Abstract: A non-invasive, passive, and fully automated heart-sound-based system and method that provides estimates for blood velocity, tissue motion, and cardiac chamber size parameters for cardiac assessment is provided. The system uses a computer processor and software to receive PCG acoustic signals from one or more sensors and simultaneously receive electrocardiogram (ECG) signals from one or more sensors that are attached to a patient. The phonocardiogram (PCG) processing system and methods compute proxy metrics for echocardiographic parameters of cardiac tissue motion and valvular blood flow for evaluation.

Abstract: Systems and methods are provided for the non-invasive computation of Pulmonary Artery Pressure (and its components of mean, systolic and diastolic) (PAP) as well as Pulmonary Capillary Wedge Pressure (and its components of mean, A-Wave and V-Wave) (PCWP) using a wearable sensor device. Cardiac acoustic and electrocardiogram sensor signals are obtained and multiple temporal, amplitude-based, and spectral features are extracted from the signals. Extracted features from a subject are used as inputs for pre-trained classification, regression, or advanced machine learning models to provide an accurate computation of PAP and PCWP and their associated component values without surgery.