Evidence map›Paper›PMID 38321561›Full record

ArticleBioelectronic medicine2024

Using a standalone ear-EEG device for focal-onset seizure detection.

McGregor Joyner, Sheng-Hsiou Hsu, Stephanie Martin, Jennifer Dwyer, Denise Fay Chen, Reza Sameni, Samuel H Waters, Konstantin Borodin, Gari D Clifford, Allan I Levey and 3 more

Abstract read
In one paragraph

Article in Bioelectronic medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
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

18 citing papers in PubMed.

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  11. Seizure Detection Devices.Journal of clinical medicine · 2025
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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

13 authors.

McGregor Joyner *NextSense Inc., Mountain View, CA, USA.
Sheng-Hsiou Hsu *NextSense Inc., Mountain View, CA, USA.
Stephanie Martin *NextSense Inc., Mountain View, CA, USA.
Jennifer Dwyer *NextSense Inc., Mountain View, CA, USA.
Denise Fay ChenDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
Reza SameniDepartment of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA.
Samuel H WatersDepartment of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Konstantin BorodinNextSense Inc., Mountain View, CA, USA.
Gari D CliffordDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
Allan I LeveyDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
John Hixson *Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Daniel Winkel *Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
Jonathan Berent *NextSense Inc., Mountain View, CA, USA. jb@nextsense.io.ORCID http://orcid.org/0000-0003-4897-3066

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeizure detection is challenging outside the clinical environment due to the lack of comfortable, reliable, and practical long-term neurophysiological monitoring devices. We developed a novel, discreet, unobstructive in-ear sensing system that enables long-term electroencephalography (EEG) recording. This is the first study we are aware of that systematically compares the seizure detection utility of in-ear EEG with that of simultaneously recorded intracranial EEG. In addition, we present a similar comparison between simultaneously recorded in-ear EEG and scalp EEG.

methodsIn this foundational research, we conducted a clinical feasibility study and validated the ability of the ear-EEG system to capture focal-onset seizures against 1255 hrs of simultaneous ear-EEG data along with scalp or intracranial EEG in 20 patients with refractory focal epilepsy (11 with scalp EEG, 8 with intracranial EEG, and 1 with both).

resultsIn a blinded, independent review of the ear-EEG signals, two epileptologists were able to detect 86.4% of the seizures that were subsequently identified using the clinical gold standard EEG modalities, with a false detection rate of 0.1 per day, well below what has been reported for ambulatory monitoring. The few seizures not detected on the ear-EEG signals emanated from deep within the mesial temporal lobe or extra-temporally and remained very focal, without significant propagation. Following multiple sessions of recording for a median continuous wear time of 13 hrs, patients reported a high degree of tolerance for the device, with only minor adverse events reported by the scalp EEG cohort.

conclusionsThese preliminary results demonstrate the potential of using ear-EEG to enable routine collection of complementary, prolonged, and remote neurophysiological evidence, which may permit real-time detection of paroxysmal events such as seizures and epileptiform discharges. This study suggests that the ear-EEG device may assist clinicians in making an epilepsy diagnosis, assessing treatment efficacy, and optimizing medication titration.

Indexed as

Ear-EEGFocal epilepsyLong-term EEGSeizure detectionTemporal lobeWearable technologies

Identifiers

PMID38321561
PMCPMC10848360

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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.