretation to be useful for every day use in routine reviews.

Conclusions

Automated interpretation methods proved to be accurate and were considered to be useful by all participants.

Significance

Computer-assisted interpretation of the EEG background pattern can bring consistency to reviewing and improve efficiency and inter-rater agreement.

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.

Characteristics of generalised epileptiform activity

OBJECTIVE

To study the age-related occurrence of specific features of generalised epileptiform activity (GEA), their correlation with EEG background activity (BA), and their internal correlation.

METHODS

17,723 consecutive routine EEGs from 12,511 patients were annotated and categorised into a database. The first EEG containing GEA from all 325 patients with such activity were selected and categorised for GEA features. The BA was studied in multivariable fractional polynomial regression models including intervening variables. The GEA features were studied in similar models for age-dependency and internal correlation.

RESULTS

High GEA-amplitude and low GEA-frequency correlated with BA slowing. The occurrence of 'irregular spike/sharp slow wave' pattern increased with age (p=0.003). Hyperventilation sensitivity was not age-related. There was no correlation between hyperventilation sensitivity and photoparoxysmal response. The age-related probability for specific GEA-types was established.

CONCLUSIONS

High GEA-amplitude and low GEA-frequency correlate with BA slowing, indicating cerebral cortical dysfunction. Hyperventilation sensitivity and photoparoxysmal response independently increase the yield of EEG. There is no age-dependency for hyperventilation sensitivity showing that an upper age threshold for hyperventilation provocation is inappropriate.

SIGNIFICANCE

The results extend our understanding of GEA and help the electroencephalographer in weighing the various GEA components.

Focal epileptiform activity described by a large computerised EEG database

OBJECTIVE

To study the age-related topographical tendency of expressing epileptiform activity, and the effect of focal epileptiform activity (FEA) on the general cortical brain activity.

METHODS

1647 consecutive routine EEGs containing FEA were visually assessed for FEA location and asymmetry. Background activity was compared with that in normal EEGs from 3268 drug-free outpatient controls.

RESULTS

FEA localisation was age-related (p<0.0005) except for the temporal region (p=0.22) where FEA was found equally often in the young and the old. The left hemisphere was more prone to FEA (p=0.018). The left-right asymmetry varied by age (p=0.013). FEA asymmetry occurred most frequently in EEGs from patients older than 80 years, and least frequent in the age-group 20-39 years. FEA was associated with lower alpha rhythm (AR) frequencies (p=0.0041) and higher AR amplitudes (p=0.0023), as well as higher general background activity (GBA) amplitude (p<0.0005), while GBA frequencies were the same (p=0.96).

CONCLUSIONS

Topographical localisation of FEA was age-dependent. There was an overall left dominance, but the side asymmetry was modest and varied by age. FEA was associated with changes in AR and GBA.

SIGNIFICANCE

The results demonstrate that FEA is associated with cerebral cortical dysfunction also distant from the epileptic focus.

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.