System and method for determining respiratory effort

Abstract

A system and method for determining respiratory effort of a subject is provided. The method includes obtaining a relaxation signal representing the subject breathing in a relaxed manner and a forced signal representing the subject breathing in a forced manner. A plurality of forced peaks is derived from the forced signal, and a candidate peak is selected from the plurality of forced peaks. The candidate peak is selected based on a characteristic of the forced peaks. A user selects a user-identified peak from the candidate peaks, and, as a result, respiratory effort is determined based on the relaxation signal and the user-identified peak.

Description

Technical Field

[0001] This invention relates to systems and methods for determining the respiratory effort of an object. Background Technology

[0002] In subjects with chronic obstructive pulmonary disease (COPD) and other respiratory disorders, assessing parasternal muscle activity (measured by surface electromyography (EMG), e.g., using electrodes located in the second intercostal space) can be used to estimate the intensity, timing, and duration of respiratory effort, serving as an indicator of the balance between respiratory muscle load and capacity. Previous research has shown that the maximum EMG level occurring during relaxed inhalation is associated with neural respiratory drive (NRD). In COPD subjects, the balance between respiratory muscle load and capacity changes during the increased lung hyperinflation observed during acute severe illness, which is reflected in neural respiratory drive (lower capacity and higher load lead to increased NRD). One method for assessing a subject's NRD is to measure their respiratory effort.

[0003] To determine a standardized version of respiratory effort, the maximum inspiratory volume through the nose is used. However, such movements are known to be highly variable and can be biased due to lack of motivation or pain inhibition in the affected subject. This full activation is particularly difficult to achieve in clinical applications with pain-related inhibition (e.g., acute subjects) and in elderly subjects. Furthermore, the subject may engage other muscles, such as postural muscles, during the maximal inspiratory movement, which can interfere with the measurement and potentially increase the maximum RMS value obtained during the maximal movement.

[0004] Therefore, there is a need to improve the way we obtain breathing effort.

[0005] US2019 / 0125214 discloses a method and apparatus for measuring respiratory parameters using an ECG device. Summary of the Invention

[0006] This invention is defined by the claims.

[0007] According to an example of one aspect of the present invention, a system for determining the respiratory effort of an object is provided, the system comprising:

[0008] The processor is configured as follows:

[0009] A relaxation signal is received from at least two electrodes, the relaxation signal indicating that the subject is breathing in a relaxed manner;

[0010] Receive forced signals from the at least two electrodes, the forced signals indicating that the subject is breathing in a forced manner;

[0011] Multiple forced peaks are derived from the forced signal;

[0012] Candidate peaks are selected from multiple forced peaks, wherein candidate peaks and non-candidate peaks are distinguished based on the characteristics of the forced peaks;

[0013] Obtain the user-identified peak, wherein the user-identified peak has been selected by the user; and

[0014] Breathing effort is determined based on the relaxation signal and the peak identified by the user.

[0015] The relaxation signal is obtained by instructing the subject to breathe in a relaxed manner and measuring the signal from the electrodes. The forced signal is obtained by instructing the subject to breathe in a forced, sharp manner (sniffling). Therefore, a forced peak can be derived from the forced signal at the corresponding time event (e.g., from an EMG waveform). The forced peak derived from the forced signal corresponds to the sniffling event. Due to various reasons (e.g., the subject does not inhale as much as possible, the subject stops sniffling midway, the subject is fatigued, etc.), some peaks may be of unsuitable quality. Therefore, candidate peaks corresponding to "good" peaks are selected, which can be used to find the breathing effort. The final selection of the user-identified peaks is made by the user. This provides a trade-off between automatic selection and user selection, as the user selects a peak from a limited set to make user involvement easier. Subsequently, the subject's breathing effort can be found based on the relaxation signal and the user-identified peaks.

[0016] The characteristics of the forced peak may be based on:

[0017] The maximum value of each forced peak in the forced peaks;

[0018] Sharpness indicator, wherein the sharpness indicator indicates the duration of the forced peak; and

[0019] Spectral flatness indicator, wherein the spectral flatness indicator indicates a comparison between the high-frequency and low-frequency components of a forced peak.

[0020] The maximum value of the forced peak corresponds to the peak amplitude of the forced peak. The sharpness indicator is used to indicate the quality of the nose puff. It corresponds to the duration of the peak and / or the effort exerted by the object to perform the nose puff. For example, the sharpness can be calculated by calculating the time derivative of the forced peak, where the value of the derivative at the time of the nose puff can be used to determine the sharpness indicator.

[0021] The spectral flatness indicator is also used to indicate the quality of the nose-pulling. It corresponds to a comparison (e.g., ratio) of the contribution of high-frequency components to low-frequency components in the forced signal during nose-pulling. It can be calculated by using spectral analysis (e.g., applying Fourier transform) on the forced signal during nose-pulling, for example, determining the ratio of low-frequency (e.g., <200Hz) components in the spectral domain to high-frequency (e.g., >200Hz) components in the spectral domain.

