ArticleFrontiers in computational neuroscience2019
Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network.
Article in Frontiers in computational neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 77 citations in OpenAlex.
- Localizing the epileptogenic zone in drug-resistant epilepsy based on ictal SEEG high-resolution time-frequency representation.Frontiers in neuroscience · 2026Article
- Epileptic Seizure Detection Using Hyperdimensional Computing and Binary Naive Bayes Classifier.Bioengineering (Basel, Switzerland) · 2025Article
- Machine learning detection of epileptic seizure onset zone from iEEG.Biomedical engineering letters · 2025Review
- A data augmentation procedure to improve detection of spike ripples in brain voltage recordings.Neuroscience research · 2025Article
- An Efficient Approach for Detection of Various Epileptic Waves Having Diverse Forms in Long Term EEG Based on Deep Learning.Brain topography · 2025Article
- KAN-EEG: towards replacing backbone-MLP for an effective seizure detection system.Royal Society open science · 2025Article
- Deep learning in intracranial EEG for seizure detection: advances, challenges, and clinical applications.Frontiers in neuroscience · 2025Review
- Networks through the lens of high-frequency oscillations.Frontiers in network physiology · 2024Review
- Combining data augmentation and deep learning for improved epilepsy detection.Frontiers in neurology · 2024Article
- A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification.Sensors (Basel, Switzerland) · 2023Article
- Epileptic seizure focus detection from interictal electroencephalogram: a survey.Cognitive neurodynamics · 2023Review
- Characterizing physiological high-frequency oscillations using deep learning.Journal of neural engineering · 2022Article
- An approach for reliably identifying high-frequency oscillations and reducing false-positive detections.Epilepsia open · 2022Article
- Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach.Brain communications · 2022Article
- Deep learning for epileptogenic zone delineation from the invasive EEG: challenges and lookouts.Brain communications · 2022Article
- Application of a convolutional neural network for fully-automated detection of spike ripples in the scalp electroencephalogram.Journal of neuroscience methods · 2021Article
- RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection.Neuroinformatics · 2021Article
- Epileptic Seizures Detection Using Deep Learning Techniques: A Review.International journal of environmental research and public health · 2021Review
- Automatic Detection of High-Frequency Oscillations Based on an End-to-End Bi-Branch Neural Network and Clinical Cross-Validation.Computational intelligence and neuroscience · 2021Article
- Identification of Epileptogenic and Non-epileptogenic High-Frequency Oscillations Using a Multi-Feature Convolutional Neural Network Model.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epilepsy is one of the most common chronic neurological diseases. High-frequency oscillations (HFOs) have emerged as promising biomarkers for the epileptogenic zone. However, visual marking of HFOs is a time-consuming and laborious process. Several automated techniques have been proposed to detect HFOs, yet these are still far from being suitable for application in a clinical setting. Here, ripples and fast ripples from intracranial electroencephalograms were detected in six patients with intractable epilepsy using a convolutional neural network (CNN) method. This approach proved more accurate than using four other HFO detectors integrated in RIPPLELAB, providing a higher sensitivity (77.04% for ripples and 83.23% for fast ripples) and specificity (72.27% for ripples and 79.36% for fast ripples) for HFO detection. Furthermore, for one patient, the Cohen's kappa coefficients comparing automated detection and visual analysis results were 0.541 for ripples and 0.777 for fast ripples. Hence, our automated detector was capable of reliable estimates of ripples and fast ripples with higher sensitivity and specificity than four other HFO detectors. Our detector may be used to assist clinicians in locating epileptogenic zone in the future.
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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.