ArticleJournal of neuroscience methods2021
Application of a convolutional neural network for fully-automated detection of spike ripples in the scalp electroencephalogram.
Article in Journal of neuroscience methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 20 citations in OpenAlex.
- A data augmentation procedure to improve detection of spike ripples in brain voltage recordings.Neuroscience research · 2025Article
- Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings.Scientific reports · 2024Article
- Spike ripples localize the epileptogenic zone best: an international intracranial study.Brain : a journal of neurology · 2024Article
- A machine learning toolbox for the analysis of sharp-wave ripples reveals common waveform features across species.Communications biology · 2024Article
- Networks through the lens of high-frequency oscillations.Frontiers in network physiology · 2024Review
- A machine learning toolbox for the analysis of sharp-wave ripples reveal common features across species.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundA reliable biomarker to identify cortical tissue responsible for generating epileptic seizures is required to guide prognosis and treatment in epilepsy. Combined spike ripple events are a promising biomarker for epileptogenic tissue that currently require expert review for accurate identification. This expert review is time consuming and subjective, limiting reproducibility and high-throughput applications. NEW
methodTo address this limitation, we develop a fully-automated method for spike ripple detection. The method consists of a convolutional neural network trained to compute the probability that a spectrogram image contains a spike ripple.
resultsWe validate the proposed spike ripple detector on expert-labeled data and show that this detector accurately separates subjects with low and high seizure risks. COMPARISON WITH EXISTING
methodThe proposed method performs as well as existing methods that require manual validation of candidate spike ripple events. The introduction of a fully automated method reduces subjectivity and increases rigor and reproducibility of this epilepsy biomarker.
conclusionWe introduce and validate a fully-automated spike ripple detector to support utilization of this epilepsy biomarker in clinical and translational work.
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Registered trials
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.