Classification of Normal/Abnormal Heart Sound Recordings: the PhysioNet/Computing in Cardiology Challenge 2016

Abstract

In the past few decades heart sound signals (i.e., phonocardiograms or PCGs) have been widely studied.Automated heart sound segmentation and classification techniques have the potential to screen for pathologies in a variety of clinical applications.However, comparative analyses of algorithms in the literature have been hindered by the lack of a large and open database of heart sound recordings.The PhysioNet/Computing in Cardiology (CinC) Challenge 2016 addresses 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 includes 4,430 recordings taken from 1,072 subjects, totalling 233,512 heart sounds collected from both healthy subjects and patients with a variety of conditions such as heart valve disease and coronary artery disease.These recordings were collected using heterogeneous equipment in both clinical and nonclinical (such as in-home visits).The length of recording varied from several seconds to several minutes.Additional data provided include subject demographics (age and gender), recording information (number per patient, body location, and length of recording), synchronously recorded signals (such as ECG), sampling frequency and sensor type used.Participants were asked to classify recordings as normal, abnormal, or not possible to evaluate (noisy/uncertain).The overall score for an entry was based on a weighted sensitivity and specificity score with respect to manual expert annotations.A brief description of a baseline classification method is provided, including a description of open source code, which has been provided in association with the Challenge.The open source code provided a score of 0.71 (Se=0.65 Sp=0.76).During the official phase of the competition, a total of 48 teams submitted 348 open source entries, with a highest score of 0.86 (Se=0.94Sp=0.78).