Title:
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Segmented Symbolic Dynamics for Risk Stratification in Patients with Ischemic Heart Failure, Cardiovascular Engineering and Technology
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Author:
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Voss, Andreas; Schroeder, Rico; Caminal Magrans, Pere; Vallverdú Ferrer, Montserrat; Brunel, Helena; Cygankiewicz, I.; Vázquez, Rafael; Bayes de Luna, Antonio
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Other authors:
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Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics |
Abstract:
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Chronic heart failure (CHF) is recognized as
major and escalating public health problem. Approximately
69% of CHF patients suffer from cardiac death within
5 years after the initial diagnosis. Until now, no generally
accepted ECG risk predictors in CHF patients are available.
The objective of this study was to investigate the suitability of
the new developed non-linear method segmented symbolic
dynamics (SSD) for risk stratification in patients with
ischemic cardiomyopathy (ICM) in comparison to other
indices from time and frequency domain, non-linear dynamics,
and clinical markers. Twenty-four hour Holter ECGs
were recorded from 256 ICM patients. Heart rate variability
(HRV) was analyzed from the filtered beat-to-beat interval
time series. For calculating SSD, NN interval time series
were segmented in 1 min overlapping windows with a
window length of 30 min. For each window a symbol- and
word-transformation was performed and probabilities of
word type occurrences were calculated. Several indices from
frequency domain and non-linear dynamics revealed high
univariate significant differences (p<0.01) discriminating
low (n = 221) and high risk ICM patients (n = 35). For
multivariate risk stratification in ICM patients the two
optimal mixed parameter sets consisting of either two clinical
and three non-clinical indices (two from SSD) or three
clinical and two non-clinical indices (one from SSD) achieved
74 and 75% sensitivity and 79 and 76% specificity, respectively.
These results suggest that the new SSD enhances
considerably risk stratification in ICM patients. The multivariate
analysis including SSD leads to an optimum accuracy
of 81%. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Automàtica i control -Informatica -Dinàmica -Sistema cardiovascular |
Rights:
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Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Document type:
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Article - Published version Article |
Published by:
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Springer
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