Evaluation of a QT Adaptation Time Estimator for ECG Exercise Stress Test in Controlled Simulation
(2023) 50th Computing in Cardiology, CinC 2023 In Computing in Cardiology- Abstract
Slowed adaptation of the QT interval to sudden abrupt heart rate (HR) changes has been identified as a marker of ventricular arrhythmic risk. However, abrupt HR changes are difficult to induce in patients. Quantifying the QT adaptation time in gradual HR changes, as observed in ECGs recording during an exercise stress test, has been recently proposed. The time lag between the QT series and an instantaneous memoryless HR-dependent QT series along stress test was computed as QT memory. Here, this method was evaluated in a control scenario using simulated exercise stress test ECG signals presenting different QT adaptation times. The method robustness was studied by contaminating the ECGs with muscular noise (MN) signals with different... (More)
Slowed adaptation of the QT interval to sudden abrupt heart rate (HR) changes has been identified as a marker of ventricular arrhythmic risk. However, abrupt HR changes are difficult to induce in patients. Quantifying the QT adaptation time in gradual HR changes, as observed in ECGs recording during an exercise stress test, has been recently proposed. The time lag between the QT series and an instantaneous memoryless HR-dependent QT series along stress test was computed as QT memory. Here, this method was evaluated in a control scenario using simulated exercise stress test ECG signals presenting different QT adaptation times. The method robustness was studied by contaminating the ECGs with muscular noise (MN) signals with different Signal-to-Noise ratio (SNR) values, either synthetic or extracted from real recordings. We found that delineation of the T-wave end point in the first transformed lead from Periodic Component Analysis offers the best performance for low SNR. Moreover, we confirmed that the estimator provides an unbiased estimate of the QT memory introduced in the simulations for the studied range of SNR values (25 to 50 dB).
(Less)
- author
- Perez, Cristina ; Pueyo, Esther ; Martinez, Juan Pablo ; Sornmo, Leif LU and Laguna, Pablo
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Computing in Cardiology, CinC 2023
- series title
- Computing in Cardiology
- publisher
- IEEE Computer Society
- conference name
- 50th Computing in Cardiology, CinC 2023
- conference location
- Atlanta, United States
- conference dates
- 2023-10-01 - 2023-10-04
- external identifiers
-
- scopus:85182341581
- ISSN
- 2325-887X
- 2325-8861
- ISBN
- 9798350382525
- DOI
- 10.22489/CinC.2023.235
- language
- English
- LU publication?
- yes
- id
- 1f6e5039-5021-4056-bc5a-bca27f1a5850
- date added to LUP
- 2024-02-15 14:20:17
- date last changed
- 2024-04-16 13:22:27
@inproceedings{1f6e5039-5021-4056-bc5a-bca27f1a5850,
abstract = {{<p>Slowed adaptation of the QT interval to sudden abrupt heart rate (HR) changes has been identified as a marker of ventricular arrhythmic risk. However, abrupt HR changes are difficult to induce in patients. Quantifying the QT adaptation time in gradual HR changes, as observed in ECGs recording during an exercise stress test, has been recently proposed. The time lag between the QT series and an instantaneous memoryless HR-dependent QT series along stress test was computed as QT memory. Here, this method was evaluated in a control scenario using simulated exercise stress test ECG signals presenting different QT adaptation times. The method robustness was studied by contaminating the ECGs with muscular noise (MN) signals with different Signal-to-Noise ratio (SNR) values, either synthetic or extracted from real recordings. We found that delineation of the T-wave end point in the first transformed lead from Periodic Component Analysis offers the best performance for low SNR. Moreover, we confirmed that the estimator provides an unbiased estimate of the QT memory introduced in the simulations for the studied range of SNR values (25 to 50 dB).</p>}},
author = {{Perez, Cristina and Pueyo, Esther and Martinez, Juan Pablo and Sornmo, Leif and Laguna, Pablo}},
booktitle = {{Computing in Cardiology, CinC 2023}},
isbn = {{9798350382525}},
issn = {{2325-887X}},
language = {{eng}},
publisher = {{IEEE Computer Society}},
series = {{Computing in Cardiology}},
title = {{Evaluation of a QT Adaptation Time Estimator for ECG Exercise Stress Test in Controlled Simulation}},
url = {{http://dx.doi.org/10.22489/CinC.2023.235}},
doi = {{10.22489/CinC.2023.235}},
year = {{2023}},
}