An open access database for the evaluation of heart sound algorithms
Résumé
In the past few decades, analysis of heart sound signals (i.e., the phonocardiogram or PCG),
especially for automated heart sound segmentation and classification, has been widely studied and has
been reported to have the potential value to detect pathology accurately in clinical applications. However,
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. This paper describes a
public heart sound database, assembled for an international competition, the PhysioNet/Computing in
Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced
from multiple research groups around the world. It includes 2,435 heart sound recordings in total
collected from 1,297 healthy subjects and patients with a variety of conditions, including heart valve
disease and coronary artery disease. The recordings were collected from a variety of clinical or
nonclinical (such as in-home visits) environments and equipment. The length of recording varied from
several seconds to several minutes. This article reports detailed information about the subjects/patients
including demographics (number, age, gender), recordings (number, location, state and time length),
associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a
brief summary of the commonly used heart sound segmentation and classification methods, including
open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC
Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for
different heart sound states, the scoring mechanism, and associated open source code are provided. In
addition, several potential benefits from the public heart sound database are discussed.