A system for treating and/or monitoring a patient includes a patient physiological parameter monitoring patch and a companion device. The patient physiological parameter monitoring patch including an energy harvesting module, an energy storage module, a sensor module and a communication module. The energy harvesting module harvesting energy from one or more ambient sources, the energy being storable in the energy storage module and usable by one or more components of the patient physiological parameter monitoring patch. The sensor module senses one or more physiological parameters of the patient and the communication module can transmit the sensed data. The companion device can receive the sensed physiological parameters and can send the same to a remote device or store the same.

A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network for determining blood pressure may include: receiving a request to determine blood pressure of a first subject from a terminal, obtaining data related to heart activity of the first subject, determining a personalized model for predicting blood pressure with respect to the first subject, determining the blood pressure of the first subject using the personalized model based on the data related to heart activity of the first subject, and sending the blood pressure of the first subject to the terminal in response to the request.

The process for classifying anesthetic depth includes: collecting of biological signals, conditioning of said signals, monitoring of activity of the central and autonomic systems, measurement of indexes and classification of patterns in anesthetic depth. The activity includes: i) Awake: Vigil—Ak. and recovery of verbal response—Rc. ii) Light Anesthesia: Light induction anesthesia—Li. Light recovery—Lr, Light dose, increase in drugs or patient movement (La), iii) General anesthesia: General anesthesia—Ga, one minute after the start of the surgery, and iv) Deep anesthesia: identification of the EEG burst-suppression pattern (BSP) associated with deep anesthesia.

A system including a hierarchical analytics framework that can utilize a first set of machine learned algorithms to identify and quantify a set of biological properties utilizing medical imaging data is provided. System can segment the medical imaging data based on the quantified biological properties to delineate existence of perivascular adipose tissue. The system can also segment the medical imaging data based on the quantified biological properties to determine a lumen boundary and/or determine a cap thickness based on a minimum distance between the lumen boundary and LRNC regions.

Systems and methods are disclosed for informing and monitoring blood flow calculations with user-specific activity data, including sensor data. One method includes receiving or accessing a user-specific anatomical model and a first set of physiological characteristics of a user; calculating a first value of a blood flow metric of the user based on the user-specific anatomical model and the first set of physiological characteristics; receiving or calculating a second set of physiological characteristics of the user by accessing or receiving sensor data of the user's blood flow and/or sensor data of the user's physiological characteristics; and calculating second value of the blood flow metric of the user based on the user-specific anatomical model and the second set of physiological characteristics of the user.

A blood vessel detection device comprises: a first irradiation unit for irradiating a predetermined site of a subject with light of a first wavelength that is absorbed less by hemoglobin or water and light of a second wavelength that is absorbed more by hemoglobin or water than the first wavelength; a second irradiation unit for irradiating the predetermined site with light of a third wavelength that is absorbed less by hemoglobin; a light intensity detection unit for detecting the intensity of light emitted from the subject at one or more positions spaced apart by a predetermined distance from the light irradiation positions of the first and second irradiation units or one or more contiguous positions; and a control unit that calculates blood vessel depth information, calculates blood vessel angle information, and determines an optimal position for measurement based on the blood vessel depth information and the blood vessel angle information.

A method for performing spatial filtering and augmenting an electroencephalogram (EEG) signal is provided. A processor constructs a spatial filter based on channel information of the EEG signal. The processor augments the EEG signal with the spatial filter. A related electronic device and a related non-transitory computer-readable storage medium are provided.

A method for performing spatial filtering and augmenting an electroencephalogram (EEG) signal is provided. A processor constructs a spatial filter based on channel information of the EEG signal. The processor augments the EEG signal with the spatial filter. A related electronic device and a related non-transitory computer-readable storage medium are provided.

Systems and methods are disclosed for identifying features of a blood vessel using extravascular and intravascular images in order to estimate a virtual flow reserve (VFR) of the imaged blood vessel. Aspects of the disclosure include using extravascular images to estimate the size of the blood vessel in regions that have not been intravascularly imaged. The VFR estimation may be based on a resistance model that incorporates both the intravascular image data and the estimated blood vessel size. In other aspects, multiangled extravascular images are captured and analyzed in order to identify the size and orientation of branch vessels.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.