Crowdsourcing reproducible seizure forecasting in human and canine epilepsy - Wikidata - awgldk - TriplyDB

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

http://www.wikidata.org/entity/Q30489169

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Interictal high-frequency oscillations in focal human epilepsy Supervised filters for EEG signal in naturally occurring epilepsy forecasting.2014 Epilepsy Benchmarks Area III: Improve Treatment Options for Controlling Seizures and Epilepsy-Related Conditions Without Side Effects Automated Detection of Epileptic Biomarkers in Resting-State Interictal MEG Data.Behavioral state classification in epileptic brain using intracranial electrophysiology.Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings.Seizure Forecasting and the Preictal State in Canine Epilepsy.SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal.Crowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy.Multi-day rhythms modulate seizure risk in epilepsy.Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.The circadian profile of epilepsy improves seizure forecasting.Seizure prediction: making mileage on the long and winding road.Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank.[Optimistic perspectives for the health of the future].Seizure Prediction Is Possible-Now Let's Make It Practical.Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain.Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review.Towards an Online Seizure Advisory System-An Adaptive Seizure Prediction Framework Using Active Learning Heuristics.Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence

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P2860

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

http://www.wikidata.org/entity/Q30489169

description

2016 nî lūn-bûn

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2016 թուականի Մարտին հրատարակուած գիտական յօդուած

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2016 թվականի մարտին հրատարակված գիտական հոդված

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2016年の論文

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2016年論文

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2016年論文

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2016年論文

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2016年論文

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2016年論文

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2016年论文

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name

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

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Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

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type

label

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

@ast

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

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prefLabel

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

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Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

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Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

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Brian Litt

http://www.w3.org/2001/XMLSchema#string

Charles Vite

http://www.w3.org/2001/XMLSchema#string

Drew Abbot

http://www.w3.org/2001/XMLSchema#string

Edward E Patterson

http://www.w3.org/2001/XMLSchema#string

Francisco Zamora-Martinez

http://www.w3.org/2001/XMLSchema#string

Gregory A Worrell

http://www.w3.org/2001/XMLSchema#string

Iryna Korshunova

http://www.w3.org/2001/XMLSchema#string

Jialune He

http://www.w3.org/2001/XMLSchema#string

Juan Pardo

http://www.w3.org/2001/XMLSchema#string

Michael Hills

http://www.w3.org/2001/XMLSchema#string

P2860

Why most published research findings are false

Clinical risk factors associated with anti-epileptic drug responsiveness in canine epilepsy

An invitation to reproducible computational research

A call for transparent reporting to optimize the predictive value of preclinical research

Large-scale electrophysiology: acquisition, compression, encryption, and storage of big data

Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy

Bagging predictors

Power failure: why small sample size undermines the reliability of neuroscience

Regularization Paths for Generalized Linear Models via Coordinate Descent

Gradient-based learning applied to document recognition

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1713-1722

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scholarly article

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10.1093/BRAIN/AWW045

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Pt 6

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139

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Benjamin Brinkmann

Francisco Javier Muñoz-Almaraz

Paloma Botella-Rocamora

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2016-03-31T00:00:00Z

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299541188

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27034258

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reproducibility

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5022671

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