A Deep Learning Algorithm for Automated Cardiac Murmur Detection Via a Digital Stethoscope Platform

John S Chorba; Avi M Shapiro; Le Le; John Maidens; John Prince; Steve Pham; Mia M Kanzawa; Daniel N Barbosa; Brent E White; Jason Paek; Sophie G Fuller; Grant W Stalker; Sara A Bravo; Dina Jean; Subramaniam Venkatraman; Patrick M McCarthy; James D Thomas

Recordings of the phonocardiogram (PCG) were performed by trained study personnel for each subject in a standardized manner. Each subject underwent 15 second recordings at the four standard auscultation positions (aortic: 2 nd intercostal space, right sternal border; pulmonic: 2 nd intercostal space, left sternal border; tricuspid: 5 th intercostal space, left sternal border; mitral: 5 th intercostal space, mid-clavicular line). These recordings were obtained with the standard, clinically available Eko mobile application wirelessly-connected first to the Eko CORE Stethoscope, then to the Eko DUO. Recorded PCG and ECG data were saved as 16-bit, 4000 and 500 Hz-sampled WAV files, respectively, and were synced in real-time to a HIPAA-compliant cloud storage location and sent to the algorithms for analysis. Auscultatory recordings were reviewed by the study investigators for quality control.