  The electrocardiogram (ECG) signal is the most widely used non-invasive tool
for the investigation of cardiovascular diseases. Automatic delineation of ECG
fiducial points, in particular the R-peak, serves as the basis for ECG
processing and analysis. This study proposes a new method of ECG signal
analysis by introducing a new class of graphical models based on optimal
changepoint detection models, named the graph-constrained changepoint detection
(GCCD) model. The GCCD model treats fiducial points delineation in the
non-stationary ECG signal as a changepoint detection problem. The proposed
model exploits the sparsity of changepoints to detect abrupt changes within the
ECG signal; thereby, the R-peak detection task can be relaxed from any
preprocessing step. In this novel approach, prior biological knowledge about
the expected sequence of changes is incorporated into the model using the
constraint graph, which can be defined manually or automatically. First, we
define the constraint graph manually; then, we present a graph learning
algorithm that can search for an optimal graph in a greedy scheme. Finally, we
compare the manually defined graphs and learned graphs in terms of graph
structure and detection accuracy. We evaluate the performance of the algorithm
using the MIT-BIH Arrhythmia Database. The proposed model achieves an overall
sensitivity of 99.64%, positive predictivity of 99.71%, and detection error
rate of 0.19 for the manually defined constraint graph and overall sensitivity
of 99.76%, positive predictivity of 99.68%, and detection error rate of 0.55
for the automatic learning constraint graph.
