  Cardiovascular disease is one of the leading causes of death according to
WHO. Phonocardiography (PCG) is a costeffective, non-invasive method suitable
for heart monitoring. The main aim of this work is to classify heart sounds
into normal/abnormal categories. Heart sounds are recorded using different
stethoscopes, thus varying in the domain. Based on recent studies, this
variability can affect heart sound classification. This work presents a Siamese
network architecture for learning the similarity between normal vs. normal or
abnormal vs. abnormal signals and the difference between normal vs. abnormal
signals. By applying this similarity and difference learning across all
domains, the task of domain invariant heart sound classification can be well
achieved. We have used the multi-domain 2016 Physionet/CinC challenge dataset
for the evaluation method. Results: On the evaluation set provided by the
challenge, we have achieved a sensitivity of 82.8%, specificity of 75.3%, and
mean accuracy of 79.1%. While overcoming the multi-domain problem, the proposed
method has surpassed the first-place method of the Physionet challenge in terms
of specificity up to 10.9% and mean accuracy up to 5.6%. Also, compared with
similar state-of-the-art domain invariant methods, our model converges faster
and performs better in specificity (4.1%) and mean accuracy (1.5%) with an
equal number of epochs learned.
