Astudillo Salinas, Darwin FabiánMedina Molina, RubenPalacio Baus, Kenneth SamuelSolano Quinde, Lizandro DamiánWong De Balzan, Sara2018-01-112018-01-112016-08-1697814577022041557170Xhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85009064458&doi=10.1109%2fEMBC.2016.7590796&partnerID=40&md5=35f14b3a2bc7631b2c524ca31377d60bhttp://dspace.ucuenca.edu.ec/handle/123456789/29258Electrocardiographic stress test records have a lot of artifacts. In this paper we explore a simple method to characterize the amount of artifacts present in unprocessed RR stress test time series. Four time series classes were defined: Very good lead, Good lead, Low quality lead and Useless lead. 65 ECG, 8 lead, records of stress test series were analyzed. Firstly, RR-time series were annotated by two experts. The automatic methodology is based on dividing the RR-time series in non-overlapping windows. Each window is marked as noisy whenever it exceeds an established standard deviation threshold (SDT). Series are classified according to the percentage of windows that exceeds a given value, based upon the first manual annotation. Different SDT were explored. Results show that SDT close to 20% (as a percentage of the mean) provides the best results. The coincidence between annotators classification is 70.77% whereas, the coincidence between the second annotator and the automatic method providing the best matches is larger than 63%. Leads classified as Very good leads and Good leads could be combined to improve automatic heartbeat labeling.en-USArticle10.1109/EMBC.2016.7590796