  Cardiovascular diseases are the leading cause of deaths and severely threaten
human health in daily life. On the one hand, there have been dramatically
increasing demands from both the clinical practice and the smart home
application for monitoring the heart status of subjects suffering from chronic
cardiovascular diseases. On the other hand, experienced physicians who can
perform an efficient auscultation are still lacking in terms of number.
Automatic heart sound classification leveraging the power of advanced signal
processing and machine learning technologies has shown encouraging results.
Nevertheless, human hand-crafted features are expensive and time-consuming. To
this end, we propose a novel deep representation learning method with an
attention mechanism for heart sound classification. In this paradigm,
high-level representations are learnt automatically from the recorded heart
sound data. Particularly, a global attention pooling layer improves the
performance of the learnt representations by estimating the contribution of
each unit in feature maps. The Heart Sounds Shenzhen (HSS) corpus (170 subjects
involved) is used to validate the proposed method. Experimental results
validate that, our approach can achieve an unweighted average recall of 51.2%
for classifying three categories of heart sounds, i. e., normal, mild, and
moderate/severe annotated by cardiologists with the help of Echocardiography.
