Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/47459
Type of publication: research article
Type of publication (PDB): Straipsnis konferencijos medžiagoje Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or Scopus DB conference proceedings (P1a)
Field of Science: Informatika / Informatics (N009)
Author(s): Tamošiūnaitė, Minija;Raudys, Šarūnas
Title: A neural network based investigation of high frequency components of the ECG
Is part of: Neural networks and soft computing : 6th international conference on neural network and soft computing, Zakopane, Poland, June 11-15, 2002: proceedings. Heidelberg : Springer/ Physica verlag, 2003
Extent: p. 492-497
Date: 2003
Series/Report no.: (Advances in soft computing. Vol. 19 1615-3871)
ISBN: 9783790800050
Abstract: New information retrieval method is applied to detect low amplitude high frequency components of electrocardiogram (ECG). The special neural network using similarities to prototype features is suggested. Prognosis error is chosen as similarity measure of a signal to a prototype. This measure is preferable in the case of a poor signal to noise ratio. New technique was successfully applied for classification of ECG recordings of myocardial infarction (MI) patients with the complication of ventricular fibrillation (VF) vs. the MI patients who have not had the VF, a problem where standard methods failed to provide satisfactory separation of pattern classes
Internet: https://www.springer.com/gp/book/9783790800050
Affiliation(s): Informatikos fakultetas
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

Files in This Item:
marc.xml7.53 kBXMLView/Open

MARC21 XML metadata

Show full item record
Export via OAI-PMH Interface in XML Formats
Export to Other Non-XML Formats


CORE Recommender

Page view(s)

79
checked on May 1, 2021

Download(s)

8
checked on May 1, 2021

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.