Quality estimation of the electrocardiogram using cross-correlation among leads
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12938_2015_Article_53.pdf
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7.19 MB
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Checksum (MD5)
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Author(s)
Morgado, Eduardo
Alonso-Atienza, Felipe
Santiago-Mozos, Ricardo
Silva, Ikaro
Ramos, Javier
Barquero-Perez, Oscar
Mark, Roger G
Date Issued
June 2015
Journal
BioMedical Engineering OnLine
Publisher
BioMed Central
Citation
Morgado, Eduardo, Felipe Alonso-Atienza, Ricardo Santiago-Mozos, Oscar Barquero-Perez, Ikaro Silva, Javier Ramos, and Roger Mark. “Quality Estimation of the Electrocardiogram Using Cross-Correlation Among Leads.” BioMedical Engineering OnLine 14, no. 1 (June 20, 2015).
Version
Final published version
Abstract
Background
Fast and accurate quality estimation of the electrocardiogram (ECG) signal is a relevant research topic that has attracted considerable interest in the scientific community, particularly due to its impact on tele-medicine monitoring systems, where the ECG is collected by untrained technicians. In recent years, a number of studies have addressed this topic, showing poor performance in discriminating between clinically acceptable and unacceptable ECG records.
Methods
This paper presents a novel, simple and accurate algorithm to estimate the quality of the 12-lead ECG by exploiting the structure of the cross-covariance matrix among different leads. Ideally, ECG signals from different leads should be highly correlated since they capture the same electrical activation process of the heart. However, in the presence of noise or artifacts the covariance among these signals will be affected. Eigenvalues of the ECG signals covariance matrix are fed into three different supervised binary classifiers.
Results and conclusion
The performance of these classifiers were evaluated using PhysioNet/CinC Challenge 2011 data. Our best quality classifier achieved an accuracy of 0.898 in the test set, while having a complexity well below the results of contestants who participated in the Challenge, thus making it suitable for implementation in current cellular devices.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. School of Engineering
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s12938-015-0053-1
