Abstract: A method or system for noninvasive evaluating and monitoring of abnormal cardiopulmonary vibrations includes obtaining one or more signals using one or more noninvasive heart and/or lung vibration signal sensors or transducers that provide a measure of physiological effects that are correlated with cardiopulmonary functions, recording, by a processor and one or more heart and/or lung vibration signal sensors or transducers for turbulence recordings between a first and second cardiopulmonary sounds, analyzing the turbulence recordings for unique vibration signatures associated with cardiopulmonary disease by searching deviations from an average beat, and presenting an assessment of cardiopulmonary disease upon finding that the deviations are above a predetermined threshold. In some embodiments, the system and method further include seeking a largest mean absolute error of a specific segment between an average beat and each beat from a subject to provide a murmur level indicative of cardiopulmonary disease.

Abstract: A system for noninvasive extraction, identification, and marking of the heart valve signals to evaluate and monitor elevated left ventricular end-diastolic pressure (LVEDP) or pulmonary capillary wedge pressure (PCWP) using at rest assessment of hemodynamic performance, based on quantitative measurements of heart and lung related parameters and cardiac events for diagnostic and therapeutic purposes includes one or more signals from one or more noninvasive sensors or transducers that measure one or more physiological effects that are correlated with cardiopulmonary functions, transmission of the data to a computing device and analysis software where a trained algorithm processes the data to determine the state or condition of elevated LVEDP or PCWP and provides an output indicative of the state or condition of the analysis.

Abstract: A health sensor system and method can include a wearable non-invasive biosensor for capturing cardiac waveform signals such as electrocardiogram (ECG) signals and composite vibration objects over one or more channels, one or more processors operatively coupled to the wearable non-invasive biosensor, and memory having computer instructions which causes the system to perform certain operations. In some embodiments, the operations can include powering the health sensor system in response to receiving a radio frequency signal using a near field communication protocol, monitoring pulmonary artery pressures based on cardiac time intervals during a period when the health sensor system is powered by the radio frequency signal, performing a heart and lung function assessment based on the monitoring of the pulmonary artery pressures, and presenting the heart and lung function assessment. In some embodiments, the biosensor can be a single NFC patch biosensor.

Abstract: A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats using at least one or more of a PCA algorithm or deep learning.

Abstract: A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats using at least one or more of a PCA algorithm or deep learning.

Abstract: A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats and processing cumulative energy within the individual heartbeat to set a threshold to set a marking point.

Abstract: A sensor device and a method using the sensor device includes a portable device (110) configured to capture composite vibration objects from at least one sensor (102b) and further configured to communicate data to a wireless node (105) in some embodiments. The sensor device further includes at least one or more processors (103, 105, or 106) operatively coupled to the portable device and configured to separate and identify separated vibration sources and further configured to identify a plurality of individual heart vibration events (302, 303, 304, 305) from the composite vibration objects where the one or more processors is further configured to mark individual heart events from the plurality of individual heart vibration events. In some embodiments, the one or more processors marks and presents individual heart events from the plurality of individual heart vibration events.

Abstract: A system (100) for monitoring and diagnosing heart conditions includes a sensor array (102) with accelerometers, an electrocardiogram sensor, and a system to synchronously capture and process (103, 105, or 106) composite heart signals. The system performs separation, identification and marking (201-205 or 501-509) of the signals, to extract information in cardiac vibrations. Machine learning, auditory scene analysis, or spare coding approaches can be used to resolve source separation problems. Analysis of the composite signals separates different vibration signals (302, 303, 304, 305), identifies streams for particular valve signal, and marks them with event time information with respect to a synchronous electrocardiogram signal (306). The psychoacoustic analysis mimics human auditory processing to enhance hearing of heart valve sounds by separating valve vibrations from the composite signal, and providing extraction, identification, marking and display of individual valve signals.

Abstract: A method and system of assessing and monitoring of cardiopulmonary diseases can include the steps of receiving a composite signal representative of individual events from a plurality of sources associated with a patients cardio-pulmonary system and separating an individual signal as a separate component of the composite signal where the individual signal i s representative of an individual event from one of a plurality of cardio-pulmonary events. The method and system can further include and deriving clinical findings responsive from the separating and presenting the clinical findings.

Abstract: A system for monitoring and diagnosing heart conditions includes a sensor array with accelerometers, an electrocardiogram sensor, and a system to capture and process the composite heart signals. The system performs source separation to extract information contained in cardio pulmonic vibrations. The system can use machine learning, auditory scene analysis, spare coding, determined Models, Principal Component Analysis (PCA), Independent Component Analysis ICA, Singular Value Decomposition (SVD), Bin-wise Clustering and Permutation posterior probability alignment, Undetermined Models, Sparseness condition, Dictionary learning, Convolutive models, K-SVD Matching Pursuit, Non-negative matrix factorization and Deep Belief Networks (Restricted Boltzmann Machine) approaches to the source separation problem. Other embodiments are disclosed.

