Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Vadim Gliner"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract 12-lead electrocardiogram (ECG) recordings can be collected in any clinic and the interpretation is performed by a clinician. Modern machine learning tools may make them automatable. However, a large fraction of 12-lead ECG data is still ava
Externí odkaz:
https://doaj.org/article/bd8e37e0c1794e0ba5494f338050358d
Autor:
Evgeny Karpulevich, Vsevolod Vladislavovich Shaklein, Vladislav Valerievich Ananev, Yurij Emilevich Teregulov, Denis Yuryevich Turdakov, Sergej Nikolaevich Skorik, Assaf Schuster, Aram Arutyunovich Avetisyan, Vadim Gliner
Publikováno v:
Proceedings of the Institute for System Programming of the RAS. 33:87-98
Recording and analyzing 12-lead electrocardiograms is the most common procedure for detecting heart disease. Recently, various deep learning methods have been proposed for the automatic diagnosis by an electrocardiogram. The proposed methods can prov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dac3cf51f008638727e0472799e9ff89
https://doi.org/10.15514/ispras-2021-33(4)-7
https://doi.org/10.15514/ispras-2021-33(4)-7
Autor:
Assaf Schuster, Vadim Gliner, Noam Keidar, Arutyun Avetisyan, Yael Yaniv, Vladimir I. Makarov
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
Standard 12-lead electrocardiography (ECG) is used as the primary clinical tool to diagnose changes in heart function. The value of automated 12-lead ECG diagnostic approaches lies in their ability to screen the general population and to provide a se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b57c48c187b67674ab2151cb9812128
https://pubmed.ncbi.nlm.nih.gov/33004907
https://pubmed.ncbi.nlm.nih.gov/33004907
Autor:
Kevin Justin Herrera, Hiroshi Nakagawa, Warren M. Jackman, Ayman A. Hussein, Atsushi Ikeda, Walid Saliba, Israel Zliberman, Vadim Gliner, Shunsuke Kuroda, Christopher Thomas Beeckler, Joseph Thomas Keyes, Assaf Govari, Oussama M. Wazni
Publikováno v:
Heart Rhythm. 18:S312
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9eb67641661ca03004996aea19f61368
https://doi.org/10.1016/j.hrthm.2021.06.777
https://doi.org/10.1016/j.hrthm.2021.06.777
Autor:
Yael Yaniv, Vadim Gliner
Publikováno v:
Physiological measurement. 39(9)
Objectives: We designed an automated algorithm to classify short electrocardiogram (ECG) strips into four categories: normal rhythm, atrial fibrillation, noisy segment, or other rhythm disturbances. Approach: The algorithm is based on identification
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85a71711b39f39951b4a6d8459445ce4
https://pubmed.ncbi.nlm.nih.gov/30187892
https://pubmed.ncbi.nlm.nih.gov/30187892
Autor:
Vadim Gliner, Yael Yaniv
Publikováno v:
CinC
Cardiac arrhythmias are the leading cause of death in the western world, where atrial fibrillation (AF) is the most common arrhythmias. The PhysioNet/CinC 2017 Challenge aimed to trigger a design of an algorithm that accurately classifies short singl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b92f823df952adbf719942fc3529d42
https://doi.org/10.22489/cinc.2017.170-101
https://doi.org/10.22489/cinc.2017.170-101
Autor:
Vadim Gliner, Yael Yaniv
Publikováno v:
Physiological Measurement; Sep2018, Vol. 39 Issue 9, p1-1, 1p