[0022] The system may also include a respiratory sensor for monitoring movement or respiratory flow during respiration, and wherein the processor is further configured to:

[0023] When the subject breathes in a relaxed manner, a relaxed breathing signal is obtained based on at least one inhalation action;

[0024] When the subject breathes in a forced manner, a forced breathing signal is obtained based on at least one inspiratory action; and

[0025] Candidate peaks are also selected based on relaxation breathing signals and forced breathing signals.

[0026] A second type of signal, the respiratory signal, can also be used. The respiratory signal represents the physiological effects of breathing and can therefore indicate the properties of the inspiratory action. These properties may include: whether the action corresponds to a relaxed or forced breath, the length of the action, the effort exerted by the object during the action, etc. For example, it can indicate the airflow into the nose or the tilt of an accelerometer during breathing. The respiratory signal can be used to determine whether a forced peak corresponds to a “good” sniff by, for example, comparing the corresponding respiratory signal during a sniff with a respiratory signal during a relaxed breath or with a respiratory signal during a previous sniff.

[0027] The breathing sensor can be one or more of the following:

[0028] accelerometer; and

[0029] Flow sensor.

[0030] The system may also include an output interface for providing real-time feedback for each of the forced peaks, wherein the feedback indicates one or more of the following:

[0031] Whether the forced peak is selected as a candidate peak;

[0032] The number of candidate peaks currently selected;

[0033] Given that the forced peak was not selected as a candidate peak, why was the forced peak not selected as a candidate peak?

[0034] Feedback to the previous forced peak.

[0035] The present invention also provides a method for determining the respiratory effort of an object, the method comprising:

[0036] Obtain relaxation signals indicating that the object is breathing in a relaxed manner;

[0037] Obtain a compulsive signal indicating that the object is breathing in a forced manner;

[0038] Multiple forced peaks are obtained from the forced signal;

[0039] Based on the characteristics of the forced peaks, candidate peaks are selected from the plurality of forced peaks;

[0040] Obtain the user-identified peak, wherein the user-identified peak has been selected by the user; and

[0041] Breathing effort is determined based on the relaxation signal and the peak identified by the user.

[0042] The characteristics of the forced peaks may include the value of each of the forced peaks, and wherein selecting a candidate peak includes comparing the value of each of the forced peaks with one or more of the following:

[0043] Values ​​of other forced peaks; and

[0044] Threshold-forced peak.

[0045] The method may further include obtaining multiple relaxation peaks from the relaxation signal, wherein selecting candidate peaks further includes comparing each of the forced peaks with at least one relaxation peak.

[0046] The method may also include:

[0047] When the subject breathes in a forced manner, a forced breathing signal is obtained based on at least one inhalation action;

[0048] Multiple forced inspiratory peaks are obtained based on the forced breathing signal;

[0049] The selection of candidate peaks is also based on comparing each forced inhalation peak with one or more of the following:

[0050] Other forced inhalation peaks; and

[0051] Threshold forced inhalation peak.

[0052] The method may also include:

[0053] When the subject breathes in a relaxed manner, a relaxed breathing signal is obtained based on at least one inspiratory movement; and

[0054] Multiple relaxation inspiratory peaks are obtained based on the aforementioned relaxation breathing signals.

[0055] The selection of candidate peaks is also based on comparing each forced inhalation peak with at least one relaxed inhalation peak.

[0056] Selecting a candidate peak can include:

[0057] Multiple sharpness indicators are determined from the forced signal, wherein each sharpness indicator indicates the duration of the forced peak, and wherein the forced peak is characterized by a corresponding sharpness indicator; and

[0058] Each of the plurality of sharpness indicators is compared with one or more of the following:

[0059] Other sharpness indicators; and

[0060] Threshold sharpness indicator.

[0061] Selecting a candidate peak can include:

[0062] Determine the spectral density of the forced signal for each of the forced peaks;

[0063] The high spectral density is determined based on the spectral density using frequencies above a threshold frequency.

[0064] The low spectral density is determined based on the frequency below the threshold frequency according to the spectral density;

[0065] Based on a comparison of high and low spectral densities, a spectral flatness indication is determined for each of the forced peaks, wherein the characteristics of the forced peak include the corresponding spectral flatness indication; and

[0066] The spectral flatness indication for each of the forced peaks will be compared with one or more of the following:

[0067] Other spectral flatness indicators; and

[0068] Threshold spectrum flatness indicator.

[0069] It can obtain the forced signal in real time and select candidate peaks in real time.

[0070] The method may further include providing real-time feedback for each of the forced peaks, wherein the feedback indicates one or more of the following:

[0071] Whether the forced peak is selected as a candidate peak;

[0072] The number of candidate peaks currently selected;

[0073] Given that the forced peak was not selected as a candidate peak, why was the forced peak not selected as a candidate peak?

[0074] Feedback to the previous forced peak.

[0075] The present invention also provides a computer program including code units, the code units being used to implement the method described above when the program is run on a processing system.

