Performance Evaluation of QT-RR Adaptation Time Lag Estimation in Exercise Stress Testing
(2024) In IEEE Transactions on Biomedical Engineering p.1-12- Abstract
<bold><italic>Background:</italic></bold> Slower adaptation of the QT interval to sudden changes in heart rate has been identified as a risk marker of ventricular arrhythmia. The gradual changes observed in exercise stress testing facilitates the estimation of the QT-RR adaptation time lag. <bold><italic>Methods:</italic></bold> The time lag estimation is based on the delay between the observed QT intervals and the QT intervals derived from the observed RR intervals using a memoryless transformation. Assuming that the two types of QT interval are corrupted with either Gaussian or Laplacian noise, the respective maximum likelihood time... (More)
<bold><italic>Background:</italic></bold> Slower adaptation of the QT interval to sudden changes in heart rate has been identified as a risk marker of ventricular arrhythmia. The gradual changes observed in exercise stress testing facilitates the estimation of the QT-RR adaptation time lag. <bold><italic>Methods:</italic></bold> The time lag estimation is based on the delay between the observed QT intervals and the QT intervals derived from the observed RR intervals using a memoryless transformation. Assuming that the two types of QT interval are corrupted with either Gaussian or Laplacian noise, the respective maximum likelihood time lag estimators are derived. Estimation performance is evaluated using an ECG simulator which models change in RR and QT intervals with a known time lag, muscle noise level, respiratory rate, and more. The accuracy of T-wave end delineation and the influence of the learning window positioning for model parameter estimation are also investigated. <bold><italic>Results:</italic></bold> Using simulated datasets, the results show that the proposed approach to estimation can be applied to any changes in heart rate trend as long as the frequency content of the trend is below a certain frequency. Moreover, using a proper position of the learning window for exercise so that data compensation reduces the effect of nonstationarity, a lower mean estimation error results for a wide range of time lags. Using a clinical dataset, the Laplacian-based estimator shows a better discrimination between patients grouped according to the risk of suffering from coronary artery disease. <bold><italic>Conclusions</italic></bold>: Using simulated ECGs, the performance evaluation of the proposed method shows that the estimated time lag agrees well with the true time lag.
(Less)
- author
- Perez, Cristina ; Pueyo, Esther ; Martinez, Juan Pablo ; Viik, Jari ; Sornmo, Leif LU and Laguna, Pablo
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- in press
- subject
- keywords
- Adaptation models, coronary artery disease, Electrocardiography, Estimation, exercise stress testing, Heart rate, Market research, QT-RR adaptation time lag, QT-RR modeling, simulated ECGs, Stress, Testing
- in
- IEEE Transactions on Biomedical Engineering
- pages
- 12 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85195362509
- pmid:38837931
- ISSN
- 0018-9294
- DOI
- 10.1109/TBME.2024.3410008
- language
- English
- LU publication?
- yes
- id
- 911d563e-db93-477d-b1ab-1b9aa0e6c4ba
- date added to LUP
- 2024-09-16 11:12:20
- date last changed
- 2024-09-16 11:13:22
@article{911d563e-db93-477d-b1ab-1b9aa0e6c4ba,
abstract = {{<p><bold><italic>Background:</italic></bold> Slower adaptation of the QT&#x00A0;interval to sudden changes in heart rate has been identified as a risk marker of ventricular arrhythmia. The gradual changes observed in exercise stress testing facilitates the estimation of the QT-RR adaptation time lag. <bold><italic>Methods:</italic></bold> The time lag estimation is based on the delay between the observed QT&#x00A0;intervals and the QT&#x00A0;intervals derived from the observed RR&#x00A0;intervals using a memoryless transformation. Assuming that the two types of QT&#x00A0;interval are corrupted with either Gaussian or Laplacian noise, the respective maximum likelihood time lag estimators are derived. Estimation performance is evaluated using an ECG simulator which models change in RR and QT intervals with a known time lag, muscle noise level, respiratory rate, and more. The accuracy of T-wave end delineation and the influence of the learning window positioning for model parameter estimation are also investigated. <bold><italic>Results:</italic></bold> Using simulated datasets, the results show that the proposed approach to estimation can be applied to any changes in heart rate trend as long as the frequency content of the trend is below a certain frequency. Moreover, using a proper position of the learning window for exercise so that data compensation reduces the effect of nonstationarity, a lower mean estimation error results for a wide range of time lags. Using a clinical dataset, the Laplacian-based estimator shows a better discrimination between patients grouped according to the risk of suffering from coronary artery disease. <bold><italic>Conclusions</italic></bold>: Using simulated ECGs, the performance evaluation of the proposed method shows that the estimated time lag agrees well with the true time lag.</p>}},
author = {{Perez, Cristina and Pueyo, Esther and Martinez, Juan Pablo and Viik, Jari and Sornmo, Leif and Laguna, Pablo}},
issn = {{0018-9294}},
keywords = {{Adaptation models; coronary artery disease; Electrocardiography; Estimation; exercise stress testing; Heart rate; Market research; QT-RR adaptation time lag; QT-RR modeling; simulated ECGs; Stress; Testing}},
language = {{eng}},
pages = {{1--12}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Biomedical Engineering}},
title = {{Performance Evaluation of QT-RR Adaptation Time Lag Estimation in Exercise Stress Testing}},
url = {{http://dx.doi.org/10.1109/TBME.2024.3410008}},
doi = {{10.1109/TBME.2024.3410008}},
year = {{2024}},
}