ArticleIEEE transactions on medical imaging2018
A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00600717 (Localizing Functional Brain Cortices and Epileptogenic Zones With High Frequency Brain Signals), which is not on this map. Cited by 13 papers.
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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.
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.
Localizing Functional Brain Cortices and Epileptogenic Zones With High Frequency Brain Signals
Who cites it
13 citing papers in PubMed.
- AI enabled decision support systems in epilepsy surgery a scoping review.Research square · 2026Article
- Deep learning enabled decision support systems in epilepsy surgery: a scoping review.npj health systems · 2026Article
- Magnetoencephalography-based approaches to epilepsy classification.Frontiers in neuroscience · 2023Review
- Transformer-Based High-Frequency Oscillation Signal Detection on Magnetoencephalography From Epileptic Patients.Frontiers in molecular biosciences · 2022Article
- A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy.Frontiers in neuroinformatics · 2022Article
- Neuromagnetic high frequency spikes are a new and noninvasive biomarker for localization of epileptogenic zones.Seizure · 2021Article
- Variability analysis of epileptic EEG using the maximal overlap discrete wavelet transform.Health information science and systems · 2020Article
- Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System.Sensors (Basel, Switzerland) · 2020Article
- Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG.Scientific reports · 2020Article
- MEG Sensor Selection for Neural Speech Decoding.IEEE access : practical innovations, open solutions · 2020Article
- Kurtosis and skewness of high-frequency brain signals are altered in paediatric epilepsy.Brain communications · 2020Article
- A Long Short-Term Memory neural network for the detection of epileptiform spikes and high frequency oscillations.Scientific reports · 2019Article
- How Can Animal Models Advance Research into High Frequency Oscillations: Guidelines for Recording, Detection and Analysis.Epilepsy currentsReview
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for the accurate and timely clinical evaluation. Since the visual examination of HFOs is time-consuming, error-prone, and with poor inter-reviewer reliability, an automatic HFOs detector is highly desirable in clinical practice. However, the existing approaches for HFOs detection may not be applicable for MEG signals with noisy background activity. Therefore, we employ the stacked sparse autoencoder (SSAE) and propose an SSAE-based MEG HFOs (SMO) detector to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first attempt to conduct HFOs detection in MEG using deep learning methods. After configuration optimization, our proposed SMO detector is outperformed other classic peer models by achieving 89.9% in accuracy, 88.2% in sensitivity, and 91.6% in specificity. Furthermore, we have tested the performance consistency of our model using various validation schemes. The distribution of performance metrics demonstrates that our model can achieve steady performance.
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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.