  In this work, a novel stack of well-known technologies is presented to
determine an automatic method to segment the heart sounds in a phonocardiogram
(PCG). We will show a deep recurrent neural network (DRNN) capable of
segmenting a PCG into its main components and a very specific way of extracting
instantaneous frequency that will play an important role in the training and
testing of the proposed model. More specifically, it involves a Long Short-Term
Memory (LSTM) neural network accompanied by the Fourier Synchrosqueezed
Transform (FSST) used to extract instantaneous time-frequency features from a
PCG. The present approach was tested on heart sound signals longer than 5
seconds and shorter than 35 seconds from freely-available databases. This
approach proved that, with a relatively small architecture, a small set of
data, and the right features, this method achieved an almost state-of-the-art
performance, showing an average sensitivity of 89.5%, an average positive
predictive value of 89.3\% and an average accuracy of 91.3%.