Abstract: A system and method for monitoring and diagnosing heart conditions may include capturing and processing a composite heart signal and isolating individual components of such a composite signal. The disclosed techniques of measuring hemodynamic parameters may extract information contained in cardio-pulmonic vibrations. In operation, a system and method may separate different vibration signals along with event time information with respect to a synchronous electrocardiogram signal, and may provide for measurement of hemodynamic parameters from these separated signals.

Abstract: A system for monitoring and diagnosing heart conditions includes a sensor array with accelerometers, an electrocardiogram sensor, and a system to capture and process the composite heart signals. The system performs source separation to extract information contained in cardio pulmonic vibrations. The system can use machine learning, auditory scene analysis, spare coding, determined Models, Principal Component Analysis (PCA), Independent Component Analysis ICA, Singular Value Decomposition (SVD), Bin-wise Clustering and Permutation posterior probability alignment, Undetermined Models, Sparseness condition, Dictionary learning, Convolutive models, K-SVD Matching Pursuit, Non-negative matrix factorization and Deep Belief Networks (Restricted Boltzmann Machine) approaches to the source separation problem. Other embodiments are disclosed.

Abstract: A system for identifying heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory, where the one or more processors are configured to perform the steps of separating a plurality of individual heart vibration events from the composite vibration objects by using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks and tagging the plurality of individual heart vibration events using at least one among principal component analysis, Gabor filtering, generalized cross correlation, phase transform, smoothed coherent transformation, Roth correlation or band filtering.

Abstract: A system (100) for monitoring and diagnosing heart conditions includes a sensor array (102) with accelerometers, an electrocardiogram sensor, and a system to synchronously capture and process (103, 105, or 106) composite heart signals. The system performs separation, identification and marking (201-205 or 501-509) of the signals, to extract information in cardiac vibrations. Machine learning, auditory scene analysis, or spare coding approaches can be used to resolve source separation problems. Analysis of the composite signals separates different vibration signals (302, 303, 304, 305), identifies streams for particular valve signal, and marks them with event time information with respect to a synchronous electrocardiogram signal (306). The psychoacoustic analysis mimics human auditory processing to enhance hearing of heart valve sounds by separating valve vibrations from the composite signal, and providing extraction, identification, marking and display of individual valve signals.

Abstract: A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats and processing cumulative energy within the individual heartbeat to set a threshold to set a marking point.

Abstract: A system and method for visualizing auditory scene analysis by way of a portable device is provided. In one embodiment, the method steps include capturing multiple sounds from a sensor array of microphones connected to the portable device, performing auditory scene analysis on detected body sounds in accordance with a psychoacoustic representation of body organ functions, and rendering to a display of the portable device a visualization of auditory scene auscultation of the body sounds, including user input functionality for separated sound source tracks, sound source identifier tracks, and sound source location trajectories.

Abstract: A system and method for visualizing auditory scene analysis by way of a portable device is provided. In one embodiment, the method steps include capturing multiple sounds from a sensor array of microphones connected to the portable device, performing auditory scene analysis on detected body sounds in accordance with a psychoacoustic representation of body organ functions, and rendering to a display of the portable device a visualization of auditory scene auscultation of the body sounds, including user input functionality for separated sound source tracks, sound source identifier tracks, and sound source location trajectories.

Abstract: A method and system are provided for a portable cardio-acoustic device. The device includes a display with user input, a sensor array to capture heart related vibrations from infrasound and acoustically transmitted audible sound, and a processor to extract salient features in accordance with human factor analysis, separate heart sounds as a function of sound patterns modeled from mechanical and physiological processes of the heart, classify heart sound patterns in accordance with biologically based signal processing models of the auditory cortex and cerebellum, and diagnose and monitor cardiovascular condition based on the classification of the heart sound patterns.

Abstract: A system and method for speech therapy is provided that includes a mobile device, a server and a web-client. The mobile device captures and processes voice signals analyzed locally and on the server and from which a speech therapy is coordinated and delivered. The web-client through interaction with the mobile device and through the server implements a speech therapy that can be monitored and managed thereon through specified clinical moderation. The web-client also provides an alternative method to capture and transmit voice signals to the server for analysis and from which a speech therapy is coordinated and delivered. Speech therapy management can implement therapy procedures, guidelines and one-to-one communication sessions between users and providers in a non-clinical setting in real-time or at scheduled times. Other embodiments are disclosed.