A new method for QT-interval estimation and modeling of fast and slow adaptation to heart rate changes yielded meaningful results in agreement with physiological knowledge on ECG recordings.
A novel computational method for estimating and modeling QT-interval adaptation to heart rate changes provides results consistent with known cellular electrophysiology.
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.
Cabasson et al. (Fri,) reported a other. New method for QT-interval estimation and modeling was evaluated on Performance of QT-interval estimation and modeling. A new method for QT-interval estimation and modeling of fast and slow adaptation to heart rate changes yielded meaningful results in agreement with physiological knowledge on ECG recordings.