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.

Cardiac dysfunction is a leading cause of death that might be prevented by early diagnosis and timely treatments. Auscultation of the heart with a stethoscope is a key component of clinical examination. In a healthy individual, for each heartbeat, two heart sounds (S1 and S2) can be heard, produced by the closure of cardiac valves. Additional heart sounds (for example S3 and S4) might indicate a severe cardiac damage and a poor prognosis, but their identification is difficult, requires training, and depends on clinical experience [1] [2] . Therefore, easily deployable technologies that allow contactless and non-subjective detection of heart sounds are desirable.

We detect sound from vibrations induced on a diffusing surface with a speckle- based laser vibrometer. Beyond previous work, vibrations are measured through a scattering medium with no speckle-memory effect, similar to thick, strongly diffusive media.

ICCD cameras can record low light events with extreme temporal resolution. Thus, they are used in a variety of bio-medical applications for single photon time of flight measurements and LIDAR measurements. In this paper, we present a method which allows improvement of the temporal resolution of ICCD cameras down to 10 ps (from the native 200 ps of our model), thus placing ICCD cameras at a better temporal resolution than SPAD cameras and in direct competition with streak cameras. The higher temporal resolution can serve for better tracking and visualization of the information carried in time-of-flight measurements.