[0076] These and other aspects of the invention will become apparent and will be explained with reference to the embodiments described below. Attached Figure Description

[0077] To better understand the invention and to more clearly illustrate how it can be practiced, reference will now be made to the accompanying drawings by way of example only, wherein,

[0078] Figure 1 This shows the locations where respiratory muscle activity can be measured.

[0079] Figure 2 The three curves are derived from the signals of the subject breathing in a relaxed manner;

[0080] Figure 3 Six graphs representing the EMG signals of the subject's respiration are shown;

[0081] Figure 4 The first example of a breathing effort being determined is shown;

[0082] Figure 5 An example of a method for determining breathing effort is shown;

[0083] Figure 6 Three graphs representing peak characteristics are shown; and

[0084] Figure 7 This is a second example of a breathing effort being determined. Detailed Implementation

[0085] The invention will be described with reference to the accompanying drawings.

[0086] It should be understood that while the detailed description and specific examples indicate exemplary embodiments of the devices, systems, and methods, they are intended for illustrative purposes only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the devices, systems, and methods of the present invention will be better understood from the following description, the appended claims, and the accompanying drawings. It should be understood that the drawings are schematic only and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the drawings to denote the same or similar parts.

[0087] This invention provides a system and method for determining the breathing effort of a subject. The method includes obtaining a relaxation signal indicating that the subject is breathing in a relaxed manner and a forced signal indicating that the subject is breathing in a forced manner. A plurality of forced peaks are derived from the forced signals, and a candidate peak is selected from the plurality of forced peaks. The candidate peak is selected based on characteristics of the forced peaks. A user selects a user-identified peak from the candidate peaks, and thus, the breathing effort is determined based on the relaxation signal and the user-identified peak.

[0088] Figure 1 An example of a location on the body of subject 104 where respiratory muscle activity can be measured is shown. Two electromyographic (EMG) electrodes 102 located in the second intercostal space near the sternum (parasternal) can be used for measurement. The two electrodes 102 can be mounted (or attached) inside a single EMG patch, which facilitates the placement of the two electrodes 102 to assess the same respiratory muscle groups for each consecutive measurement (e.g., daily). Electrodes 102 are known to primarily measure inspiratory respiratory effort resulting from the activation of the parasternal intercostal muscles during inspiration in subject 104.

[0089] The signal measured at the parasternal muscle in the second intercostal space during inhalation, via two EMG electrodes 102, can be used as an indicator of daily deterioration or improvement in COPD subjects 104 when multiple measurements are performed over several days, and as a predictor of readmission after discharge.

[0090] However, signals from the activity of the second intercostal respiratory muscles also include electrocardiogram (ECG) signals from the heartbeat.

[0091] Figure 2 Three graphs are shown representing the signals from object 104 during respiration.

[0092] Figure a) above shows the raw EMG and ECG signals (including contributions from cardiac electrical activity—ECG contributions—which need to be removed) of COPD subject 104 during relaxed breathing. ECG contributions can be identified by periodic strong peaks (also known as QRS complexes).

[0093] Intermediate plot b) shows the RMS values ​​(in μV on the y-axis), from which it can be seen that the maximum RMS level during a specific single regular breath 204 is approximately 20 μV. This maximum RMS level corresponds to the maximum call of the parasternal muscles on the inspiratory side. The ECG RMS signal peak 202 can also be seen in plot b).

[0094] The bottom graph c) shows the signal from the flow sensor, which measures the pressure in the subject's nose. The trough (negative pressure) in this signal indicates inhalation.

[0095] A drawback when observing the maximum value of the RMS signal during the relaxed breathing phase is that the level of the RMS signal is affected by the level of the subject's subcutaneous skin tissue. Experiments also showed that the RMS value was generally lower when the subject 104 was more obese. Furthermore, it is known that the EMG amplitude decreases with the distance between the electrode 102 and the muscle.

[0096] As a solution to this problem, object 104 can also perform a series of maximal effort movements (e.g., over 1 minute) and the maximum RMS peak level can be measured during this movement. This allows the average RMS peak level during relaxed breathing to be divided (normalized) by the maximum RMS peak level during the maximal effort movement. A significant benefit of this normalization is obtaining a measurement during relaxed breathing, expressed as a percentage of respiratory muscle activation relative to the maximum possible muscle activation. With this result (e.g., percentage), thresholds can be more easily defined to assess object 104 in terms of improvement or deterioration.

[0097] The region 204 highlighted in the intermediate curve b) shows the duration of inhalation performed by object 104. The difference between the inhalation EMG signal 204 and the ECG signal 202 lies in the longer duration and lower amplitude in the RMS signal. The ECG signal 202 is periodic and has spikes compared to the inhalation EMG signal 204.

[0098] Figure 3 Six graphs representing the EMG signals of an object's respiration are shown.

[0099] Figure 1a) shows the EMG and ECG signals. The contribution of the ECG is significant because the signal was measured at the parasternal position. The X-axis shows the interval in seconds.

