Heart sounds are difficult to interpret due to events
with very short temporal onset between them (tens of millisec-
onds) and dominant frequencies that are out of the human audi-
ble spectrum. Computer assisted decision systems may help but
they require robust signal processing algorithms. In this paper,
we propose a new algorithm for heart sound segmentation using a
hidden semi-Markov model. The proposed algorithm infers more
suitable sojourn time parameters than those currently suggested
by the state-of-art, through a maximum likelihood approach. We
test our approach over three different datasets, including the
publicly available PhysioNet and Pascal datasets. We also release
a pediatric dataset composed of 29 heart sounds. In contrast with
any other dataset available online, the annotations of the heart
sounds in the released dataset contain information about the
beginning and the ending of each heart sound event. Annotations
were made by two cardiopulmonologists. The proposed algorithm
is compared with the current state-of-the-art. The results show
a significant increase in segmentation performance, regardless
the dataset or the methodology presented. E.g.: when using the
PhysioNet dataset to train and to evaluate the HSMMs, our
algorithm achieved average an F-score of 92% compared to 89%
achieved by the algorithm described in [1]. In this sense, the
proposed approach to adapt sojourn time parameters represents
an effective solution for heart sound segmentation problems, even
when the training data does not perfectly express the variability
of the testing data.