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Contents:

The conditioning algorithm can include using an endpoint-finding algorithm to identify the end-points of the arc; estimating the trajectory of the arc; adjusting the arc's trajectory such that it has the endpoints estimated by the endpoint-finding algorithm; and adjusting the samples to be along the adjusted trajectory. The conditioning algorithm can also include computing a best-fit line in the complex plane repeatedly for subsets, such as small subsets, of consecutive samples.

In some embodiments, the demodulation algorithm comprises evaluating the changes in the direction of the best-fit lines and accumulating them. Also disclosed herein is a method of performing a non-contact, point-in-time measurement of vital signs. In some embodiments, the interval selection algorithm extends the interval until at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 60 seconds, or more of high-quality data is obtained.

The time interval could be consecutive. In some embodiments, the interval selection algorithm extends the interval until at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more complete breaths, which can be consecutive breaths, with high-quality data is obtained. In some embodiments, the interval selection algorithm assesses the irregularity of respiration in at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 60 seconds or more of high-quality data, and if this assessment indicates irregular breathing, extends the measurement until breathing appears to be regular, a periodic pattern repeats, or at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 60 seconds or more has passed and breathing is still irregular and non-periodic.

The interval selection algorithm can have a time-out, such that if the interval extends beyond 10, 20, 30, 40, 50, 60 seconds, or more, or between about 30 seconds and 5 minutes in some embodiments, the device provides an error message, retry message, or error code. In some embodiments, the time-out is determined by other equipment when the device is integrated with another device that performs vital signs measurements.

In some embodiments, the time-out occurs at the completion of all the other vital signs measurements. Also disclosed herein is a system for sensing a physiological motion. The system includes one or more antennas configured to transmit electromagnetic radiation; one or more antennas configured to receive electromagnetic radiation; at least one processor configured to extract information related to cardiopulmonary motion by executing at least one of a demodulation algorithm, a non-cardiopulmonary motion detection algorithm, and a rate estimation algorithm; and a communications system configured to communicate with an output device, said output device configured to output information related to the cardiopulmonary motion.

The device can provide a spot check point-in-time measurement of vital signs, which can include, for example, a respiratory rate or heart rate. The source of radiation can be a voltage-controlled oscillator, which is phase-locked to a crystal with a phase-lock loop circuit, such that the frequency of the radiation can be selected within a band, providing a tunable frequency synthesizer and frequency selectivity.

In some embodiments, the same antenna is configured to transmit and receive electromagnetic radiation, and the antenna comprises an array of metal elements with an air gap between the elements and the ground plane, The air gap can be between about 0. Spread spectrum techniques can be used to introduce a pseudo-random phase noise to the frequency synthesizer utilizing the phase-locked oscillator.

The signal conditioning can provide a DC-coupled signal, and the ADC can be high-resolution, such as 12, 16, 20, 24 bits, or more. The system can be powered by a variety of power sources, such as AC or DC current. In one embodiment, the system is powered through 5V USB bus power.

The system can include a radio and processor integrated in the same housing, or as separate modules. The processor can run the algorithms and provides rate and other information to a separate host computer. The host computer can provide a command over a communications interface to initiate measurements. The device can include an integrated light source to provide feedback on the proper aiming of the device. The integrated light source can illuminate the areas included in the antenna field of view. The sensor's integrated display can provide instant feedback messages including progress, error messages, retry messages, low-signal information, results, and other information.

The system can also include real-time audio feedback, such that if the system is aimed improperly such that the signal power is low, there is an audible indication. In some embodiments, disclosed is a method of sensing motion using a motion sensor.

In some embodiments, the output action comprises the display of a history of point-in-time measurements, including values and times, such that trends can be viewed. Estimating point in time vital signs parameters can comprise determining the length of the measurement interval with a interval selection algorithm that utilizes the information corresponding to a non-cardiopulmonary motion or other signal interference and information corresponding to the physiological movement of the subject or a part of the subject.

The pre-determined intervals can be user selectable from a menu of intervals. The pre-determined intervals can be selected by the user with a keypad interface. In some embodiments, an external device controls a device which estimates point-in-time vital signs parameters by sending commands for when to start measurements, in cases wherein the device that estimates point-in-time vital signs does not have interval measurement capability. The external device can be, for example, a computer, a vital signs measurement device, or a patient monitor.