[0100] Figure 2b) shows the trajectory for removing ECG contributions (“EMG with ECG removed”), where a 200Hz high-pass filter was used to remove ECG contamination.

[0101] Figure 3c) shows the RMS of the EMG after removing the electrocardiogram, where two averaging windows were used to calculate the RMS: a 50-millisecond averaging window and a 1-second averaging window.

[0102] Figure 4(d) shows the RMS of EMG after removing the electrocardiogram, with an average window of 1 second.

[0103] Figure 5 (e) shows the RMS of EMG after removing the electrocardiogram, with an average window of 50 ms.

[0104] Figure 6(f) shows the RMS of EMG after removing the electrocardiogram, with an average window of 1 second for the first 60 seconds and an average window of 50 ms for the last 60 seconds.

[0105] A 1-second averaging time is preferred for relaxation breathing because it eliminates some ECG (residual) contamination and provides a better-defined peak level for the relaxation signal (better averaging of noise). For example, the relaxation peak 302a has a better-defined peak and lower noise level compared to 302b.

[0106] However, for the sniffing region (forced signal) within the last 60 seconds, it can be seen that the RMS level during the sniffing operation is reduced when calculated using a 1-second average RMS. This is related to the fact that sniffing is a sharp inhalation action that strongly activates the parasternal respiratory muscles only for a very short period of time. Furthermore, it can be seen that, for 1-second RMS calculations, longer-duration sniffing (e.g., the first sniffing action around 75 seconds) produces higher RMS values ​​than shorter-duration sniffing (e.g., the last sniffing action around approximately 115 seconds). This is undesirable because it is preferable to have a repeatable maximum sniffing level measurement independent of its duration. For example, forced peak 304a has a much lower RMS peak level compared to forced peak 304b.

[0107] Therefore, there is no single choice for the RMS average window that provides the best output results for both relaxation breathing and nasal sniffing.

[0108] For RMS calculations of relaxation breathing, a long averaging window is preferable, while for RMS calculations of the nasal sniffing action, a short averaging window is preferable. Therefore, it may be advantageous to differentiate between RMS calculations for relaxation breathing and nasal sniffing, i.e., RMS for relaxation breathing with a first (long) averaging window (e.g., 1 second) and RMS for nasal sniffing with a second (short) averaging window (e.g., 50 ms). Figure 3 The curve is shown in f).

[0109] Figure 4 A first example of a system for determining respiratory effort is shown. First, a signal 402 representing the subject's breathing is obtained. This signal can be obtained from electrodes 102 on the subject 104 or from pre-recorded historical data of the subject 104. Signal 402 can cover time periods when the subject breathes in a relaxed manner and / or when the subject breathes in a forced manner. Signal 402 is then processed by processor 404.

[0110] Optionally, if signal 402 has not yet been filtered, it can first be filtered using ECG removal block 406. This will produce a filtered relaxation signal and a filtered forced signal. Since the EMG signal is measured in the parasternal region, there will be ECG contamination (ECG and EMG) in the signal. Therefore, ECG removal block 406 can be used for both EMG and ECG signals, where two types of ECG removal techniques can be applied:

[0111] (i) Spectral ECG removal, wherein a high-pass filter is applied in the spectral domain with a cutoff frequency of, for example, 200 Hz, to effectively remove ECG contributions, since ECG contributions are typically minimized above 200 Hz; and

[0112] (ii) Time-based ECG removal, wherein a high-pass filter with a cutoff frequency of, for example, 20 Hz is applied to effectively remove P-waves and T-waves from the ECG contribution, and the remaining QRS composite wave is removed by time masking.

[0113] Spectral ECG removal removes all spectral contributions from the ECG signal by having a high cutoff frequency. Time-based ECG removal preserves higher-frequency spectral components in the frequency domain but removes high-frequency QRS complexes based on characteristic shapes in the time domain, such as using time-gated filtering techniques based on ECG models.

[0114] Which of these methods to use depends on the application, such as how much contribution to retain in the frequency range of 20 to 200 Hz. For ease of implementation and robustness against arrhythmias, spectral ECG removal can be used because it avoids the detection of R-peaks and the construction of ECG models.

[0115] The output of ECG removal block 406 indicates a level of ECG contamination that should not interfere with EMG measurements. In the inspiratory EMG block, the inspiratory phase 408 is selected from this ECG removal signal.

[0116] Based on whether the subject breathes in a relaxed manner or sniffs, the inhalation phase 408 may include at least relaxed breathing and forced breathing.

[0117] Based on the assumption that inhalation phase 408 is relaxation breathing, a smoothing function with a first (long) averaging window 412 is applied to the EMG signal to obtain a smooth relaxation signal 410. Based on the assumption that inhalation phase 408 is forced breathing (sniffling), a smoothing function with a second (short) averaging window 416 is applied to the EMG signal to obtain a smooth forced signal 414.

