Abstract: 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.

Abstract: 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.

Abstract: Recursive least squares (RLS) adaptive acoustic signal filtering for a physiological monitoring system reduces residual heart sound in a primary signal remaining after application of a respiration sound bandpass filter to a first instance of a mixed signal containing respiration sound and heart sound. Residual heart sound in the primary signal is reduced by minimizing a component in the primary signal that correlates with a reference signal containing heart sound but almost no residual respiration sound after application of a heart sound bandpass filter to a second instance of the mixed signal. The correlative component in the primary signal is minimized by applying an adaptive filter to the reference signal and subtracting the filtered reference signal from the primary signal to produce a residue signal, wherein the coefficients for the adaptive filter are selected to minimize the least square error of the residue signal.

Abstract: An adaptive acoustic signal filter for a respiration monitoring system includes a filter stage and a cutoff frequency adapter. The filter stage applies a cutoff frequency to an input acoustic signal waveform containing respiration and heart sound components in a filtering operation to produce a filtered acoustic signal waveform from which heart sound components have been removed. The adapter then performs cutoff frequency optimization tests on the filtered signal waveform and determines from the tests whether adjustment of the cutoff frequency is indicated. These tests assess whether the filtering operation struck a proper balance between removing heart sound components and preserving respiration sound components in the filtered signal waveform. If adjustment of the cutoff frequency is indicated, the adapter adjusts the cutoff frequency and the adjusted cutoff frequency is provided to the filter stage for application in a next filtering operation performed on the input signal waveform.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present invention isolates respiration phases in an acoustic signal using trend analysis. Once respiration phases are isolated, they are used to estimate respiration parameters. An exemplary method comprises receiving an acoustic signal recording body sounds; identifying candidate peaks at maxima of the signal; identifying candidate valleys at minima of the signal; selecting significant peaks from among the candidate peaks using heights of the candidate peaks; selecting significant valleys from among the candidate valleys using heights of the candidate valleys; detecting silent phases in the signal based at least in part on rise rates from the significant valleys; isolating respiration phases in the signal based at least in part on the significant valleys and the silent phases; calculating respiration parameter estimates based at least in part on the respiration phases; and outputting the respiration parameter estimates.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Linear classification is used to determine the quality of acoustic physiological signal samples. A feature dataset is extracted from acoustic physiological signal samples of known quality (i.e., weak, noisy, good) acquired over a sampling period. A linear discriminant analysis is performed on the feature dataset to determine a direction of a linear classifier for the feature dataset. A classification error risk analysis is performed on the feature dataset to determine an offset of the linear classifier. The linear classifier is used to classify into reliability classes acoustic physiological signal samples acquired over an operating period. Information is selected for outputting using the assigned classifications, and is outputted.

Abstract: 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.

Abstract: Method and device for continual physiological monitoring in which the display of physiological parameter estimates is conditioned on conformance of the estimates with expectations. Current estimates of physiological parameters are compared with expectations for the current estimates determined using prior estimates of the physiological parameters. Nonconformance with expectations can result in display of information indicating present unavailability of an estimate for the physiological parameter. The method and device are adaptable for use with various types of monitored physiological parameters and various expectation metrics.

Abstract: A physiological monitoring device and large noise handling method for use on such a device in which a reliable estimate of a physiological parameter is ensured by identifying and replacing large noise components of a physiological signal prior to estimation. An estimation period for a physiological parameter is segmented into time windows. Noisy time windows within the estimation period are identified. The noisy time windows are replaced with replacement time windows having a baseline amplitude. An estimate of the physiological parameter for the estimation period is calculated using the replacement time windows in lieu of the noisy time windows, and is outputted. If the share of noisy time windows exceeds a predetermined limit share, calculating and/or outputting of an estimate may be precluded. The physiological parameter may be heart rate.

Abstract: Lightweight automatic gain control (AGC) methods and systems reduce usage of often scarce computing resources in ambulatory monitoring systems through an AGC algorithm that relies on lightweight calculations and judicious constraints on gain reevaluations and adjustments. Statistical range sampling is used to adjust the gain of a physiological signal to keep the signal within a target amplitude range and may be coupled with dynamic range control to prevent gain adjustments from occurring too frequently. Moreover, gain reevaluations and adjustments may be temporarily suspended when the physiological signal is noisy.