A closed loop system using in-ear infrasonic hemodynography and method therefor are disclosed. The system includes an in-ear biosensor system that detects biosignals including infrasonic signals of an individual, and sends the biosignals to an analysis system that identifies physiological data from the biosignals that is associated with the autonomic nervous system of the individual. External sensors can detect other physiological data of the individual during environmental conditions and under different stimuli, and send the other data and the context under which it was detected to the analysis system. The analysis system can train a machine learning model with the identified physiological data in conjunction with the other physiological data, execute actions in response to new information to adjust the autonomic nervous system of the individual, optimize their performance on tasks, and train the individual to adjust their autonomic nervous system in response to new stimuli.

Systems and methods are disclosed for identifying anatomically relevant blood flow characteristics in a patient. One method includes: receiving, in an electronic storage medium, a patient-specific representation of at least a portion of vasculature of the patient having a lesion at one or more points; receiving values for one or more metrics of interest associated with one or more locations in the vasculature of the patient; receiving one or more observed lumen measurements of the vasculature of the patient; determining the location of a diseased region in the vasculature of the patient using the received values for the one or more metrics of interest, wherein the determination of the location includes predicting or receiving one or more healthy lumen measurements of the vasculature of the patient; determining the extent of the diseased region; and generating a visualization of at least the diseased region.

Systems and methods are disclosed for identifying anatomically relevant blood flow characteristics in a patient. One method includes: receiving, in an electronic storage medium, a patient-specific representation of at least a portion of vasculature of the patient having a lesion at one or more points; receiving values for one or more metrics of interest associated with one or more locations in the vasculature of the patient; receiving one or more observed lumen measurements of the vasculature of the patient; determining the location of a diseased region in the vasculature of the patient using the received values for the one or more metrics of interest, wherein the determination of the location includes predicting or receiving one or more healthy lumen measurements of the vasculature of the patient; determining the extent of the diseased region; and generating a visualization of at least the diseased region.

Disclosed are an ultrasound device and method for acquiring physiological parameter(s) thereby. The method comprises: acquiring ultrasonic data of a target object, the ultrasonic data including at least an ultrasound image; performing image recognition on the ultrasound image to acquire an image recognition result; acquiring physiological parameter(s) corresponding to the image recognition result from a bedside device, the physiological parameter(s) being acquired by detecting the target object by the bedside device; and displaying the acquired physiological parameter(s) and the ultrasound image. By means of the ultrasound device and the method for acquiring physiological parameter(s) thereby according to the present disclosure, relevant physiological parameter(s) can be automatically obtained from the bedside device and displayed by the ultrasound device; and in this way, the relevant physiological parameter(s) can be quickly provided to the doctor, reducing the doctor's operations and effectively improving the efficiency of the doctor's diagnosis.

The present disclosure describes systems, methods, and devices to infer changes in pulmonary artery pressure in a subject using Doppler radar. A portable, non-invasive device for non-invasively measuring right ventricular cardiac motion that can be used in a subject's home can infer pulmonary artery pressure changes to increase patient compliance and mitigate the likelihood of heart decompensation. A mobile pulmonary artery pressure monitor can be especially useful to patients with congestive heart failure who are elderly, incapacitated, or do not have easy access to a clinic, doctor's office, or hospital.

The present disclosure describes systems, methods, and devices to infer changes in pulmonary artery pressure in a subject using Doppler radar. A portable, non-invasive device for non-invasively measuring right ventricular cardiac motion that can be used in a subject's home can infer pulmonary artery pressure changes to increase patient compliance and mitigate the likelihood of heart decompensation. A mobile pulmonary artery pressure monitor can be especially useful to patients with congestive heart failure who are elderly, incapacitated, or do not have easy access to a clinic, doctor's office, or hospital.

An ultrasound imaging system may acquire ultrasound data from a heart. The ultrasound data may be analyzed to on-invasively provide a value for cardiac pressure, such as left ventricular end diastolic pressure (LVEDP). In some examples, the ultrasound data may be acquired from B-mode images, Doppler images, and/or strain measurements. In some examples, the ultrasound data may be acquired across an entire cardiac cycle of the heart. In some examples, the ultrasound data may include strain measurements and/or volume measurements of the left atrium. In some examples, the ultrasound data may be analyzed by a correlation algorithm, such as a partial least squares model and/or a neural network.

An ultrasound imaging system may acquire ultrasound data from a heart. The ultrasound data may be analyzed to on-invasively provide a value for cardiac pressure, such as left ventricular end diastolic pressure (LVEDP). In some examples, the ultrasound data may be acquired from B-mode images, Doppler images, and/or strain measurements. In some examples, the ultrasound data may be acquired across an entire cardiac cycle of the heart. In some examples, the ultrasound data may include strain measurements and/or volume measurements of the left atrium. In some examples, the ultrasound data may be analyzed by a correlation algorithm, such as a partial least squares model and/or a neural network.

An ultrasonic sensor for monitoring a user's health characteristics, is flexible and close-fitting on the user's skin. The ultrasonic sensor includes a substrate, a signal transmitting layer positioned on the substrate, a signal receiving layer positioned on the substrate, and a flexible layer positioned on the signal receiving layer. The flexible layer is configured to attach to the user's skin. The signal transmitting layer includes a second electrode layer and a plurality of piezoelectric units formed on the second electrode layer. Each piezoelectric unit includes a second piezoelectric material layer formed on the second electrode layer and a conductive layer formed on the second piezoelectric material layer.

An ultrasonic sensor for monitoring a user's health characteristics, is flexible and close-fitting on the user's skin. The ultrasonic sensor includes a substrate, a signal transmitting layer positioned on the substrate, a signal receiving layer positioned on the substrate, and a flexible layer positioned on the signal receiving layer. The flexible layer is configured to attach to the user's skin. The signal transmitting layer includes a second electrode layer and a plurality of piezoelectric units formed on the second electrode layer. Each piezoelectric unit includes a second piezoelectric material layer formed on the second electrode layer and a conductive layer formed on the second piezoelectric material layer.