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EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes

https://doi.org/10.1109/tmi.2019.2958699

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Interictal networks in Magnetoencephalography

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Deep learning with convolutional neural networks for EEG decoding and visualization

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Reproducibility of EEG‐MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy

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Algorithms for Computational Biology

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Evaluation of signal space separation via simulation

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Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG

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Automated interictal EEG spike detection using artificial neural networks

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Automated interictal spike detection and source localization in magnetoencephalography using independent components analysis and spatio-temporal clustering

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Interictal spike analysis of high-density EEG in patients with partial epilepsy

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Automatic detection of prominent interictal spikes in intracranial EEG: Validation of an algorithm and relationsip to the seizure onset zone

inferredcites →https://doi.org/10.1016/j.clinph.2015.11.451openalex-snapshot

When spikes are symmetric, ripples are not: Bilateral spike and wave above 80 Hz in focal and generalized epilepsy

inferredcites →https://doi.org/10.1016/j.clinph.2016.11.005openalex-snapshot

Spike detection: Inter-reader agreement and a statistical Turing test on a large data set

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Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System

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Source analysis of epileptiform discharges in absence epilepsy using Magnetoencephalography (MEG)

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Localization of MEG pathologic gamma oscillations in adult epilepsy patients with focal cortical dysplasia

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DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning

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Toward a definition of MEG spike: Parametric description of spikes recorded simultaneously by MEG and depth electrodes

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Spike detection: a review and comparison of algorithms

inferredcites →https://doi.org/10.1038/nn.4504openalex-snapshot

Magnetoencephalography for brain electrophysiology and imaging

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A Randomized Controlled Trial of Surgery for Temporal Lobe Epilepsy

inferredcites →https://doi.org/10.1088/0031-9155/51/7/008openalex-snapshot

Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements

inferredcites →https://doi.org/10.1093/med/9780195328547.001.0001openalex-snapshot

Seizures and Epilepsy

inferredcites →https://doi.org/10.1097/00004691-199901000-00006openalex-snapshot

An Automated System for Epileptogenic Focus Localization in the Electroencephalogram

inferredcites →https://doi.org/10.1109/access.2016.2602354openalex-snapshot

Epileptic MEG Spikes Detection Using Common Spatial Patterns and Linear Discriminant Analysis

inferredcites →https://doi.org/10.1109/access.2017.2718044openalex-snapshot

Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping

inferredcites →https://doi.org/10.1109/access.2018.2875487openalex-snapshot

A Review of EEG and MEG Epileptic Spike Detection Algorithms

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An investigation of deep neural networks for noise robust speech recognition

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Epileptiform spike detection via convolutional neural networks

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Very deep convolutional networks for end-to-end speech recognition

inferredcites →https://doi.org/10.1109/ijcnn.2018.8489493openalex-snapshot

Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller

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Artifact Removal in Magnetoencephalogram Background Activity With Independent Component Analysis

inferredcites →https://doi.org/10.1109/tbme.2011.2157917openalex-snapshot

An Automatic Spike Detection System Based on Elimination of False Positives Using the Large-Area Context in the Scalp EEG

inferredcites →https://doi.org/10.1109/tmi.2018.2836965openalex-snapshot

A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy

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Intracranial EEG Substrates of Scalp EEG Interictal Spikes

inferredcites →https://doi.org/10.1111/j.1528-1167.2007.01223.xopenalex-snapshot

Magnetoencephalography Is More Successful for Screening and Localizing Frontal Lobe Epilepsy than Electroencephalography

inferredcites →https://doi.org/10.1148/radiol.2253011597openalex-snapshot

Magnetoencephalographically Directed Review of High-Spatial-Resolution Surface-Coil MR Images Improves Lesion Detection in Patients with Extratemporal Epilepsy

inferredcites →https://doi.org/10.1155/2017/3035606openalex-snapshot

Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

inferredcites →https://doi.org/10.1186/s40537-014-0007-7openalex-snapshot

Deep learning applications and challenges in big data analytics

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Empirical Evaluation of Rectified Activations in Convolutional Network

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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Stied: A Deep Learning Model for the Spatiotemporal Detection of Focal Interictal Epileptiform Discharges with Meg

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Informed MEG/EEG source imaging reveals the locations of interictal spikes missed by SEEG

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