[0118] The inspiratory phase 408 can be determined based on, for example, when a nurse tells the subject 104 when to perform relaxation breathing and when to perform nasal sniffing, or by using an automated nasal sniffing detector.

[0119] The inspiratory phase 408 can be used as a guide to select the maximum peak 420 in the RMS of the ECG signal removal. During relaxation breathing, peaks in each respiratory cycle are selected, and an average 418 over, for example, 1 minute can be calculated. After relaxation breathing, subject 104 is asked to perform nasal aspiration. Peaks in each nasal aspiration are detected, and the maximum value 420 of all available nasal aspirations (e.g., performed over 1 minute) can then be calculated. The calculated clinical EMG parameter is respiratory effort 422, for example, based on the average peak 418 of relaxation breathing, which is normalized (divided) by the maximum peak 420 obtained from the nasal aspiration procedures. In this way, a measure representing the percentage of respiratory muscle activation relative to the maximum possible muscle recruitment is obtained, used to assess the subject in terms of improvement or deterioration.

[0120] ECG and EMG signals can be buffered to collect samples in blocks or windows of, for example, 10 seconds or 1 minute. Buffering may also include overlap with previous iterations to allow output of 1 minute of data, for example, by advancing each iteration by 10 seconds.

[0121] ECG and EMG signals can optionally be received from, for example, a memory module and further processed (with different averaging windows). This can be used to analyze historical data of an object. The received signals can also be pre-filtered (ECG signals are removed).

[0122] Optionally, the inspiratory phase 408 is determined with the aid of the breathing unit 424 determining the breathing signal 426. For example, an accelerometer can be placed on the subject's chest to measure the tilt of the chest. Alternatively, a flow sensor that measures the pressure in the subject's nose can be used.

[0123] Breathing signals 426 representing relaxed breathing and forced breathing can be obtained, similar to how EMG signals 402 are obtained. Multiple peaks can be obtained from the forced breathing signal and the relaxed breathing signal. The maximum (or minimum, depending on the peak orientation) of the peaks can represent the quality of nasal snoring in the forced breathing signal. The maximum value (e.g., peak amplitude) of the forced breathing peaks can be compared with the values ​​of other forced breathing peaks, threshold values, or relaxed breathing peaks.

[0124] The breathing signal 426 can also be used to determine whether the EMG signal 402 represents the subject 104 breathing in a relaxed or forced manner based on the duration, maximum value, minimum value, and / or noise of the breathing signal 426. For example, if a flow sensor is used to determine the breathing signal 426, a relaxed breathing operation will result in a lower flow rate than sniffing, so that the inspiratory phase 408 can be determined by the flow rate of the flow sensor.

[0125] An automatic nose-sniffing detection module can also be used. The automatic nose-sniffing detector can detect whether the breathing signal 426 is a relaxation signal or a forced signal based on the EMG signal 402. This can be done by pre-calibrating multiple objects, calibrating the object 104 that generates the EMG signal 402, or by using a predetermined threshold voltage (or RMS voltage) in the EMG signal.

[0126] The smoothing function and corresponding averaging window used may depend on user (nurse) preferences, the quality of the acquired signal 402, and / or the available processing equipment / software. For example, the moving root mean square (RMS) can be used for both relaxed and forced signals, and the averaging window is a window of the moving RMS average, such as... Figure 3 As shown in c), d), e), and f). Alternatively, a moving average can be calculated for the signal, or a curve fit can be used to approximate the signal.

[0127] To determine the most suitable averaging window, a set of smoothed signals with various averaging windows can be calculated and compared with each other. The suitability of the averaging window may depend on the judgment of the user (e.g., a nurse) or the average difference between the data points of the actual signal and the smoothed signal.

[0128] There may also be a user input interface for users to input certain parameters. For example, users may be able to input the type of smoothing function for relaxation and compulsion signals, the duration of the first and second average windows and / or ECG removal techniques, as well as any further filtering required.

[0129] Figure 5 An example of a method for determining respiratory effort 422 is shown. Since it is known from several studies that fully automated detection of abrupt maximal inspiratory movements is difficult to achieve in practice, it is best to “guide” the nurse (performing a randomized respiratory effort measurement) within the workflow to obtain the subject’s best possible maximal inspiratory movement. A forced peak 502 can first be identified from a smoothed forced signal 414. A series of candidate peaks 504 can then be selected based on the characteristics of forced peaks 502 and 503. The user (e.g., the nurse) can then select one of the candidate peaks based on user judgment via user input 506 or a user-identified peak 508. The user-identified peak 508 and the smoothed relaxation signal 410 can then be used to calculate respiratory effort 422.

[0130] For example, a relaxation peak can be identified from a smooth relaxation signal 410, and then the average value of the relaxation peak can be determined. For example, breathing effort 422 can be obtained as the average relaxation peak divided by the user-identified peak 508.

[0131] Furthermore, once a valid nasal aspiration procedure has been performed, the user (e.g., a nurse) can manually stop the "series" of nasal aspiration actions. Therefore, the time spent by object 104 performing the forced breathing procedure (nasal aspiration) can be minimized.

