Evidence map›Paper›PMID 37603979›Full record

ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2023

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

Tonmoy Monsoor, Yipeng Zhang, Atsuro Daida, Shingo Oana, Qiujing Lu, Shaun A Hussain, Aria Fallah, Raman Sankar, Richard J Staba, William Speier and 2 more

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, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
4.0field-weighted citation impact, top 5% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 1 country.

Tonmoy MonsoorDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Yipeng ZhangDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Atsuro DaidaDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA.
Shingo OanaDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA.
Qiujing LuDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Shaun A HussainDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA.
Aria FallahDepartment of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA.
Raman SankarDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA; The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA.
Richard J StabaDepartment of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA.
William SpeierDepartment of Bioengineering, University of California, Los Angeles, CA, USA; Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
Vwani RoychowdhuryDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Hiroki NariaiDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA; The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA. Electronic address: hnariai@mednet.ucla.edu.
University of California, Los Angeles · USMattel Children's Hospital · USChildren’s Institute · USOlive View-UCLA Medical Center · US

Funding

Defining the Epileptogenic Network and Identifying Which Components Generate SeizuresR01NS106957 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Richard Staba · 2018 to 2026
$3.4M
Refining neurophysiological biomarkers of epilepsy using deep learning to guide pediatric epilepsy surgeryK23NS128318 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Hiroki Nariai · 2023 to 2026
$968k
NINDS NIH HHS K23 NS128318NINDS NIH HHS R01 NS106957
6 · The paper itself

Abstract

objectiveThis study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery.

methodsWe analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method.

resultsThe MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors had the highest spike association rate. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification.

conclusionsHFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. SIGNIFICANCE: Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes.

Indexed as

Deep LearningDrug Resistant EpilepsyEpilepsyChildElectroencephalographyHumansSeizuresDeep learningHFOMachine learningMNISTE

Identifiers

PMID37603979
PMCPMC10861270
OpenAlexW4385688072

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.