Sharp Laboratories of America, Inc

Becton Dickinson and Company Limited

Yongji Fu

Bryan Hallberg

Te-Chung Isaac Yang

Deepak V Ayyagari

Yungkai Kyle Lai

Steven Keith

Nhedti Colquitt

Elaine McVey

Yiwen Zhang

The present invention relates to a system and method for improving glucose sensor accuracy by utilizing multiple calibration methods and selecting the most accurate method depending on a consensus glucose concentration estimate. Embodiments of the present invention comprise the steps of performing at least one in vivo update of surrounding glucose to acquire glucose values; calculating multiple updated calibration estimates using the updated glucose values; calculating an initial consensus glucose estimate from sensor output using each updated calibration estimate; applying a smooth crossover function to the multiple calibration estimates based on the value of the initial consensus glucose estimate; and adding weights to the multiple calibration estimates to acquire a consensus glucose estimate.

System and method for using multiple sensor calibration methods

The present invention provides adaptive lightweight acoustic signal classification for physiological monitoring applications. In an exemplary implementation, the total energy of a segment of an acoustic signal recording body sounds is first determined. For each of a plurality of signal classes (e.g., good, noisy, weak), the probability that the segment belongs to the signal class is then calculated using the total energy and profile data for the signal class. The segment is then assigned to one of the plurality of signal classes by reference to the probabilities. Physiological data are then selectively generated and outputted using the segment, depending on the assigned signal class, and the segment is selectively applied as feedback to update profile data for the assigned signal class.

Adaptive lightweight acoustic signal classification for physiological monitoring

Physiological signal quality classification methods and systems designed to improve ambulatory monitoring. Physiological signals are classified as good, noisy or weak based on signal properties. Once classified, signals are processed differently depending on their classification in order to encourage reliance on reliable physiological data, discourage reliance on unreliable physiological data and induce action to improve signal quality. For example, for a good signal, physiological data may be extracted from the signal and displayed to a person being monitored. For a noisy signal, a noisy signal notification may be displayed to the person in lieu of extracted physiological data. For a weak signal, a weak signal notification may be displayed to the person in lieu of extracted physiological data. Moreover, a noisy or weak signal notification displayed to a person being monitored may be accompanied by a corrective action recommendation, such as 'move to quieter environment' for a noisy signal or 'check body placement of sensor' for a weak signal.

Physiological signal quality classification for ambulatory monitoring

Dual path noise detection and isolation for an acoustic respiration monitoring system detects noise in an acoustic signal recording lung sounds using two discrete noise detection techniques. A first technique detects portions of the signal that exhibit long-term, moderate amplitude noise by analyzing cumulative energy in the signal. A second technique detects portions of the signal that exhibit short-term, high amplitude noise by analyzing peak energy in the signal. Noisy portions of the signal are isolated using the combined results of the dual path detection. A respiration parameter is estimated using the signal without resort to the noisy portions and information based at least in part on the respiration parameter is outputted.

Dual path noise detection and isolation for acoustic ambulatory respiration monitoring system

A multistage system and method for estimating respiration parameters from an acoustic signal. At a first stage, the method and system detect and isolate portions of the signal that exhibit long-term, moderate amplitude noise by analyzing cumulative energies in the signal, and portions of the signal that exhibit short-term, high amplitude noise by analyzing peak energies in the signal. At a second stage, the method and system filter heart sound from the signal energy envelope by applying an adaptive filter that minimizes the loss of respiration sound. At a third stage, the system and method isolate respiration phases in the signal by identifying trends in the energy envelope. Once respiration phases are isolated, these phases are used to estimate respiration parameters, such as respiration rate and I/E ratio.

Multistage method and system for estimating respiration parameters from acoustic signal

Data binning methods and systems for estimating a subject's respiratory airflow from a body sound signal detected by an acoustic sensor on the subject's body. The methods and systems operate in a configuration mode followed by a monitoring mode. In the configuration mode, a body sound signal and respiratory airflow are detected by an on-body acoustic sensor and a spirometer, respectively, over a common time period. Time-aligned body sound signal and respiratory airflow data points are then generated and assigned to bins each spanning a discrete signal range (e.g. discrete signal entropy range or signal amplitude range). Respiratory airflow estimation data (e.g. mean airflow and standard deviation) are then calculated for each bin and an entry for each bin associating the discrete range and the estimation data is stored in a lookup table. Then, in the monitoring mode, the lookup table is accessed using subsequent body sound signal readings (e.g. taken in the field or at home) to provide respiratory airflow estimates without further need for a spirometer.

Data binning method and system for estimating respiratory airflow from body sound signal

Physiological signal quality classification methods and systems for ambulatory monitoring. Physiological signals are classified as good, noisy or weak based on signal properties. Once classified, signals are processed differently depending on their classification For example, for a good signal, physiological data may be extracted from the signal and displayed to a person being monitored. For a noisy signal, a noisy signal notification may be displayed to the person in lieu of extracted physiological data. For a weak signal, a weak signal notification may be displayed to the person in lieu of extracted physiological data. Moreover, a noisy or weak signal notification displayed to a person being monitored may be accompanied by a corrective action recommendation, such as 'move to quieter environment' for a noisy signal or 'check body placement of sensor' for a weak signal.

Lightweight wheeze detection methods and systems for portable respiratory health monitoring devices conserve computing resources in portable respiratory health monitoring devices by employing lightweight algorithm that calculates a partial STFT image of a respiratory signal that includes all data points necessary for wheeze detection but excludes many data points that are unnecessary for wheeze detection. The methods and systems provide substantial savings in computing resources while still ensuring every wheeze in a respiratory signal is detected.

Lightweight wheeze detection methods and systems

Methods and systems for particle characterization using a light fluctuation component of an optical sensor output signal. The use of the light fluctuation component enables particle characterization (e.g. provision of information on particle size, type and confidence) without requiring measurements at multiple wavelengths or multiple angles and using relatively lightweight calculations. The methods and systems allow integration of real-time airborne particle characterization into portable monitors. The methods and systems in some embodiments also use the output signal to further characterize particles through determination of particle density information.

Method and systems for particle characterization using optical sensor output signal fluctuation

A personal environmental monitoring method and system and a portable monitor for use therein permit real-time mobile monitoring of environmental conditions in the immediate vicinity to ensure compatibility with the particular environmental sensitivities of a person being monitored. The portable monitor may be a fully integrated mobile device that provides real-time mobile monitoring of immediate environmental conditions without network connectivity.

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Vancouver, WA, United States of America

Yongji Fu