[0132] Figure 6 Three graphs representing the characteristics of peak 503 are shown.

[0133] Figure a) above shows the RMS signal of the EMG signal after high-pass filtering (>200Hz). The x-axis corresponds to time in seconds.

[0134] The middle figure b) shows the low-to-high frequency (L / H) component ratio, where the low-frequency component is calculated from the EMG signal from 20 Hz to 200 Hz, while the high-frequency component is calculated from the EMG signal >200 Hz.

[0135] The bottom graph (c) shows the duration of each inhalation.

[0136] The selection of candidate nose suction procedures can be determined based on measurements of certain characteristics 503 of the candidate nose suction procedures (such as the sharpness and flatness of the spectrum) and historical records of nose suction metrics. All of this information can be provided (in real time) to the nurse to select the best possible nose suction procedure and to terminate the nose suction session if a good nose suction procedure is achieved.

[0137] For example, the following feature 503 can be used to identify candidates for nose tucks:

[0138] (i) Sharpness indication, which corresponds to the “sharpness” of the EMG signal (in time) during the maximum maneuver (e.g., the maximum maneuver should begin with a low RMS reading and then return to a low RMS reading with some reasonable time constant). For example, the sharpness indication can be determined based on the first derivative of the RMS signal with respect to time.

[0139] (ii) Spectral flatness indication, which should refer to the “flatness” of the EMG signal spectrum during maximum maneuvering. Spectral flatness is based on a comparison of frequency components between two ranges. During an inspiratory burst, the EMG spectrum is fairly flat. However, when contaminated (postural) muscles are added during nasal sucking, the spectrum will contain more low frequencies. Therefore, the spectral flatness indication can be determined by measuring, for example, the ratio between high-frequency components (e.g., >200 Hz) and low-frequency components (e.g., 20 to 200 Hz). It can be seen that this frequency ratio decreases during poor nasal sucking. The frequency components (high and low) can be derived from the measured power for each frequency range.

[0140] A higher high-frequency to low-frequency ratio in Figure b) indicates a higher proportion of high-frequency components than low-frequency components, suggesting less engagement of the postural muscles during the nose-sniffing process. During the first three nose-sniffing periods (from 75 to 95 seconds), this ratio was noticeably smaller compared to the latter three (from 95 to 120 seconds). Therefore, the last three nose-sniffing periods were considered to perform better because they contained a lower proportion of contaminating low-frequency components from the postural muscles.

[0141] The duration of the EMG signal is shown in the graph below. It can be clearly seen that for relaxed breathing (from 0 to 60 seconds), the duration of breathing is much longer (less sharp) compared to the sniffling action (from 60 to 120 seconds). It can also be seen that the last four sniffles are sharper (shorter in duration) compared to the first two sniffles.

[0142] Candidate peak 504 can be selected based on the spectral flatness and sharpness indicators of each peak (e.g., in real time when the subject is performing a nose suck). Therefore, appropriate feedback can be communicated to the nurse in such a way that the next maximal effort can be performed in a better manner. For example, automatic indications that the action is not strong enough, not fast enough (sharpness), or not performed using only respiratory muscles (e.g., postural muscles are also invoked, resulting in insufficient spectral flatness) are communicated to the nurse to provide further feedback to the subject.

[0143] Feedback can be conveyed through an audiovisual output device, such as a display, speaker, interactive user interface, or any combination thereof. The feedback provided may include whether a forced peak was selected as a candidate peak 504 based on peak sharpness and / or spectral flatness indications, the number of candidate peaks 504 already selected, why no forced peak 502 was selected as a candidate peak 504 (e.g., taking too long, using postural muscles, not strong enough, etc.), and the characteristics measured for each peak and how these characteristics compare to the characteristics of other forced peaks 502. Feedback regarding previous forced peaks 502 may also be shown.

[0144] Based on candidate peak 504 and feedback from each peak, the nurse can decide when to terminate the nose suction procedure session. Similarly, some guidance can be provided to the nurse to make this decision easier. For example, information about the last nose suction performed can be presented. In another example, the trend in the magnitude or quality of the last nose suction performed can be shown to easily observe that there is no longer room for improvement. Early termination of the nose suction session avoids unnecessary stress on object 104.

[0145] Figure 7A second example of a system for determining respiratory effort 422 is shown. For example, electrode 102 can record EMG signals 402 for several minutes (e.g., 3 minutes), with the focus on obtaining the average value of the relaxation phase RMS peak level 418.

[0146] The smoothing relaxation signal 410 and the smoothing forced signal 414 are shown, but to prevent graphic distortion, Figure 4 The average windows (412 and 416) in the data are omitted.

[0147] Once the first few minutes have passed, the average value of 418 and the maximum value of 702 for normal relaxation breathing are calculated, and the system will automatically enter a mode in which the system attempts to identify the RMS peak during maximal effort (sniffling).

