Curve registration for study of P-wave morphing during exercise
The P-wave shape analysis remains an interesting objective in various situations. In this paper, the aim is to investigate how the P-wave morphology changes during time-varying effort. To perform this task, a method, Self-Modeling Registration (SMR), derived from the Curve Registration (CR) theory is used. This method permits to link different shapes by mean of time warping functions. First, using the SMR method, significant P-wave shapes are extracted from two ECG records under maximal and graded exercise test where the effort is released abruptly. Then, the selected shapes are linked by warping functions estimated by SMR method. The obtained warping functions show a reversible evolution of the P-wave shape independently of the possible superimposition of the T-wave. To give a physiological explanation of this P-wave morphing, an atrial contribution evolution scenario under exercise is proposed and validated by simulation.
P-wave shape changes observed in the surface electrocardiogram of subjects with obstructive sleep apnoea
Automated detection of sleep apnoea from the surface ECG is a challenge with potentially significant clinical benefit. The objective of this study is to explore a possible relation between P-wave shape changes and the presence of Obstructive Sleep Apnoea (OSA) episodes in ECG records. Our working hypothesis is that apnoea-related phenomena (e.g., hypoxia, changes in intra-thoracic pressure) influence the depolarisation dynamics of the atria, and hence lead to detectable shape changes in the surface P-wave. For this purpose, a P-wave shape clustering algorithm is proposed. It uses the k-means method, an unsupervised classifier, coupled with a shape metric, the CISA (Corrected Integral Shape Averaging) distance, to distinguish apnoeic and non-apnoeic epochs. The method was applied to perform two-clustering on P-wave shapes extracted from ECG segments. The results obtained indicate that shape information from the ECG can be used in a classification approach for OSA detection. The study also raises the question of elucidating the mechanisms of how OSA may alter P-wave shape
Selective versus non-selective antiarrhythmic approach for prevention of atrial fibrillation after coronary surgery: is there a need for pre-operative risk stratification? A prospective placebo-controlled study using low-dose sotalol.
AIM
This study evaluated the advantages of 'selective' over 'non-selective' antiarrhythmic prevention of atrial fibrillation after coronary surgery based on a new risk prediction algorithm.


METHODS AND RESULTS
In a retrospective analysis of a prospective randomized trial, a model for risk prediction was determined based on clinical data of the control group (A; n = 107) and tested in a test group (B; n = 107, treated with low dose sotalol). Using this algorithm, the effect of a 'selective' antiarrhythmic approach in high-risk patients was compared to a 'non-selective' approach, where all patients were treated. In total, 75 (35%) patients developed atrial fibrillation and 14 (7%) side-effects led to discontinuation of study medication. Based on the risk prediction algorithm, 36% of group A patients were classified as high-risk patients with an incidence of atrial fibrillation of 76% compared to 26% in low-risk patients (P < 0.0001). The selective approach, i.e. treatment of high-risk patients only reduced the incidence of atrial fibrillation from 76% to 50% (P = 0.0295) compared to a reduction from 44% to 26% (P = 0.0065) when all patients were treated. More importantly, with the non-selective approach 100% of patients were exposed to the possible side-effects of sotalol and costs compared to 24% only with the selective approach (P < 0.0001).


CONCLUSIONS
Thus, a selective approach based on a clinical risk prediction algorithm should improve the cost-effectiveness and safety of low-dose sotalol in the prevention of atrial fibrillation after coronary bypass surgery.