In some embodiments, disclosed herein is a method of estimating the presence or absence of paradoxical breathing using a motion sensor. In some embodiments, the paradoxical breathing indication algorithm comprises: evaluating the distribution of samples in the complex plane and distinguishing an arc or a line from an ellipse, circle, crescent-moon shape, kidney-bean shape, egg shape, figure-8 or ribbon shape, or other shape that is not a line or arc, indicating the absence of paradoxical breathing is a line or arc is detected; and indicating the presence of paradoxical breathing if a shape other than a line or arc is detected.

In some embodiments, the paradoxical breathing indication algorithm comprises comparing the trajectory in the complex plane during inhalation with that during exhalation; indicating the absence of paradoxical breathing if the two are similar; and indicating the presence of paradoxical breathing if the two are significantly different. In other embodiments, the paradoxical breathing indication algorithm comprises: segmenting the shape in the complex plane by determining the best-fit line for each frame segments of the data ; calculating an orientation vector pointing in the direction of movement in the complex plane for every frame; calculating the change in phase between each consecutive orientation vector; determining whether the change in phase between each consecutive orientation vector is positive or negative; indicating the presence of paradoxical breathing if either positive phase change or negative phase change is dominant; and indicating the absence of paradoxical breathing if the phase change is approximately evenly distributed between positive and negative.

In some embodiments, the paradoxical breathing indication algorithm comprises fitting the samples in the complex plane to an arc that subtends an angle no greater than a threshold value. The angle could be between degrees, such as 90 to degrees, or less than about , , , , , , , , , 90, 80, 70, 60, or less degrees in some embodiments.

The threshold can be determined based on information in the patient's medical record. In some embodiments, the paradoxical breathing indication algorithm comprises fitting the samples in the complex plane to an ellipse; determining the eccentricity of the ellipse; indicating the presence of paradoxical breathing if the eccentricity of the ellipse is above a threshold; and indicating the absence of paradoxical breathing if the eccentricity of the ellipse is below a threshold.

In some embodiments, comparing the trajectory comprises fitting a circle or an arc to the inhalation samples in the complex plane and to the exhalation samples in the complex plane; and comparing the centers and the radii of the circles for inhalation and exhalation. The paradoxical breathing indication algorithm can also include calculating the area enclosed by a full breathing cycle in the complex plane; indicating the presence of respiration if the area bounded by the points is greater than a threshold; and indicating the absence of respiration if the area bounded by the points is less than a threshold.

In some embodiments, the paradoxical breathing indication algorithm includes fitting a circle to the samples in the complex plane from one or more complete breathing cycles; estimating the center of that circle; calculating the distance from each sample to the center of the circle; calculating the variance of the distance from each sample to the center of the circle; indicating the presence of paradoxical breathing if the variance is above a threshold; and indicating the absence of paradoxical breathing if the variance is below a threshold.

Also disclosed herein is a method of determining the regularity of respiration, comprising: processing one or more frames of a respiratory waveform to obtain information regarding the irregularity or regularity of respiration; said respiratory waveform comprising one or more frames, wherein the one or more frames comprise time sampled values of respiratory signals; and communicating the information to an output system that is configured to perform an output action. The respiratory waveform can be obtained by one of Doppler radar, ultrawideband radar, impedance pneumography, chest straps, airflow measurements, or load cells.

The output of the system can be an indication of regularity or irregularity a binary state ; an integrated regularity index that compiles a variety of information about the regularity of respiration into a signal number or a single bar graph; separate indications of the irregularity of the breath-to-breath interval and the irregularity of the depth of breath; or individual indications of several measures of irregularity. In some embodiments, processing one or more frames comprises: performing an auto-correlation function on a subset of frames; identifying whether major peaks are present; identifying the number of samples from the center to major peaks, if they are present; determining whether breathing is regular based on the number of samples to the first major peak and the height of the first major peak; and identifying the second major peak that is not a multiple of the respiratory period as the period of periodic breathing.

The subset of frames can include samples obtained over a time longer than the expected period of respiration.