[0148] To identify the forced peak from the smooth forced signal 414, information obtained from the relaxed breathing phase (which precedes the maximal effort breathing phase) can be used, for example:

[0149] (i) Compare the peak RMS level during maximal effort with the absolute threshold (minimum) peak RMS level or relative to the peak RMS level during relaxed breathing, for example, assuming that the RMS value of maximal effort inhalation is at least 25% higher than the maximal peak RMS level during relaxed breathing; and

[0150] (Ii) The respiratory signal 426 is used to detect the maximum movement relative to the absolute (minimum) respiratory level (pressure or tilt) or relative to the peak respiratory level during relaxed breathing 704, for example assuming that the maximum effort pressure is at least 25% higher than the maximum pressure peak level 704 during relaxed inhalation. Alternatively, if the tilt of the accelerometer is at least 25% higher than the tilt during relaxed inhalation, for example, the maximum movement can be identified.

[0151] Candidate peak selection can also be based on the relaxed breathing phase. Features 503 of the EMG signal 402 (e.g., sharpness and spectral flatness indicators) can be considered during maximal inspiratory action to provide the nurse with information about the quality of the subject's nasal aspiration. Features 503 can be output to an indicator 706, allowing the nurse to determine which of the candidate peaks 504 is best suited for calculating respiratory effort 422.

[0152] Furthermore, the smoothed relaxation signal 410, relaxation signal, smoothed forced signal 414, forced signal, breathing signal 426, and / or candidate peak 504 can be displayed on the display 706. The nurse can therefore select the user-identified peak 508 based on the candidate peak 504 via the user input interface 506 based on the information on the display 706. Alternatively, the nurse can select the user-identified peak 508 based on the nurse's judgment, based on the ability of the action performed by the object 104 (e.g., sound, duration, etc.).

[0153] Technicians will be able to easily develop processors for performing any of the methods described herein. Therefore, each step of the flowchart can represent a different action performed by the processor and can be executed by the corresponding module of the processing system.

[0154] As described above, the system utilizes a processor to perform data processing. A processor can be implemented in various ways, using software and / or hardware, to perform a variety of required functions. A processor typically employs one or more microprocessors, which can be programmed using software (e.g., microcode) to perform the desired functions. A processor can be implemented as a combination of dedicated hardware for performing some functions and one or more programmed microprocessors and associated circuitry for performing other functions.

[0155] Examples of circuits that may be used in the various embodiments of this disclosure include, but are not limited to, conventional microprocessors, application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

[0156] In various implementations, the processor may be associated with one or more storage media, such as volatile and non-volatile computer memories, such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when run on one or more processors and / or controllers, perform the required functions. The various storage media may be fixed within the processor or controller, or they may be portable, allowing one or more programs stored thereon to be loaded into the processor.

[0157] Those skilled in the art, through studying the accompanying drawings, the disclosure, and the claims, will be able to understand and implement variations of the disclosed embodiments when practicing the claimed invention. In the claims, the word "comprising" does not exclude other elements or steps, and the words "a" or "an" do not exclude a plurality.

[0158] A single processor or other unit can perform the functions of several items described in the claims.

[0159] Although specific measures are described in different dependent claims, this does not imply that combinations of these measures cannot be used advantageously.

[0160] Computer programs can be stored / distributed on suitable media such as optical storage media or solid-state media that are provided together with or as part of other hardware, but they can also be distributed in other forms such as via the Internet or other wired or wireless telecommunications systems.

[0161] If the term “suitable” is used in the claims or description, it should be noted that the term “suitable” is intended to be equivalent to the term “configured as”.

[0162] Any reference numerals in the claims should not be construed as limiting the scope.

Claims

1. A system for determining the breathing effort (422) of an object (104), the system comprising a processor (404) configured to: Receive a relaxation signal indicating that the object (104) is breathing in a relaxed manner; Receive a forced signal indicating that the object (104) is breathing in a forced manner; Multiple forced peaks (502) are derived from the forced signal; Candidate peak (504) is selected from the plurality of forced peaks (502), wherein, Candidate peaks (504) and non-candidate peaks are distinguished based on the characteristics (503) of the forced peak (502); Obtain the user-identified peak (508), wherein the user-identified peak (508) has been selected by the user from the candidate peaks; and Breathing effort (422) is determined based on the relaxation signal and the user-identified peak (508).

2. The system according to claim 1, wherein, The feature (503) of the forced peak (502) used to select the candidate peak (504) includes one or more of the following: The maximum value of each forced peak in the forced peak (502); Sharpness indicator, wherein the sharpness indicator indicates the duration of the forced peak (502); and Spectral flatness indicator, wherein the spectral flatness indicator indicates a comparison between the high-frequency components and the low-frequency components of the forced peak (502).

