In an aspect, a method for imaging a target comprises steps of: performing optical coherence tomography (OCT) scanning on the target with one or more beams of source light, the one or more beams of source light comprising a plurality of wavelengths; wherein performing OCT scanning comprises: providing the source light to a reference optical path and to a sample optical path, wherein providing the source light to a sample optical path comprises illuminating the target with the source light; and recording interference data corresponding to an interaction of a light from the reference optical path and a light from the sample optical path; processing the interference data; and identifying blood or one or more blood-features in the target based on an optical attenuation of light in or associated with the sample optical path by the blood or the one or more blood-features.

Systems and methods for monitoring physiologic response to Valsalva maneuver (VM) are disclosed. An exemplary patient monitor may detect a natural incidence of a VM session occurred in an ambulatory setting using a heart sound (HS) signal sensed from the patient. The patient monitor may include a physiologic response analyzer to sense patient physiologic response during the detected VM session, and generate a cardiovascular or autonomic function indicator based on the sensed physiologic response to the VM. Using the physiologic response to the VM, the system may detect a target physiologic event using the sensed physiologic response to the VM.

Certain embodiments of the present disclosure relate to a monitoring device that includes a housing, contactless sensors coupled to the housing, and a processing device disposed in the housing. In at least one embodiment, the monitoring device is configured to measure pulse wave velocity in a subject.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

An apparatus and a method of measuring bioinformation are provided. The apparatus for measuring bioinformation includes a first sensor configured to measure a first biosignal including arterial pulse wave information, a second sensor configured to measure a second biosignal including venous or capillary pulse wave information, and a bioinformation estimator configured to estimate bioinformation of a user based on a time delay between the first biosignal and the second biosignal.

Pressure sensing guidewire assemblies are described herein where the guidewire assembly may be comprised of an elongate guidewire body and multiple pressure sensors secured near or at a distal end of the guidewire body. The signals obtained from the guidewire connectors and aortic sensor modules may be synchronized to minimize signal acquisition delays. The signals may be further processed to equalize the pressure waveforms by shifting the connector waveform to align correctly with the aortic module waveform and improve output signals.

Provided are foldable electronic device and method for estimating bio-information by using the same. The foldable electronic device may include: a main body part including a first main body and a second main body that are configured to be folded toward each other or unfolded from each other along a fold line where the first main body and the second main body meet; an image sensor part including a first image sensor and a second image sensor which are disposed at the first main body; and a processor configured to obtain a contact image of an object from the first image sensor disposed at the first main body and obtain an image of a marker that is displayed on the second main body, from the second image sensor disposed at the first main body, when the object is in contact with the first image sensor and the main body part is folded along the fold line, and estimate bio-information based on the contact image of the object and the image of the marker.

A device for liveness detection is disclosed. The liveness detecting device has a simplest structure that principally comprises a light sensing unit and a signal processing module. Particularly, the signal processing module is configured for having a physiological feature extracting unit and a liveness detecting unit therein. The physiological feature extracting unit is adopted for extracting a first physiological feature from a PPG signal, or extracting a second physiological feature from the PPG signal that has been applied with a signal process. As such, through the first and second physiological features, the liveness detecting unit is able to determine whether a subject is a living body or not. The liveness detecting device does not use any camera unit and iPPG technology, such that the liveness detecting device has advantages of simple structure, low cost and immediately completing liveness detection.

An apparatus for predicting a cardiovascular risk factor according to an embodiment includes a target cardiovascular risk factor predicting module for producing an initial prediction value for a target cardiovascular risk factor from a fundus image, at least one related cardiovascular risk factor predicting module for producing respective prediction values for at least one related cardiovascular risk factor from the fundus image, and a combining module for producing a final prediction value for the target cardiovascular risk factor on the basis of the initial prediction value for the target cardiovascular risk factor and the respective prediction values for the at least one related cardiovascular risk factor.

Systems and apparatus are included that are configured to determine the effectiveness of apparatus and methods used to diagnose and unblock microvascular obstruction (MVO). An infusion system blocks antegrade flow for a short time and measures vascular pressure response as an infusate is infused in stepwise fashion at increasingly higher flowrates. During the antegrade flow occlusion, calculations of the real-time vascular resistance can be obtained using the formula R(t)=P(t)/Q.sub.mean(t) where: Q.sub.mean(t) is the flow mean values generated by the infusion system; P(t) is the distal pressure response in the vessel generated from the flow infusion; and R(t) is the calculated vascular resistance using the two other known parameters.