Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/111548
Title: Effects of Lead Position, Cardiac Rhythm Variation and Drug-induced QT Prolongation on Performance of Machine Learning Methods for ECG Processing
Authors: Bogdanov, M.
Baigildin, S.
Fabarisova, A.
Ushenin, K.
Solovyova, O.
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
IEEE
Citation: Effects of Lead Position, Cardiac Rhythm Variation and Drug-induced QT Prolongation on Performance of Machine Learning Methods for ECG Processing / M. Bogdanov, S. Baigildin, A. Fabarisova et al. // Proceedings - 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2020. — 2020. — Vol. — P. 40-43. — 9117753.
Abstract: Machine learning shows great performance in various problems of electrocardiography (ECG) signal analysis. However, collecting a dataset for biomedical engineering is a very difficult task. Any dataset for ECG processing contains from 100 to 10,000 times fewer cases than datasets for image or text analysis. This issue is especially important because of physiological phenomena that can significantly change the morphology of heartbeats in ECG signals. In this preliminary study, we analyze the effects of lead choice from the standard ECG recordings, variation of ECG during 24-hours, and the effects of QT-prolongation agents on the performance of machine learning methods for ECG processing. We choose the problem of subject identification for analysis, because this problem may be solved for almost any available dataset of ECG data. In a discussion, we compare our findings with observations from other works that use machine learning for ECG processing with different problem statements. Our results show the importance of training dataset enrichment with ECG signals acquired in specific physiological conditions for obtaining good performance of ECG processing for real applications. © 2020 IEEE.
Keywords: ELECTROCARDIOGRAPHY
HEART RHYTHM VARIATION
MACHINE LEARNING
MORPHOLOGY ANALYSIS
QT-PROLONGATION
SUBJECT IDENTIFICATION
BIOMEDICAL ENGINEERING
BIOPHYSICS
MACHINE LEARNING
PHYSIOLOGY
CARDIAC RHYTHMS
MACHINE LEARNING METHODS
PHYSIOLOGICAL CONDITION
PROBLEM STATEMENT
QT PROLONGATION
REAL APPLICATIONS
SUBJECT IDENTIFICATION
TRAINING DATASET
URI: http://hdl.handle.net/10995/111548
Access: info:eu-repo/semantics/openAccess
Conference name: 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2020
Conference date: 14 May 2020 through 15 May 2020
SCOPUS ID: 85089701447
PURE ID: 13661650
ISBN: 9781728131658
metadata.dc.description.sponsorship: The reported study was supported by RFBR research project No. 19-37-50079 and supported by the IIF UrB RAS theme №AAAA-A18-118020590031-8, RF Government Act #211 of March 16, 2013, the Program of the Presidium RAS.
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

Files in This Item:
File Description SizeFormat 
2-s2.0-85089701447.pdf363,19 kBAdobe PDFView/Open


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