Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Jakub Hejc"'
Autor:
Marina Ronzhina, Veronika Olejnickova, Tibor Stracina, Marie Novakova, Oto Janousek, Jakub Hejc, Jana Kolarova, Miroslava Hlavacova, Hana Paulova
Publikováno v:
BMC Cardiovascular Disorders, Vol 17, Iss 1, Pp 1-11 (2017)
Abstract Background Detailed quantitative analysis of the effect of left ventricle (LV) hypertrophy on myocardial ischemia manifestation in ECG is still missing. The associations between both phenomena can be studied in animal models. In this study,
Externí odkaz:
https://doaj.org/article/c90a98aa67d84edba9bd4590d9869d0f
Autor:
Frantisek Lehar, Nándor Szegedi, Jakub Hejc, Jiri Jez, Filip Soucek, Tomas Kulik, Anna Siruckova, Zoltan Sallo, Klaudia Vivien Nagy, Bela Merkely, László Geller, Zdeněk Starek
Publikováno v:
EP Europace. 24:1636-1644
Aims Interventional cardiology procedures may expose patients and staff to considerable radiation doses. We aimed to assess whether exposure to ionizing radiation during catheter ablation of supraventricular tachycardia (SVT) can be completely avoide
Publikováno v:
Computing in Cardiology Conference (CinC).
Publikováno v:
Computing in Cardiology Conference (CinC).
Publikováno v:
2021 Computing in Cardiology (CinC).
Autor:
Jakub Hejc, David Pospisil, Petra Novotna, Martin Pesl, Oto Janousek, Marina Ronzhina, Zdenek Starek
Publikováno v:
2021 Computing in Cardiology (CinC).
Publikováno v:
2021 Computing in Cardiology (CinC).
Autor:
Martin Pesl, Jakub Hejc, Tomas Kulik, Tomas Vicar, Petra Novotna, Marina Ronzhina, Juraj Jakubik, Pavel Leinveber, Juan Pablo Gonzalez Rivas, Zdenek Starek
Publikováno v:
2021 Computing in Cardiology (CinC).
Publikováno v:
CinC
Since common electrocardiography (ECG) diagnostics approaches are time-consuming and arrhythmia-type sensitive, deep-learning methods are state-of-the-art for their detection accuracy. However, premature ventricular contractions' (PVC) localization v
Publikováno v:
CinC
The latest trends in clinical care and telemedicine suggest a demand for a reliable automated electrocardiogram (ECG) signal classification methods. In this paper, we present customized deep learning model for ECG classification as a part of the Phys