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Heart sound auscultation has been demonstrated to be beneficial in clinicalusage for early screening of cardiovascular diseases. Due to the highrequirement of well-trained professionals for auscultation, automaticauscultation benefiting from signal processing and machine learning can helpauxiliary diagnosis and reduce the burdens of training professional clinicians.Nevertheless, classic machine learning is limited to performance improvement inthe era of big data. Deep learning has achieved better performance than classicmachine learning in many research fields, as it employs more complex modelarchitectures with stronger capability of extracting effective representations.Deep learning has been successfully applied to heart sound analysis in the pastyears. As most review works about heart sound analysis were given before 2017,the present survey is the first to work on a comprehensive overview tosummarise papers on heart sound analysis with deep learning in the past sixyears 2017--2022. We introduce both classic machine learning and deep learningfor comparison, and further offer insights about the advances and futureresearch directions in deep learning for heart sound analysis.

A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era