  Cardiac auscultation is one of the most cost-effective techniques used to
detect and identify many heart conditions. Computer-assisted decision systems
based on auscultation can support physicians in their decisions. Unfortunately,
the application of such systems in clinical trials is still minimal since most
of them only aim to detect the presence of extra or abnormal waves in the
phonocardiogram signal, i.e., only a binary ground truth variable (normal vs
abnormal) is provided. This is mainly due to the lack of large publicly
available datasets, where a more detailed description of such abnormal waves
(e.g., cardiac murmurs) exists.
  To pave the way to more effective research on healthcare recommendation
systems based on auscultation, our team has prepared the currently largest
pediatric heart sound dataset. A total of 5282 recordings have been collected
from the four main auscultation locations of 1568 patients, in the process,
215780 heart sounds have been manually annotated. Furthermore, and for the
first time, each cardiac murmur has been manually annotated by an expert
annotator according to its timing, shape, pitch, grading, and quality. In
addition, the auscultation locations where the murmur is present were
identified as well as the auscultation location where the murmur is detected
more intensively. Such detailed description for a relatively large number of
heart sounds may pave the way for new machine learning algorithms with a
real-world application for the detection and analysis of murmur waves for
diagnostic purposes.
