000075591 001__ 75591
000075591 005__ 20200221144212.0
000075591 0247_ $$2doi$$a10.1088/0967-3334/37/8/1370
000075591 0248_ $$2sideral$$a95651
000075591 037__ $$aART-2016-95651
000075591 041__ $$aeng
000075591 100__ $$aDaluwatte, C.
000075591 245__ $$aAssessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs
000075591 260__ $$c2016
000075591 5060_ $$aAccess copy available to the general public$$fUnrestricted
000075591 5203_ $$aFalse and non-actionable alarms in critical care can be reduced by developing algorithms which assess the trueness of an arrhythmia alarm from a bedside monitor. Computational approaches that automatically identify artefacts in ECG signals are an important branch of physiological signal processing which tries to address this issue. Signal quality indices (SQIs) derived considering differences between artefacts which occur in ECG signals and normal QRS morphology have the potential to discriminate pathologically different arrhythmic ECG segments as artefacts. Using ECG signals from the PhysioNet/Computing in Cardiology Challenge 2015 training set, we studied previously reported ECG SQIs in the scientific literature to differentiate ECG segments with artefacts from arrhythmic ECG segments. We found that the ability of SQIs to discriminate between ECG artefacts and arrhythmic ECG varies based on arrhythmia type since the pathology of each arrhythmic ECG waveform is different. Therefore, to reduce the risk of SQIs classifying arrhythmic events as noise it is important to validate and test SQIs with databases that include arrhythmias. Arrhythmia specific SQIs may also minimize the risk of misclassifying arrhythmic events as noise.
000075591 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000075591 590__ $$a2.058$$b2016
000075591 591__ $$aENGINEERING, BIOMEDICAL$$b37 / 77 = 0.481$$c2016$$dQ2$$eT2
000075591 591__ $$aPHYSIOLOGY$$b50 / 84 = 0.595$$c2016$$dQ3$$eT2
000075591 591__ $$aBIOPHYSICS$$b49 / 73 = 0.671$$c2016$$dQ3$$eT3
000075591 592__ $$a0.688$$b2016
000075591 593__ $$aBiomedical Engineering$$c2016$$dQ2
000075591 593__ $$aBiophysics$$c2016$$dQ2
000075591 593__ $$aPhysiology$$c2016$$dQ3
000075591 593__ $$aPhysiology (medical)$$c2016$$dQ3
000075591 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000075591 700__ $$aJohannesen, L.
000075591 700__ $$aGaleotti, L.
000075591 700__ $$0(orcid)0000-0001-9963-1205$$aVicente, J.
000075591 700__ $$aStrauss, D.G.
000075591 700__ $$aScully, C.G.
000075591 773__ $$g37, 8 (2016), 1370-1382$$pPhysiol. meas.$$tPHYSIOLOGICAL MEASUREMENT$$x0967-3334
000075591 8564_ $$s708485$$uhttps://zaguan.unizar.es/record/75591/files/texto_completo.pdf$$yPostprint
000075591 8564_ $$s86086$$uhttps://zaguan.unizar.es/record/75591/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000075591 909CO $$ooai:zaguan.unizar.es:75591$$particulos$$pdriver
000075591 951__ $$a2020-02-21-13:12:13
000075591 980__ $$aARTICLE