The present study demonstrates that pulse rate parametrization using a consumer smart wristband is in principle feasible. The results show that the smart wristband is well suited for computing basic PRV parameters which have been reported to be associated with poorer health outcomes. In addition, the study introduces a methodology for the estimation of post-exercise heart recovery time and the heart's adaptation to physical workload during free-living activities.

Physical activity session frequency and distribution over time may play a significant role on survival after major cardiovascular events. However, the existing amount-based metrics do not account for these properties, thus the physical activity pattern is not fully evaluated. The aim of this work is to introduce a metric which accounts for the difference between the actual and uniform distribution of physical activity, thus its value depends on physical activity aggregation over time. The practical application is demonstrated on a step data from 40 participants, half of them diagnosed with chronic cardiovascular disease (CVD). The metric is capable of discriminating among different daily patterns, including going to and from work, walking in a park and being active the entire day. Moreover, the results demonstrate the tendency of CVD patients being associated with higher aggregation values, suggesting that CVD patients spend more time in a sedentary behaviour compared to healthy participants. By combining the aggregation with the intensity metric, such common weekly patterns as inactivity, regular activity and “weekend warrior” can be captured. The metric is expected to have clinical relevance since it may provide additional information on the relationship between physical activity pattern and health outcomes.

Flow velocity in left atrial appendage decreases when paroxysmal atrial fibrillation (PAF) progresses to longer episodes, suggesting that the temporal PAF episode pattern may be related to risk of thrombus formation. This study investigates the feasibility of discriminating episode patterns based on two descriptors: the aggregation characterizes the temporal distribution of PAF episodes, whereas the Gini coefficient characterizes differences in episode duration. The descriptors were studied on three PhysioNet databases with annotated PAF episodes, resulting in a total of 102 recordings. Three types of patterns were defined: congregation of several episodes in a single and multiple clusters, and episodes dispersed over the entire monitoring period. The results show that the aggregation descriptor achieves large values for patterns with a single and multiple clusters (0.76 ± 0.07 and 0.60 ± 0.08, respectively). In contrast, much lower values are obtained for dispersed episode patterns (0.10 ± 0.05). The Gini coefficient is better suited for discriminating among the patterns with high PAF burden and, therefore, represents a descriptor which is complementary to aggregation. Both descriptors may have relevance when studying the relationship between episode pattern and the risk of thrombus formation.

No abstract