Estimation and Modeling of QT-Interval Adaptation to Heart Rate Changes

This paper introduces a new method for QT-interval estimation. It consists in a batch processing mode of the improved Woody's method. Performance of this methodology is evaluated using synthetic data. In parallel, a new model of QT-interval dynamics behavior related to heart rate changes is pre...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2012-04, Vol.59 (4), p.956-965
Main Authors: Cabasson, Aline, Meste, Olivier, Vesin, Jean-Marc
Format: Article
Language:English
Subjects:
Adaptation models
Algorithms
Batch processing
Bioengineering
Biological and medical sciences
Computer Science
Computerized, statistical medical data processing and models in biomedicine
Diagnosis, Computer-Assisted
Diagnosis, Computer-Assisted - methods
Electrocardiography
Electrocardiography (ECG)
Electrocardiography - methods
Electrocardiography. Vectocardiography
Electrodiagnosis. Electric activity recording
Engineering Sciences
Estimation
Heart Rate
Heart Rate - physiology
Humans
Investigative techniques, diagnostic techniques (general aspects)
Life Sciences
Medical management aid. Diagnosis aid
Medical sciences
modeling
Models and simulation
Pattern Recognition, Automated
Pattern Recognition, Automated - methods
QT adaptation
QT estimation
QT intervals
Reproducibility of Results
Sensitivity and Specificity
Signal and Image processing
Studies
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Summary:This paper introduces a new method for QT-interval estimation. It consists in a batch processing mode of the improved Woody's method. Performance of this methodology is evaluated using synthetic data. In parallel, a new model of QT-interval dynamics behavior related to heart rate changes is presented. Since two kinds of QT response have been pointed out, the main idea is to split the modeling process into two steps: 1) the modeling of the fast adaptation, which is inspired by the electrical behavior at the cellular level relative to the electrical restitution curve, and 2) the modeling of the slow adaptation, inspired by experimental works at the cellular level. Both approaches are based on a low-complexity autoregressive process whose parameters are estimated using an unbiased estimator. This new modeling of QT adaptation, combined with the presented QT-estimation process, is applied to several ECG recordings with various heart rate variability dynamics. Its potential is then illustrated on ECG recorded during rest, atrial fibrillation episodes, and exercise. Meaningful results in agreement with physiological knowledge at the cellular level are obtained.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2011.2181507