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In some embodiments, the subset of frames includes samples obtained over a time longer than the expected cycle period of irregular respiration. The method can also include using a wavelet transform function to create an index of repeating patterns in a respiration signal. In some embodiments, the irregularity of the breath-to-breath interval, or breath duration, is estimated from one or more of the group consisting of: the standard deviation of the breath-to-breath interval, the frequency of apneaic events, the coefficient of variation of the breath-to-breath interval, the standard deviation of the respiratory rate, and the coefficient of variation of the respiratory rate.

In some embodiments, the irregularity of the amplitude of a breath or the depth of breath, or breath duration, is estimated from the standard deviation of the breath depth, the coefficient of variation of the breath depth, the standard deviation of the respiratory signal amplitude, or the coefficient of variation of the respiratory signal amplitude.

Information regarding the irregularity or regularity of respiration can include assessment of whether irregular breathing is periodic. This assessment can include estimating each breath-to-breath interval, and storing it with the time point at the end of the interval in which it was calculated; interpolating between these breath-to-breath intervals to create a waveform; performing the Fourier transform, performing the autocorrelation function, or calculating the power spectral density of the waveform; determining whether there are significant peaks of the Fourier transform, the autocorrelation function, or the power spectral density of the waveform; and determining that if significant peaks exist, the breathing is irregular and periodic.

The assessment can also include interpolating between these breath-to-breath intervals to create a waveform; identifying peaks of the waveform; determining the time between the peaks; calculating the coefficient of variation of the time between the peaks; determining if the coefficient of variation of the time between the peaks is low, the breathing is irregular and periodic; and determining if the coefficient of variation of the time between the peaks is low, the breathing is irregular and is not periodic.

In some embodiments, assessment of whether irregular breathing is periodic comprises: identifying apneaic events; determining the time of cessation of apneaic events; estimating the interval between the cessation of each consecutive pair of apneaic events; determining whether the interval between the cessation of each consecutive pair of apneaic events is consistent by calculating the coefficient of variation of the interval between the events by calculating the coefficient of variation; determining if the coefficient of variation is below a threshold, breathing is periodic; and determining if the coefficient of variation is above a threshold, breathing is irregular and not periodic.

In some embodiments, assessment of whether irregular breathing is periodic comprises calculating the envelope of the respiratory waveform; performing the Fourier transform, performing the autocorrelation function, or calculating the power spectral density of the waveform; and determining whether there are significant peaks of the Fourier transform, the autocorrelation function, or the power spectral density of the waveform.

In some embodiments, the envelope is calculated by interpolating between the peak amplitudes, or squaring the signal and applying a low-pass filter.

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In some embodiments, information regarding the irregularity or regularity of respiration is assessed by the following algorithms:. If neither one is above a threshold, the respiration is considered regular. If the coefficient of variation of either the breath-breath interval or the depth of breath is above a threshold, the respiration is considered irregular, and additional processing is performed. If a periodic component exists in at least one of the waveforms, the cycle time is periodic.


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  • If a periodic component does not exist in either waveform, the cycle time is not periodic. If the cycle time is still not periodic, skip to step g. If multiple peaks are not available, extend the interval used for this step. Calculate the number of these intervals divided by the total time interval used for calculation. Calculate the mean of these apneaic events. In some embodiments, the respiratory rate is estimated by counting repeating key points, which are points in a respiration cycle that are identifiable using specific algorithms.

    The key points can include peaks, valleys, zero crossings, points of fastest change, points of no change, and points with the greatest change in direction. In some embodiments, the respiratory rate is determined before demodulation by making key points in the complex plane. The key points can also include points with low velocity in the complex plane or points with high velocity in the complex plane. The rate of the respiratory signal can be estimated in the time domain by tracking the points where a signal crosses a time-delayed version of itself.

    The time delay can be adaptively set using the spectrum of the data to provide a delay that is long enough to suppress small variations or noise, and short enough to compare within the same respiratory cycle. The cardiopulmonary movement information can be pre-conditioned before rate estimation by normalizing the envelope of the signal before applying a rate estimation algorithm that utilizes peak-finding.

    In some embodiments, each breath is identified based on breath characteristics, and breaths that meet the required characteristics are used for rate-finding. Breath characteristics can include the ratio of the duration of an inhale to the ratio of an exhale that must lie within a defined interval, and can include detection of a peak and detection of a valley.

    The defined interval can be determined based on the patient's height, weight, and other information in the patient's medical chart.