3. The system according to claim 1 or 2, wherein, The processor (404) is also configured to: When the object (104) breathes in a relaxed manner, it receives a relaxed breathing signal representing a movement or respiratory flow that indicates at least one inhalation action; When the object (104) breathes in a forced manner, it receives a forced breathing signal representing movement or respiratory flow indicating at least one inspiratory action; and Candidate peaks (504) are also selected based on the relaxed breathing signal and the forced breathing signal, wherein the relaxed breathing signal and the forced breathing signal indicate the attributes of the inhalation action.

4. The system of claim 3 further includes a breathing unit (424) for obtaining the relaxation breathing signal and the forced breathing signal, wherein, The breathing unit (424) includes one or more of the following: accelerometer; and Flow sensor.

5. The system according to any one of claims 1 to 2, further comprising an output interface (706) for providing real-time feedback for each of the forced peaks (502), wherein, The feedback indicates one or more of the following: Whether to select the forced peak (502) as the candidate peak (504); The number of candidate peaks currently selected (504); Why was the forced peak (502) not selected as a candidate peak (504) based on the forced peak (502) that was not selected as a candidate peak (504)? The characteristic (503) of one or more of the forced peaks (502); and Feedback to the previous forced peak (502).

6. The system according to any one of claims 1 to 2, further comprising at least two electrodes (102) arranged to receive the relaxation signal and / or the forcing signal when attached to the object.

7. A computer-implemented method for determining the breathing effort (422) of an object (104), the method comprising: Receive a relaxation signal indicating that the object (104) is breathing in a relaxed manner; Receive a forced signal indicating that the object (104) is breathing in a forced manner; Multiple forced peaks (502) are derived from the forced signal; Candidate peaks (504) are selected from the plurality of forced peaks (502) based on the characteristics (503) of the forced peaks (502); Obtain the user-identified peak (508), wherein the user-identified peak (508) has been selected by the user from the candidate peaks; and Breathing effort (422) is determined based on the relaxation signal and the user-identified peak (508).

8. The method according to claim 7, wherein, The feature (503) of the forced peak (502) used to select the candidate peak (504) includes the maximum value of each forced peak in the forced peak (502), and wherein selecting the candidate peak (504) includes comparing the maximum value of each forced peak in the forced peak (502) with one or more of the following: The maximum value of other forced peaks (502); and Threshold-forced peak.

9. The method according to any one of claims 7 or 8, further comprising obtaining a plurality of relaxation peaks from the relaxation signal, wherein, Selecting candidate peaks (504) further includes comparing each of the forced peaks (502) with at least one of the relaxation peaks.

10. The method according to any one of claims 7 to 8, further comprising: When the object (104) breathes in a forced manner, it receives a forced breathing signal representing a movement or respiratory flow rate of at least one inspiratory action; Multiple forced inspiratory peaks were obtained from the forced breathing signal; The selection of candidate peaks (504) is further based on comparing each forced inhalation peak in the forced inhalation peaks with one or more of the following: Other forced inhalation peaks; and Threshold forced inhalation peak.

11. The method of claim 10, further comprising: When the object (104) breathes in a relaxed manner, it receives a relaxed breathing signal representing a movement or respiratory flow that indicates at least one inhalation action; as well as Multiple relaxation inspiratory peaks are obtained based on the aforementioned relaxation breathing signals. The selection of candidate peaks (504) is further based on comparing each of the forced inhalation peaks with at least one of the relaxed inhalation peaks.

12. The method according to any one of claims 7 to 8, wherein, Selecting candidate peak (504) includes: A plurality of sharpness indicators are determined based on the forced signal, wherein each sharpness indicator indicates the duration of the forced peak (502), and wherein a feature (503) of the forced peak (502) includes a corresponding sharpness indicator; and Each of the plurality of sharpness indicators is compared with one or more of the following: Other sharpness indicators; and Threshold sharpness indicator.

13. The method according to any one of claims 7 to 8, wherein, Selecting candidate peak (504) includes: Determine the spectral density of the forced signal for each forced peak in the forced peaks (502); The high spectral density is determined based on the spectral density using frequencies above a threshold frequency. The low spectral density is determined based on the frequency below the threshold frequency according to the spectral density; A spectral flatness indication for each forced peak (502) is determined by comparing the high spectral density with the low spectral density, wherein the forced peak (502) is characterized by a corresponding spectral flatness indication; and The spectral flatness indication for each of the forced peaks (502) is compared with one or more of the following: Other spectral flatness indicators; and Threshold spectrum flatness indicator.

14. The method according to any one of claims 7 to 8, wherein, The method further includes obtaining the forced signal in real time and selecting the candidate peak (504) in real time, wherein the method also includes providing real-time feedback for each of the forced peaks (502), wherein the feedback indicates one or more of the following: Whether to select the forced peak (502) as the candidate peak (504); The number of candidate peaks currently selected (504); Based on the forced peak (502) that was not selected as a candidate peak (504), why was the forced peak (502) not selected as a candidate peak (504)? Feedback to the previous forced peak (502).

15. A computer program product comprising code units, wherein when the code units are run on a processing system, the code units are configured to implement the method according to any one of claims 7 to 14.

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