Disclosed are a method and system for detecting a concentration of an analyte based on a change in relative permittivity of a biological tissue within a living body. The method of detecting a concentration of an analyte may include generating a fringing field, measuring a change in a resonant frequency generated by an oscillator based on a change in capacitance attributable to a change in an analyte within a region of the fringing field, and measuring a change characteristic of the analyte within the fringing field based on the change in the resonant frequency.

Systems and methods for minimizing or eliminating transient non-glucose related signal noise due to non-glucose rate limiting phenomenon such as ischemia, pH changes, temperatures changes, and the like. The system monitors a data stream from a glucose sensor and detects signal artifacts that have higher amplitude than electronic or diffusion-related system noise. The system replaces some or the entire data stream continually or intermittently including signal estimation methods that particularly address transient signal artifacts. The system is also capable of detecting the severity of the signal artifacts and selectively applying one or more signal estimation algorithm factors responsive to the severity of the signal artifacts, which includes selectively applying distinct sets of parameters to a signal estimation algorithm or selectively applying distinct signal estimation algorithms.

A wearable device for detecting and/or measuring physiological information from a subject includes a housing, at least one optical emitter supported by the housing, at least one optical detector supported by the housing, a first light guide supported by the housing, a second light guide supported by the housing, a motion sensor supported by the housing, and a processor supported by the housing. The processor is configured to calculate footsteps, distinguish footsteps from heart beats, and to remove footstep motion artifacts from signals produced by the at least one optical detector. Also, the processor is configured to process signals produced by the at least one optical detector to determine subject heart rate and to produce integrity data about the subject heart rate. The process is further configured to generate a multiplexed output serial data string comprising the subject heart rate and the integrity data.

A modeling method for screening surgical patients, used in analysis modeling for heart rate variability (HRV). Low-cost, portable and wearable signal acquisition equipment is utilized to acquire an electrocardiography (ECG) signal of an epileptic 24 hours before surgery; a multiscale entropy (MSE) of the ECG is calculated by means of a programmed HRV analysis method, wherein characteristic parameters representing heart rate complexity are extracted on the basis of an MSE curve, and a medical refractory epileptic suitable for vagus nerve stimulation (VNS) surgery is accurately and efficiently screened, thus avoiding unnecessary expenditures and avoiding delaying an optimal opportunity for treatment. Meanwhile, the curative effects of the VNS treatment may be wholly improved by means of clearly selecting VNS surgical indication patients according to the characteristic parameters of the MSE complexity of the ECG.

A method of estimating concentration of a blood compound may include: removing a baseline drift from Near-Infrared (NIR) spectroscopy data to obtain drift-free spectral features; obtaining a set of global features based on the drift-free spectral features; and estimating the concentration of the blood compound by regression using the set of global features.

A method and system for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient is disclosed. Geometric features representing at least a portion of a coronary artery tree of the patient are extracted from medical image data. Lesions are detected in coronary artery tree of the patient and a hemodynamic quantity of interest is computed at a plurality of points along the coronary artery tree including multiple points within the lesions based on the extracted geometric features using a machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest. Post-treatment values for the hemodynamic quantity of interest are predicted at the plurality of points along the coronary artery tree including the multiple points within the lesions for each of one or more candidate treatment options for the patient, resulting in a respective predicted post-treatment pullback curve for the hemodynamic quantity of interest for each of the one or more candidate treatment options. A visualization of a treatment prediction for at least one of the candidate treatment options is displayed.

A calibration method includes receiving magnetic field values, which are generated by a plurality of real magnetic transmitters and are measured at multiple positions on a grid in a region containing a magnetic field perturbing element. Approximate locations of the real magnetic transmitters are received. Using the approximate locations, a respective plurality of imaginary magnetic sources is characterized inside the field perturbing element. Using the measured magnetic field values, the approximate locations, and the characterized imaginary sources, there are iteratively calculated (i) actual locations of the real and imaginary magnetic sources in the region, and (ii) modeled magnetic field values that would result from the real and imaginary magnetic sources at the actual locations. Using the calculated locations, and the modeled magnetic field values at the multiple positions on the grid, a magnetic field calibration function is derived for the region.

A sensor system has a low-noise sensor controller providing communications between an active-temperature-regulated optical sensor and an external monitor. A low-noise sensor controller drives optical emitters, receives resulting detected signals after attenuation by a blood perfused tissue site and communicates the detector signals to the attached signal processor. An optically-isolated controller front-end receives and digitizes the detected signals. A controller serializer transmits the digitized detector signal to the processor via a single, shielded coaxial cable.

Disclosed embodiments include methods and systems including a receiver unit of a glucose monitoring system. The receiver is configured for communicating with a remote transmitter unit coupled with a glucose sensor. The glucose sensor generates data signals associated with a glucose level. The receiver unit includes a processor, a display, and a memory for storing instructions which, when executed by the processor: access a transmitter key associated with the remote transmitter unit; transmit a command to the remote transmitter unit after verifying the transmitter key; receive communication packets from the remote transmitter unit including a first data segment with data signals indicative of the glucose level and a second data segment with information corresponding to a remaining life of the remote transmitter unit; estimate a remaining life of the remote transmitter unit; process the data signals; and output the estimated remaining life and the processed data signals for display.

A smartphone plugin determines the heart rate and the breathing rate of a user, who is either holding the smartphone in his/her hand or who has the smartphone resting on his/her chest when lying in a supine position, using only smartphone accelerometer output data and no external sensors. The smartphone is preloaded with spectral entropy to weight mapping information for each of a plurality of use cases. The plugin performs frequency domain processing on accelerometer output data to determine an estimated heart rate EHR and an estimated breathing rate EBR. The spectral entropy of accelerometer output data is determined, and is used along with an appropriate spectral entropy to weight mapping, to determine an EHR weight for each EHR value and an EBR weight for each EBR value. The weights are used to adjust the EHR and EBR values to generate more accurate heart rate and breathing rate values.