PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 7508368

Automatic EEG spike detection: what should the computer imitate?

W R Webber·B Litt·R P Lesser·R S Fisher·I Bankman
View original source ↗Export metadata ↓

Abstract

We conducted a study to explore how electroencephalographers (EEGers) read EEGs and reach clinical impressions based upon them. Eight EEGers and a rule-based computerized "spike" detector marked epileptiform discharges ("EDs") in 12 test records. Of all marked events, 18% were marked by all readers and 38% were marked by only one reader. Readers agreed on basic clinical features of the records, such as whether a record demonstrated EDs, the rank order of ED sources by location, and the ranking of test records in order of the number of EDs detected. Readers marked records in a consistent pattern that was independent of an objective measure of expertise and experience. Our computerized ED detector had lower sensitivity and selectivity than human readers, but either parameter could be adjusted to be comparable to human EEGers at the expense of the other. We propose that EEGers employ reproducible, quantitatively different styles of reading EEG tracings to reach qualitatively similar clinical impressions. In practice, EDs are not absolutely defined, but appear to represent a continuum of activity which lends itself better to description and rank ordering than to absolute quantitation. More than just counting EDs, a successful computerized ED detector should be adaptable to the style of individual readers in order to help them efficiently formulate their clinical impressions.

Explore related subjects

BrainElectroencephalographyHumansObserver VariationReference StandardsSignal Processing, Computer-AssistedJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

W R Webber, B Litt, R P Lesser, R S Fisher, I Bankman. 1993. Automatic EEG spike detection: what should the computer imitate?. https://doi.org/10.1016/0013-4694(93)90149-p

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

A MAGIBU-based model for pediatric and juvenile CNS tumors: an in-house epigenetic decision-support framework compared with online DNA methylation classifiers.

Background: DNA methylation profiling is a tool that provides key support for central nervous system (CNS) tumor classification. However, diagnostically ambiguous pediatric cases may result in discordant outputs across classifiers. We developed MAGIBU, a cross-platform, projection-based framework that embeds individual methylomes into a fixed CNS reference landscape, ranking diagnostic entities by local epigenetic proximity to support clinician-led integrative diagnosis. Methods: As a proof-of-concept, we evaluated MAGIBU in eight morphologically challenging pediatric/juvenile CNS tumors with unresolved diagnoses after institutional and central pathology review. To establish a benchmark in the absence of a definitive histopathological ground truth, a consensus epigenetic reference was defined a priori for cases showing concordant results between the Heidelberg CNS Tumor Methylation Classifier and Methylscape Analysis. Comparisons were also performed with Epigenomic Digital Pathology (EpiDiP). To validate MAGIBU beyond this discovery cohort, performance was assessed at the family level across the CNS methylation spectrum (n = 678, 28 methylation families), on non-array platforms (whole-genome bisulfite sequencing and Oxford Nanopore), and in a focused analysis of the low-grade glioma and diffuse midline glioma compartment across four independent cohorts (n = 670). Results: In the discovery cohort, MAGIBU achieved high concordance with the consensus reference (Cohen's κ = 0.855), outperforming EpiDiP (κ = 0.278), which frequently placed low-grade tumors in proximity to higher-grade reference regions. Conclusions: MAGIBU provides a stable, quantitative differential diagnosis framework that mitigates the limitations of rigid categorical assignments. By leveraging a distance-based proximity metric, it offers a transparent decision-support tool that integrates effectively with clinical, radiological, and molecular data. While performance is inherently dependent on reference atlas composition, MAGIBU represents a robust complementary approach for the diagnostic workup of ambiguous CNS tumors.

Brain↗
PubMed2021

1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans.

Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.

Brain↗