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Ivo Viscor
Researcher at Academy of Sciences of the Czech Republic
Publications - 63
Citations - 507
Ivo Viscor is an academic researcher from Academy of Sciences of the Czech Republic. The author has contributed to research in topics: QRS complex & Left bundle branch block. The author has an hindex of 10, co-authored 53 publications receiving 321 citations.
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Journal ArticleDOI
Intracerebral EEG Artifact Identification Using Convolutional Neural Networks
Petr Nejedly,Petr Nejedly,Jan Cimbalnik,Petr Klimes,Filip Plesinger,Josef Halamek,Vaclav Kremen,Ivo Viscor,Benjamin H. Brinkmann,Martin Pail,Milan Brázdil,Milan Brázdil,Gregory A. Worrell,Pavel Jurák +13 more
TL;DR: A novel machine-learning approach for detection of artifacts in iEEG signals in clinically controlled conditions using convolutional neural networks (CNN) and benchmarks the method’s performance against expert annotations.
Journal ArticleDOI
Left bundle branch pacing compared to left ventricular septal myocardial pacing increases interventricular dyssynchrony but accelerates left ventricular lateral wall depolarization.
Karol Curila,Pavel Jurák,Marek Jastrzębski,Frits W. Prinzen,Petr Waldauf,Josef Halamek,Kevin Vernooy,Radovan Smisek,Radovan Smisek,Jakub Karch,Filip Plesinger,Paweł Moskal,Marketa Susankova,Lucie Znojilova,Luuk I.B. Heckman,Ivo Viscor,Vlastimil Vondra,Pavel Leinveber,Pavel Osmancik +18 more
TL;DR: In this article, the authors compared different pacing techniques using ultra-high-frequency electrocardiography (UHF-ECG) and found that nsLBBp resulted in larger e-DYS than did LVSP and nsHBp.
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Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG
TL;DR: This work proposes a method for automated classification of 1-lead Holter ECG recordings using two machine learning methods in parallel that led to shared rank #2 in the follow-up PhysioNet/CinC Challenge 2017 ranking.
Journal ArticleDOI
Heart sounds analysis using probability assessment.
TL;DR: A method for automated discrimination of heart sounds recordings according to the Physionet Challenge 2016 to decide if the recording refers to normal or abnormal heart sounds or if it is not possible to decide, a prototype of a new machine-learning method.
Journal ArticleDOI
Ventricular dyssynchrony assessment using ultra-high frequency ECG technique.
Pavel Jurák,Josef Halamek,Jaroslav Meluzín,Filip Plesinger,Tereza Postranecka,Jolana Lipoldová,Miroslav Novák,Vlastimil Vondra,Ivo Viscor,Ladislav Soukup,Petr Klimes,Petr Vesely,Josef Šumbera,Karel Zeman,Roshini Asirvatham,Jason Tri,Samuel J. Asirvatham,Pavel Leinveber +17 more
TL;DR: UHFQRS offers a new and simple technique for assessing electrical activation patterns in ventricular dyssynchrony with a temporal-spatial resolution that cannot be obtained by processing standard surface ECG.