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Comparative Analysis on Wavelet-Based Detection of Finite Duration Low-Amplitude Signals Related To Ventricular Late Potentials

dc.contributor.author Mousa, A
dc.contributor.author Yilmaz, A
dc.date.accessioned 2020-04-18T13:30:21Z
dc.date.accessioned 2025-09-18T14:10:52Z
dc.date.available 2020-04-18T13:30:21Z
dc.date.available 2025-09-18T14:10:52Z
dc.date.issued 2004
dc.description.abstract Ventricular late potentials (VLPs) are considered as a noninvasive marker of patients with myocardial infarction, who are prone to the development of ventricular tachycardia. This paper investigates the effects of variations in physical properties of myocardial infarcts in terms of their effects on the parametric variations in VLP analysis. A sufficiently large set of signals underlining the behavior of physical parameters was employed to represent the effect of physical size, position, orientation and type of infarct. The approximated signals are variations from real electrocardiography signals by adding potentials representing late potentials based on duration, frequency, amplitude and position. The aim is not to exactly model VLP but rather to generate an approximate set of signals to examine the performance of the standard methods for different possibilities in infarct dynamics. We investigate some of the detection approaches together with their related assumptions, and try to pinpoint the drawbacks and inaccuracies of these methods and also their assumptions. The three widely accepted criteria-QRS duration, root-mean-square and duration of the signal at the end of QRS for VLP detection-were used in the investigation. Results from the application of these parameters to the set of signals are presented. In addition we investigate the physical nature of an infarct and list a number of possible reasons that might be the cause of a low success rate for the detection of additive potentials. To improve the performance of the common methods, two more wavelet transform parameters are added to those of the standard methods. The method derived from this analysis is presented as an alternative means for the detection of late signals named as delayed potentials, a more general class that includes VLP as a subset. en_US
dc.identifier.citation Mousa, A.; Yılmaz, A., "Comparative analysis on wavelet-based detection of finite duration low-amplitude signals related to ventricular late potentials", Physiological Measurement, Vol.25, No.6, (2004). en_US
dc.identifier.doi 10.1088/0967-3334/25/6/010
dc.identifier.issn 0967-3334
dc.identifier.issn 1361-6579
dc.identifier.scopus 2-s2.0-12344264702
dc.identifier.uri https://doi.org/10.1088/0967-3334/25/6/010
dc.identifier.uri https://hdl.handle.net/20.500.12416/13835
dc.language.iso en en_US
dc.publisher Iop Publishing Ltd en_US
dc.relation.ispartof Physiological Measurement
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Delayed Potentials (Dp) en_US
dc.subject Electrocardiography (Ecg) en_US
dc.subject Ventricular Late Potentials (Vlp) en_US
dc.subject Wavelet Transform (Wt) en_US
dc.title Comparative Analysis on Wavelet-Based Detection of Finite Duration Low-Amplitude Signals Related To Ventricular Late Potentials en_US
dc.title Comparative analysis on wavelet-based detection of finite duration low-amplitude signals related to ventricular late potentials tr_TR
dc.type Article en_US
dspace.entity.type Publication
gdc.author.scopusid 7102595144
gdc.author.scopusid 7101628003
gdc.author.wosid Yilmaz, Atila/D-4993-2013
gdc.bip.impulseclass C5
gdc.bip.influenceclass C4
gdc.bip.popularityclass C5
gdc.coar.access metadata only access
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.department Çankaya University en_US
gdc.description.departmenttemp Cankaya Univ, Dept Comp Engn, Ankara, Turkey; Hacettepe Univ, Dept Elect Engn, Ankara, Turkey en_US
gdc.description.endpage 1457 en_US
gdc.description.issue 6 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q2
gdc.description.startpage 1443 en_US
gdc.description.volume 25 en_US
gdc.description.woscitationindex Science Citation Index Expanded
gdc.description.wosquality Q2
gdc.identifier.openalex W2009085052
gdc.identifier.pmid 15712723
gdc.identifier.wos WOS:000226591400010
gdc.index.type WoS
gdc.index.type Scopus
gdc.index.type PubMed
gdc.oaire.diamondjournal false
gdc.oaire.impulse 2.0
gdc.oaire.influence 3.3142329E-9
gdc.oaire.isgreen false
gdc.oaire.keywords Time Factors
gdc.oaire.keywords Myocardial Infarction
gdc.oaire.keywords Reproducibility of Results
gdc.oaire.keywords Signal Processing, Computer-Assisted
gdc.oaire.keywords Prognosis
gdc.oaire.keywords Risk Assessment
gdc.oaire.keywords Sensitivity and Specificity
gdc.oaire.keywords Pattern Recognition, Automated
gdc.oaire.keywords Electrocardiography
gdc.oaire.keywords Risk Factors
gdc.oaire.keywords Tachycardia, Ventricular
gdc.oaire.keywords Humans
gdc.oaire.keywords Diagnosis, Computer-Assisted
gdc.oaire.keywords Algorithms
gdc.oaire.popularity 5.6748894E-10
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 03 medical and health sciences
gdc.oaire.sciencefields 0302 clinical medicine
gdc.openalex.collaboration National
gdc.openalex.fwci 0.6214
gdc.openalex.normalizedpercentile 0.73
gdc.opencitations.count 6
gdc.plumx.crossrefcites 6
gdc.plumx.mendeley 5
gdc.plumx.pubmedcites 1
gdc.plumx.scopuscites 8
gdc.publishedmonth 12
gdc.scopus.citedcount 8
gdc.virtual.author Mousa, Ayad
gdc.wos.citedcount 8
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