Resumen: False 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. Idioma: Inglés DOI: 10.1088/0967-3334/37/8/1370 Año: 2016 Publicado en: PHYSIOLOGICAL MEASUREMENT 37, 8 (2016), 1370-1382 ISSN: 0967-3334 Factor impacto JCR: 2.058 (2016) Categ. JCR: ENGINEERING, BIOMEDICAL rank: 37 / 77 = 0.481 (2016) - Q2 - T2 Categ. JCR: PHYSIOLOGY rank: 50 / 84 = 0.595 (2016) - Q3 - T2 Categ. JCR: BIOPHYSICS rank: 49 / 73 = 0.671 (2016) - Q3 - T3 Factor impacto SCIMAGO: 0.688 - Biomedical Engineering (Q2) - Biophysics (Q2) - Physiology (Q3) - Physiology (medical) (Q3)