  Physiological signals, such as the electrocardiogram and the phonocardiogram
are very often corrupted by noisy sources. Usually, artificial intelligent
algorithms analyze the signal regardless of its quality. On the other hand,
physicians use a completely orthogonal strategy. They do not assess the entire
recording, instead they search for a segment where the fundamental and abnormal
waves are easily detected, and only then a prognostic is attempted. Inspired by
this fact, a new algorithm that automatically selects an optimal segment for a
post-processing stage, according to a criteria defined by the user is proposed.
In the process, a Neural Network is used to compute the output state
probability distribution for each sample. Using the aforementioned quantities,
a graph is designed, whereas state transition constraints are physically
imposed into the graph and a set of constraints are used to retrieve a subset
of the recording that maximizes the likelihood function, proposed by the user.
The developed framework is tested and validated in two applications. In both
cases, the system performance is boosted significantly, e.g in heart sound
segmentation, sensitivity increases 2.4% when compared to the standard
approaches in the literature.
