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.

Intraprocedural techniques for identifying a location of an origin of an idiopathic ventricular arrhythmia in a patient are presented. The techniques include acquiring an at least partial electroanatomical geometry; acquiring an electrocardiogram segment of the idiopathic ventricular arrhythmia; calculating at least one integral of the electrocardiogram segment; acquiring a plurality of pacing site sets of coordinates; acquiring a plurality of pacing electrocardiogram segments of the patient; calculating at least one pacing integral; relating each pacing site set of coordinates to at least one corresponding pacing integral, such that a plurality of coefficients are obtained; determining an estimated set of coordinates of the origin of the idiopathic ventricular arrhythmia based on at least one integral of the electrocardiogram segment and on the plurality of coefficients; projecting the estimated set of coordinates of the origin on to the at least partial electroanatomical geometry to obtain a map; and providing the map.

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.

Systems and methods for monitoring of biometric and contextual variables to assist in screening for, quantification of, and prediction of smoking behavior, and for assisting in smoking cessation are described.

Medical patient monitoring sensor devices including a disposable sensor assembly and a reusable pairing device are disclosed. The disposable sensor assembly can collect patient physiological data and provide power for the reusable pairing device. The reusable pairing device can establish wireless communication with a monitoring device. Once the reusable pairing device receives patient physiological data from the disposable sensor assembly, the reusable pairing device can wirelessly transmit the data to the computing device via the wireless communication.

A wearable device collects a plurality of photoplethysmography (PPG) waveforms from a PPG sensor in the wearable device and collects inertial data associated with subject motion from an inertial sensor in the wearable device. The wearable device processes the inertial data in an assessment processor of the wearable device to determine a data integrity of the plurality of PPG waveforms and, responsive to the determined data integrity, processes the plurality of PPG waveforms in the assessment processor using a neural network comprising thousands of coefficients to generate an assessment of the subject blood pressure.

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 wearable device collects a plurality of photoplethysmography (PPG) waveforms from a PPG sensor in the wearable device and collects inertial data associated with subject motion from an inertial sensor in the wearable device. The wearable device processes the inertial data in an assessment processor of the wearable device to determine a data integrity of the plurality of PPG waveforms and, responsive to the determined data integrity, processes the plurality of PPG waveforms in the assessment processor using a neural network comprising thousands of coefficients to generate an assessment of the subject blood pressure.

Blood pressure signals are reconstructed from PhotoPlethysmoGraphy (PPG) signals by: receiving PPG signals including systolic, diastolic and dicrotic phases; and determining first and second derivatives of the PPG signals and: a first set of values indicative of lengths of the signal paths of the PPG signal, the first derivative and the second derivative thereof in the systolic, diastolic and dicrotic phases; a second set of values indicative of relative durations of the PPG signal and the first and second derivatives thereof in the systolic, diastolic and dicrotic phases; and a third set of values indicative of the time separation of peaks and/or valleys in subsequent waveforms of the PPG signal. Reconstruction also includes applying artificial neural network processing to the first, second and third set of values. The artificial neural network processing includes artificial neural network training as a function of blood pressure signals to produce reconstructed blood pressure signals.

Systems and methods for monitoring of biometric and contextual variables to assist in screening for, quantification of, and prediction of smoking behavior, and for assisting in smoking cessation are described.