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Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/29154
Title: Semiautomatic validation of RR time series in an ECG stress test database
Authors: Armijos, J
Astudillo Salinas, Darwin Fabián
Garciá, D
Medina Molina, Ruben
Palacio Baus, Kenneth Samuel
Wong De Balzan, Sara
metadata.dc.ucuenca.nombrerevista: 11th International Symposium on Medical Information Processing and Analysis SIPAIM 2015
Keywords: Cardiovascular Autonomic Neuropathy
Stress Test Ecg
Issue Date: 17-Nov-2015
metadata.dc.ucuenca.embargoend: 1-Jan-2022
metadata.dc.ucuenca.volumen: 9681
metadata.dc.source: Proceedings of SPIE - The International Society for Optical Engineering
metadata.dc.identifier.doi: 10.1117/12.2214314
Publisher: SPIE
metadata.dc.type: Article
This paper reports an automatic method for characterizing the quality of the RR-time series in the stress test database known as DICARDIA. The proposed methodology is simple and consists in subdividing the RR time series in a set of windows for estimating the quantity of artifacts based on a threshold value that depends on the standard deviation of RR-time series for each recorded lead. In a first stage, a manual annotation was performed considering four quality classes for the RR-time series (Reference lead, Good Lead, Low Quality Lead and Useless Lead). Automatic annotation was then performed varying the number of windows and threshold value for the standard deviation of the RR-time series. The metric used for evaluating the quality of the annotation was the Matching Ratio. The best results were obtained using a higher number of windows and considering only three classes (Good Lead, Low Quality Lead and Useless). The proposed methodology allows the utilization of the online available DICARDIA Stress Test database for different types of research.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958213726&doi=10.1117%2f12.2214314&partnerID=40&md5=177e520d9d022f29ac7b887163a4436b
ISBN: 9781628419160
ISSN: 0277786X
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