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Sensors 2017, 17(11), 2448; https://doi.org/10.3390/s17112448

Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring

1
Cardiology Service, Arrhythmia Unit, Hospital General Universitario Virgen de la Arrixaca, El Palmar, Murcia 30120, Spain
2
Department of Signal Theory and Communications, University of de Alcalá, Alcalá de Henares, Madrid 28805, Spain
3
Department of Signal Theory and Communications, Miguel Hernández University, Elche, Alicante 03202, Spain
4
Instituto de Neurociencias, Miguel Hernández University–CSIC, Alicante 03550, Spain
5
Department of Signal Theory and Communications, Rey Juan Carlos University, Fuenlabrada, Madrid 28943, Spain
6
Center for Computational Simulation, Universidad Politécnica de Madrid, Boadilla, Madrid 28223, Spain
*
Author to whom correspondence should be addressed.
Received: 17 September 2017 / Revised: 15 October 2017 / Accepted: 20 October 2017 / Published: 25 October 2017
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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Abstract

Noise and artifacts are inherent contaminating components and are particularly present in Holter electrocardiogram (ECG) monitoring. The presence of noise is even more significant in long-term monitoring (LTM) recordings, as these are collected for several days in patients following their daily activities; hence, strong artifact components can temporarily impair the clinical measurements from the LTM recordings. Traditionally, the noise presence has been dealt with as a problem of non-desirable component removal by means of several quantitative signal metrics such as the signal-to-noise ratio (SNR), but current systems do not provide any information about the true impact of noise on the ECG clinical evaluation. As a first step towards an alternative to classical approaches, this work assesses the ECG quality under the assumption that an ECG has good quality when it is clinically interpretable. Therefore, our hypotheses are that it is possible (a) to create a clinical severity score for the effect of the noise on the ECG, (b) to characterize its consistency in terms of its temporal and statistical distribution, and (c) to use it for signal quality evaluation in LTM scenarios. For this purpose, a database of external event recorder (EER) signals is assembled and labeled from a clinical point of view for its use as the gold standard of noise severity categorization. These devices are assumed to capture those signal segments more prone to be corrupted with noise during long-term periods. Then, the ECG noise is characterized through the comparison of these clinical severity criteria with conventional quantitative metrics taken from traditional noise-removal approaches, and noise maps are proposed as a novel representation tool to achieve this comparison. Our results showed that neither of the benchmarked quantitative noise measurement criteria represent an accurate enough estimation of the clinical severity of the noise. A case study of long-term ECG is reported, showing the statistical and temporal correspondences and properties with respect to EER signals used to create the gold standard for clinical noise. The proposed noise maps, together with the statistical consistency of the characterization of the noise clinical severity, paves the way towards forthcoming systems providing us with noise maps of the noise clinical severity, allowing the user to process different ECG segments with different techniques and in terms of different measured clinical parameters. View Full-Text
Keywords: noise maps; ECG; noise clinical severity; Holter; external event recorder; long-term monitoring; noise bars noise maps; ECG; noise clinical severity; Holter; external event recorder; long-term monitoring; noise bars
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Everss-Villalba, E.; Melgarejo-Meseguer, F.M.; Blanco-Velasco, M.; Gimeno-Blanes, F.J.; Sala-Pla, S.; Rojo-Álvarez, J.L.; García-Alberola, A. Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring. Sensors 2017, 17, 2448.

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