  Heart rate estimation from electrocardiogram signals is very important for
the early detection of cardiovascular diseases. However, due to large
individual differences and varying electrocardiogram signal quality, there does
not exist a single reliable estimation algorithm that works well on all
subjects. Every algorithm may break down on certain subjects, resulting in a
significant estimation error. Ensemble regression, which aggregates the outputs
of multiple base estimators for more reliable and stable estimates, can be used
to remedy this problem. Moreover, active learning can be used to optimally
select a few trials from a new subject to label, based on which a stacking
ensemble regression model can be trained to aggregate the base estimators. This
paper proposes four active stacking approaches, and demonstrates that they all
significantly outperform three common unsupervised ensemble regression
approaches, and a supervised stacking approach which randomly selects some
trials to label. Remarkably, our active stacking approaches only need three or
four labeled trials from each subject to achieve an average root mean squared
estimation error below three beats per minute, making them very convenient for
real-world applications. To our knowledge, this is the first research on active
stacking, and its application to heart rate estimation.
