title: Quality estimation of the electrocardiogram using cross-correlation among leads.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Signal Theory and Communications, Rey Juan Carlos University, Camino del Molino s/n, 28943, Fuenlabrada, Madrid, Spain. eduardo.morgado@urjc.es.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Morgado
authors.affiliation: Department of Signal Theory and Communications, Rey Juan Carlos University, Camino del Molino s/n, 28943, Fuenlabrada, Madrid, Spain. felipe.alonso@urjc.es.
authors.forename: Felipe
authors.initials: F
authors.lastname: Alonso-Atienza
authors.affiliation: Department of Signal Theory and Communications, Rey Juan Carlos University, Camino del Molino s/n, 28943, Fuenlabrada, Madrid, Spain. ricardo.santiago@urjc.es.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Santiago-Mozos
authors.affiliation: Department of Signal Theory and Communications, Rey Juan Carlos University, Camino del Molino s/n, 28943, Fuenlabrada, Madrid, Spain. oscar.barquero@urjc.es.
authors.forename: Óscar
authors.initials: Ó
authors.lastname: Barquero-Pérez
authors.affiliation: Laboratory for Computational Physiology, MIT, 77 Massachusetts Ave, Cambridge, 02139, MA, USA. ikaro@mit.edu.
authors.forename: Ikaro
authors.initials: I
authors.lastname: Silva
authors.affiliation: Department of Signal Theory and Communications, Rey Juan Carlos University, Camino del Molino s/n, 28943, Fuenlabrada, Madrid, Spain. javier.ramos@urjc.es.
authors.forename: Javier
authors.initials: J
authors.lastname: Ramos
authors.affiliation: Laboratory for Computational Physiology, MIT, 77 Massachusetts Ave, Cambridge, 02139, MA, USA. rgmark@mit.edu.
authors.forename: Roger
authors.initials: R
authors.lastname: Mark
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004562:Electrocardiography; D006801:Humans; D011786:Quality Control; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D000069553:Supervised Machine Learning