In the context of wearable medical systems, resources are scarce while performance requirements are high. Traditional sampling strategies create large amounts of data, which hinders the device's battery lifetime. However, energy savings are possible when relying on an event-triggered strategy, following the brain example. In this paper, we explore the use of non-Nyquist sampling for cardiovascular monitoring systems, with an in-depth analysis of the performance of a knowledge-based adaptive sampling strategy. By reducing the average sampling rate from 360 Hz down to 13.6 Hz, we can increase the battery lifetime by 4× with a marginal impact on the accuracy of heart-rate analysis.