  Assessment of heart sounds is the cornerstone of cardiac examination, but it
requires a stethoscope, skills and experience, and a direct contact with the
patient. We developed a contactless, machine-learning assisted method for
heart-sound identification and quantification based on the remote measurement
of the reflected laser speckle from the neck skin surface in healthy
individuals. We compare the performance of this method to standard digital
stethoscope recordings on an example task of heart-beat sound biometric
identification. We show that our method outperforms the stethoscope even
allowing identification on the test data taken on different days. This method
might allow development of devices for remote monitoring of cardiovascular
health in different settings.
