Results 121 to 130 of about 141,484 (301)

Deep Learning of Human Perception in Audio Event Classification

, 2018
In this paper, we introduce our recent studies on human perception in audio event classification by different deep learning models. In particular, the pre-trained model VGGish is used as feature extractor to process audio data, and DenseNet is trained by
Beuret, Samuel, Oyama, Keizo, Yu, Yi, Zeng, Donghuo   +3 more
core   +1 more source

Delayed brain and spine migration of a retained SEEG electrode fragment: An unexpected complication

Epileptic Disorders, EarlyView.
Abstract Background Stereoelectroencephalography (SEEG) is a well‐established technique for localizing epileptogenic zones in patients with drug‐resistant epilepsy, including children. While considered safe, rare but serious complications can occur.
Manel Krouma, Julia Makhalova, Gauthier Toutain, Francesca Pizzo, Stanislas Lagarde, Anne Lepine, Fabrice Bartolomei, Didier Scavarda   +7 more
wiley   +1 more source

A call for ethical, equitable, and effective artificial intelligence to improve care for all people with epilepsy: A roadmap. A report by the ILAE Global Advocacy Council and Big Data Commission

Epilepsia, EarlyView.
Abstract The artificial intelligence (AI) revolution is upon us. It will inevitably form a central component of epilepsy workflows and patient advocacy. Therefore, it behooves us as health care providers to ride the crest of this wave and guide its direction for the benefit of all people with epilepsy.
Colin B. Josephson, Sandor Beniczky, Spiros Denaxas, Akio Ikeda, Lara Jehi, Angelina Kakooza Mwesige, Nathalie Jette, Gabriel Davis Jones, Philippe Ryvlin, Arjune Sen, Chahnez Charfi Triki, Gabriella Waters, Alla Guekht, J. Helen Cross   +13 more
wiley   +1 more source

Scalp‐negative medial temporal interictal epileptic discharges alter large‐scale brain networks: A simultaneous high‐density electroencephalographic and intracranial electroencephalographic study

Epilepsia, EarlyView.
Abstract Objective Interictal epileptiform discharges (IEDs) observed on scalp electroencephalography (EEG) serve as a diagnostic hallmark of epilepsy. However, only a small fraction of IEDs recorded by intracranial EEG (iEEG) are detectable on the scalp; the vast majority remain invisible on scalp recordings.
Nicolas Roehri, Pia De Stefano, Laurent Spinelli, Renaud Marquis, Stanislas Lagarde, Shahan Momjian, Margitta Seeck, Serge Vulliemoz   +7 more
wiley   +1 more source

Generative topographic mapping of electroencephalography (EEG) data

, 2014
Generative Topographic Mapping (GTM) assumes that the features of high dimensional data can be described by a few variables (usually 1 or 2). Based on this assumption, the GTM trains unsupervised on the high dimensional data to find these variables from which the features can be generated.
Dantanarayana, Navini, author, Anderson, Charles, advisor, Ben-Hur, Asa, committee member, Davies, Patricia, committee member   +3 more
openaire   +1 more source

Synchrotron‐generated microbeams as a radiosurgical alternative for drug‐resistant epilepsies: Proof of concept in a mouse model of mesiotemporal lobe epilepsy

Epilepsia, EarlyView.
Abstract Objective One‐third of patients with epilepsy, particularly those with mesial temporal lobe epilepsy (MTLE), remain resistant to medication. Resective surgery, the gold standard, is highly invasive and carries significant risks. Here, using a mouse model, we explored the potential of microbeam radiation therapy (MRT), a new technique based on ...
Loan Samalens, Camille Beets, Clothilde Courivaud, Sarvenaz Keshmiri, Jean‐François Adam, Paolo Pellicioli, Emmanuel Luc Barbier, Raphaël Serduc, Antoine Depaulis   +8 more
wiley   +1 more source

Quantifying the impact of a computer‐aided diagnostic score on the clinical diagnosis of functional seizures

Epilepsia, EarlyView.
Abstract Objective The diagnosis of functional/dissociative seizures (FDS) without ictal video‐electroencephalography is challenging. The Functional/Dissociative Seizures Likelihood Score (FSLS) is a machine learning‐based diagnostic score that aims to help clinicians identify FDS.
Wesley T. Kerr, Katherine N. McFarlane, Corinne H. Allas, Samuel W. Terman, Markus Reuber, Sung Hyun Seo, Payge Barnard, Adriana Y. Koek, Amir H. Karimi, Siddhika S. Sreenivasan, Jena S. Grauer, Di Sun, Meagan Watson, Elissa H. Patterson, James Castellano, Anto I. Bagić, L. Brian Hickman, Kenneth H. Kutschman, Laurence Knowles, Alistair Wardrope, Piyush Ostwal, Bridget K. MacDonald, Melissa Berry, Lomalan Reddy, Najda Robinson‐Mayer, Nicholas J. Beimer, Rachna Reddy, Sarah K. Yaghoubi, Laura A. Kirkpatrick, Danielle R. Carns, Alex J. Israel, Jamie D. Feusner, Zongqi Xia, Yanshan Wang, Laura A. Strom, Page B. Pennell, John M. Stern, Lubomir Hadjiiski, William C. Stacey   +38 more
wiley   +1 more source

Timing is everything: Expert opinion on researching epilepsy rhythms by the ILAE Task Force on Chronobiology

Epilepsia, EarlyView.
Abstract Recurrent seizures, the hallmark of epilepsy, are influenced by rhythms operating over multiple timescales. Chronobiology is the study of biological timing that aims to explain temporal patterns of events like seizures. Fueled by recent advances in genetics, computational modeling, and device engineering, the chronobiology of epilepsy is now a
Maxime O. Baud, Christophe Bernard, Birgit Frauscher, Philippa J. Karoly, Vikram R. Rao   +4 more
wiley   +1 more source

Weaning from ketogenic diet therapy in children with epilepsy: Insights from a retrospective study

Epilepsia, EarlyView.
Abstract Objective This study was undertaken to describe weaning practices following ketogenic diet therapy (KDT) in children with epilepsy and to identify clinical factors associated with seizure exacerbation or antiseizure medication adjustments during or after weaning from KDT.
Noémie Donnard, Blandine Dozières‐Puyravel, Katia Geraldes, Céline Perrot, Virginie Quéméner, Estelle Goujon, Pierre Truchy, Hala Nasser, François‐Xavier Mauvais, Marion Danse, Stéphane Auvin   +10 more
wiley   +1 more source

Hidden conditional random fields for classification of imaginary motor tasks from EEG data [PDF]

, 2011
Brain-computer interfaces (BCIs) are systems that allow the control of external devices using information extracted from brain signals. Such systems find application in rehabilitation of patients with limited or no muscular control. One mechanism used in
Delgado Saa, Jaime Fernando, Çetin, Müjdat   +1 more
core