Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022

Matthew Reyna1, Yashar Kiarashinejad1, Andoni Elola2, Jorge Oliveira3, Francesco Renna4, Annie Gu1, Nadi Sadr1, Erick Andres Perez Alday1, Ashish Sharma1, Sandra Mattos5, Miguel Coimbra4, Reza Sameni1, Ali Bahrami Rad1, Gari Clifford6
1Emory University, 2University of the Basque Country, 3Instituto de Telecomunicações, 4INESC TEC, Faculdade de Ciências da Universidade do Porto, 5Real Hospital Português, 6Emory University and Georgia Institute of Technology


The George B.\ Moody PhysioNet Challenge 2022 explored the detection of abnormal heart function from phonocardiogram (PCG) recordings.

Although imaging ultrasound is becoming more common for investigating heart defects, the PCG still has the potential to assist with rapid and low-cost screening, and the automated annotation of PCG recordings has the potential to further improve access. Therefore, for this Challenge, we asked participants to design working, open-source algorithms that use PCG recordings to identify heart murmurs and clinical outcomes.

This Challenge provides several innovations. First, we sourced 5272 PCG recordings from 1568 patients in Brazil, providing high-quality data for a diverse population. Second, we required the Challenge teams to submit code for training and running their models, improving the reproducibility and reusability of the algorithms. Third, we devised a cost-based evaluation metric that reflects the costs of screening, treatment, and diagnostic errors, allowing us to facilitate the development of more clinically relevant algorithms.

A total of 89 teams submitted 780 algorithms during the Challenge. These algorithms represent a diversity of approaches from both academia and industry for detecting abnormal cardiac function from PCG recordings.