Laboratory of Informatics, Signals and Systems (I3S), Centre National de laRecherche Scientifique (CNRS) University of Nice-Sophia Antipolis, Sophia Antipolis, France.
IEEE Trans Biomed Eng. 2012 Apr;59(4):956-65. doi: 10.1109/TBME.2011.2181507. Epub 2011 Dec 22.
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.
本文提出了一种新的 QT 间期估计方法。它由 Woody 方法的批处理模式组成。使用合成数据评估了该方法的性能。同时，提出了一种与心率变化相关的 QT 间期动力学行为的新模型。由于已经指出了两种 QT 反应，因此主要思想是将建模过程分为两个步骤：1）快速适应建模，该建模受细胞水平上与电恢复曲线相对应的电行为的启发，以及 2）受细胞水平上的实验工作启发的慢适应建模。这两种方法都基于一个低复杂度的自回归过程，其参数使用无偏估计器进行估计。将 QT 适应的这种新建模与所提出的 QT 估计过程结合起来，应用于具有各种心率变异性动力学的多个 ECG 记录。然后在休息、心房颤动发作和运动期间记录的 ECG 上说明了其潜力。得到了与细胞水平上的生理知识相符的有意义的结果。
