Low-complexity autoregressive modeling of the fast and slow QT adaptation to heart rate changes.

The aim is to develop a new model of the QT interval dynamics behavior related to heart rate changes. 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, 2) the modeling of the "slow" adaptation, inspired by experiments works at the cellular level. Both are modeled as low-complexity autoregressive process whose parameters are computed using an unbiased estimator. The relevance of this approach is illustrated on several ECG recordings where the variations of the heart rate are various (rest, atrial fibrillation episodes, exercise). Significant results are obtained in agreement with the physiological knowledge at the cellular level.

A new modeling of the overlapping T wave for the efficient estimation of the P-R intervals during exercise and recovery.

The analysis of the heart period series is a difficult task especially under graded exercise conditions. Having a good tool to characterize the P-R and R-R intervals, i.e. a good method of time delay estimation, would carry out a better knowledge of the neural activity during exercise and recovery in the field of pacemaker's design. Unfortunately, for the P-R intervals, the problem of estimation has been rarely addressed. In this paper, we propose a new method for estimating the P-R intervals based on an iterative Maximum-Likelihood approach. The main contribution is to take into account the overlapping T wave on ECG recorded during exercise. The goal of this study is to compute a model of the T wave which overlaps the P wave and then to cancel this influence before the determination of the P-R intervals.