Evidence map›Paper›PMID 35065325›Full record

ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2022

Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.

Daniel Ehrens, Mackenzie C Cervenka, Gregory K Bergey, Christophe C Jouny

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 38% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Daniel EhrensDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: dehrens@jhu.edu.
Mackenzie C CervenkaDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Gregory K BergeyDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Christophe C JounyDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Johns Hopkins Medicine · USJohns Hopkins University · US

Funding

Multimodal Detection of Early Partial Seizure OnsetsR01NS075020 · NINDS · JOHNS HOPKINS UNIVERSITY · PI JOUNY, CHRISTOPHE C · 2011 to 2014
$1.4M
NINDS NIH HHS R01 NS075020
6 · The paper itself

Abstract

objectiveTo develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity.

methodsOur algorithm was tested on intracranial EEG from epilepsy patients admitted to the EMU for presurgical evaluation. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels to classify the novelty of the current activity. In this study we compared multiple configurations using a one-class SVM to assess if there is significance over specific neural features or electrode locations.

resultsOur results show that the algorithm reaches a sensitivity of 87% for early-onset seizure detection and of 97.7% as a generic seizure detection.

conclusionsOur algorithm is capable of running in real-time and achieving a high performance for early seizure-onset detection with a low false positive rate and robustness in detection of different type of seizure-onset patterns. SIGNIFICANCE: This algorithm offers a solution to warning systems in the EMU as well as a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network.

Indexed as

Support Vector MachineAdultElectroencephalographyEpilepsyFemaleHumansMaleMiddle AgedSeizuresSensitivity and SpecificityDynamic learning algorithmEarly seizure-onset detectionNovelty classifierOne-class SVMReal-time seizure detection

Identifiers

PMID35065325
PMCPMC8857071
OpenAlexW4206693396

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.