Electrical activation of tissue can be mapped from using electrophysiological data from a plurality of electrodes carried by a high density grid catheter. Each clique of three or more electrodes will define a pair of orthogonal bipoles as well as several unipoles. An electroanatomical mapping system can analyze the electrophysiological data such that, for each clique, an integral of an omnipolar electrogram the best morphologically matches a representative (e.g., average) unipolar electrogram for the clique is identified. The orientation of the best-fit omnipole is then defined as the activation direction for the clique. The conduction velocity magnitude can also be computed as a ratio of an amplitude of the unipolar electrogram for the clique to an amplitude of the integral of the omnipolar electrogram for the clique along the activation direction. The resulting activation map can also be output graphically.

Embodiments and aspects described herein provide methods and systems for determining pressure difference across a tube arising from fluid flow within the tube, comprising: obtaining three-dimensional time dependent fluid velocity data at a plurality of points along the tube; processing the three-dimensional time dependent fluid velocity data to determine: i) a flow rate (Q) of the fluid through the tube; ii) the kinetic energy (K) of the fluid flow through the tube; iii) an advective energy rate (A) of the fluid flow through the tube; and iv) a viscous dissipation rate (V) pertaining to the fluid flow; and calculating the pressure difference in dependence on all of the flow rate (Q), kinetic energy (K), advective energy rate (A), and viscous dissipation rate (V). Further embodiments are also described.

A diagnostic Electrochemical Impedance Spectroscopy (EIS) procedure is applied to measure values of impedance-related parameters for one or more sensing electrodes. The parameters may include real impedance, imaginary impedance, impedance magnitude, and/or phase angle. The measured values of the impedance-related parameters are then used in performing sensor diagnostics, calculating a highly-reliable fused sensor glucose value based on signals from a plurality of redundant sensing electrodes, calibrating sensors, detecting interferents within close proximity of one or more sensing electrodes, and testing surface area characteristics of electroplated electrodes. Advantageously, impedance-related parameters can be defined that are substantially glucose-independent over specific ranges of frequencies. An Application Specific Integrated Circuit (ASIC) enables implementation of the EIS-based diagnostics, fusion algorithms, and other processes based on measurement of EIS-based parameters.

A diagnostic Electrochemical Impedance Spectroscopy (EIS) procedure is applied to measure values of impedance-related parameters for one or more sensing electrodes. The parameters may include real impedance, imaginary impedance, impedance magnitude, and/or phase angle. The measured values of the impedance-related parameters are then used in performing sensor diagnostics, calculating a highly-reliable fused sensor glucose value based on signals from a plurality of redundant sensing electrodes, calibrating sensors, detecting interferents within close proximity of one or more sensing electrodes, and testing surface area characteristics of electroplated electrodes. Advantageously, impedance-related parameters can be defined that are substantially glucose-independent over specific ranges of frequencies. An Application Specific Integrated Circuit (ASIC) enables implementation of the EIS-based diagnostics, fusion algorithms, and other processes based on measurement of EIS-based parameters.

The exemplified technology facilitates de-noising of magnetic field-sensed signal data (e.g., of an electrophysiological event) using signal reconstruction processes that fuse the magnetic field-sensed signal data with another sensed signal data (e.g., voltage gradient signal data) captured simultaneously with the magnetic field-sensed signal data. To this end, the purely algorithmic processing technique beneficially facilitates removal and/or filtering of noise from a sensor lead of a noisy captured source and rebuilds the signal for that lead from information simultaneously obtained from other leads of a different source. In some embodiments, a data are fused via a sparse approximation operation that uses candidate terms based on Van der Pol differential equations.

A system for monitoring a vascular access is provided. The system includes a wearable vascular access monitor which can be a sleeve or other protective covering fitted with two or more sensors for obtaining physiological measurements at different locations from the vascular access. The sleeve or other protective covering is also fitted with an ultra-low power processor for relaying the physiological measurements to a patient's mobile phone. The patient's phone can evaluate the physiological measurements to determine a state of the vascular access, and if the physiological measurements fall out of nominal ranges, the patient's phone can alert a clinic, nurse, or physician. The system can be used to monitor fistulas or grafts used for hemodialysis or peritoneal dialysis.

Electrical activation of tissue can be mapped from using electrophysiological data from a plurality of electrodes carried by a high density grid catheter. Each clique of three or more electrodes will define a pair of orthogonal bipoles as well as several unipoles. An electroanatomical mapping system can analyze the electrophysiological data such that, for each clique, an integral of an omnipolar electrogram the best morphologically matches a representative (e.g., average) unipolar electrogram for the clique is identified. The orientation of the best-fit omnipole is then defined as the activation direction for the clique. The conduction velocity magnitude can also be computed as a ratio of an amplitude of the unipolar electrogram for the clique to an amplitude of the integral of the omnipolar electrogram for the clique along the activation direction. The resulting activation map can also be output graphically.

According to certain embodiments, a wearable electronic device, comprises: at least one light receiving unit; at least one light emitting unit; an external light calibration circuit; and a processor electrically connected with the at least one light receiving unit, at least one light emitting unit, and the external light calibration circuit, wherein the processor is configured to: control the at least one light emitting unit to radiate light during first periods, and not emit light during second periods, and detect light through the at least one light receiving unit during the second periods, and controlling the external light calibration circuit to provide an input to the at least one light receiving unit during first periods, based on the light detected during the second periods; and wherein during the first periods the at least one light receiving unit provides an output based on light received, and the input from the external light calibration circuit.

Certain aspects of the present disclosure relate to methods and systems for providing decision support around glucose management for patients with diabetes. Time-varying inputs including blood glucose, meal intake information, and amount of infused insulin are processed using a machine learning model to obtain predicted glucose levels for a plurality of prediction horizons and uncertainties for the predictions. A confidence interval is generated for each prediction and the confidence intervals are compared to hypo- and hyperglycemic thresholds. If a confidence interval is entirely below or entirely above the hypo- and hyperglycemic thresholds, respectively, then a decision support output is provided.

A method of calibrating an inertial measurement unit, the method comprising: (a) collecting data from the inertial measurement unit while stationary as a first step; (b) collecting data from the inertial measurement unit while repositioning the inertial measurement unit around three orthogonal axes of the inertial measurement unit as a second step; (c) calibrating a plurality of gyroscopes using the data collected during the first step and the second step; (d) calibrating a plurality of magnetometers using the data collected during the first step and the second step; (e) calibrating a plurality of accelerometers using the data collected during the first step and the second step; (f) where calibrating the plurality of magnetometers includes extracting parameters for distortion detection and using the extracted parameters to determine if magnetic distortion is present within a local field of the inertial measurement unit.