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Heart sounds have been widely studied and have been demonstrated to have value for detecting pathologies in clinical applications. Over the last few decades, the use of heart sound signals has become increasingly uncommon and its practice in modern medicine somewhat diminished, although research into automated analysis has continued. Unfortunately, a comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. The 2016 PhysioNet/Computing in Cardiology (CinC) Challenge addressed this issue by assembling the largest public heart sound database, aggregated from eight sources obtained by seven independent research groups around the world. The database comprises a total of 4,430 recordings collected from 1,072 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease.
Clifford et al. (Tue,) studied this question.
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