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Title: Characterizing artifacts in RR stress test time series
Authors: Astudillo Salinas, Darwin Fabián
Medina Molina, Ruben
Palacio Baus, Kenneth Samuel
Solano Quinde, Lizandro Damian
Wong De Balzan, Sara
metadata.dc.ucuenca.nombrerevista: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC 2016
Issue Date: 16-Aug-2016
metadata.dc.ucuenca.embargoend: 1-Jan-2022
metadata.dc.ucuenca.volumen: 2016-October
metadata.dc.source: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
metadata.dc.identifier.doi: 10.1109/EMBC.2016.7590796
Orlando Florida
metadata.dc.type: Article
Electrocardiographic 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.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009064458&doi=10.1109%2fEMBC.2016.7590796&partnerID=40&md5=35f14b3a2bc7631b2c524ca31377d60b
ISBN: 9781457702204
ISSN: 1557170X
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