Remote measurements of heart valve sounds for health assessment and biometric identification

Heart failure will contribute to the death of one in three people who read this thesis; and one in three of those who don't. Although in order to diagnose patients’ heart condition cardiologists have access to electrocardiograms, chest X-rays, ultrasound imaging, MRI, Doppler techniques, angiography, and transesophageal echocardiography, these diagnostic techniques require a cardiologist’s visit, are expensive, the examination time is long and so are the waiting lists. Furthermore abnormal events might be sporadic and thus constant monitoring would be needed to avoid fatalities. Therefore in this thesis we propose a cost effective device which can constantly monitor the heart condition based on the principles of phonocardiography, which is a cost-effective method which records heart sounds. Manual auscultation is not widely used to diagnose because it requires considerable training, it relies on the hearing abilities of the clinician and specificity and sensitivity for manual auscultation are low since results are qualitative and not reproducible. However we propose a cheap laser-based device which is contactless and can constantly monitor patients’ heart sounds with a better SNR than the digital stethoscope. We also propose a Machine Learning (ML) aided software trained on data acquired with our device which can classify healthy from unhealthy heart sounds and can perform biometric authentication. This device might allow development of gadgets for remote monitoring of cardiovascular health in different settings

Time-of-flight imaging at 10 ps resolution with an ICCD camera

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

Remote laser-speckle sensing of heart sounds for health assessment and biometric identification

The paper describes a contactless, machine-learning assisted method for heart-sound identification and quantification based on the remote measurement of the reflected laser speckle from the human neck skin surface. For more details see Readme.rt

Remote laser-speckle sensing of heart sounds for health assessment and biometric identification

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