Cough peak flow rate measurement methods, systems and medical equipment

Abstract

This invention relates to the field of medical device technology and provides a method, system, and medical device for measuring peak cough flow rate. The method includes real-time acquisition of inspiratory and expiratory flow rate signals and airway pressure signals; monitoring changes in the expiratory flow rate signal to identify burst cough events and lock a cough analysis window; calculating the difference in inspiratory and expiratory flow rates within the cough analysis window to obtain a baseline cough flow rate sequence; calculating the airway pressure signal within the cough analysis window using a system leakage factor to obtain a leakage flow rate compensation sequence; determining coefficients based on the ratio of tubing and patient airway resistance parameters and multiplying the expiratory flow rate signals to obtain a tubing resistance compensation sequence; superimposing the above sequences to obtain a corrected flow rate sequence; and extracting extreme value features and evaluating the effectiveness of the corrected flow rate sequence generated after a preset number of iterations to determine the peak cough flow rate value. This invention solves the problems of inaccurate and inefficient measurement results in existing peak cough flow rate measurements.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a method, system and medical device for measuring cough peak flow rate. Background Technology

[0002] Cough peak flow (CPF) is an important physiological indicator used clinically to objectively assess a patient's cough intensity and airway clearance capacity. In the intensive care unit (ICU), CPF is primarily used to predict the success rate of weaning and extubation. Studies have shown that patients with lower CPF have weaker spontaneous sputum expectoration capabilities and a significantly increased risk of sputum blockage, pulmonary infection, and even respiratory failure after extubation, requiring further intubation. In addition, CPF is also used to assess respiratory muscle strength in patients with neuromuscular diseases.

[0003] However, in existing clinical applications of ventilators or anesthesia machines, the measurement of peak cough flow rate still faces the following fundamental problems:

[0004] Lack of integrated tools: Existing general-purpose ventilators or anesthesia machines typically lack dedicated modules for measuring CPF. Healthcare professionals often need to manually calculate or estimate peak values ​​by observing real-time waveforms (flow-time curves) on the ventilator screen, or disconnect the patient from the ventilator and use a handheld spirometer or peak flow meter. However, this method is not only cumbersome but also lacks real-time accuracy and is highly susceptible to subjective errors.

[0005] Leakage interference: Gas leaks are inevitable in the airway system during invasive or non-invasive ventilation (such as mask leaks or leaks at the edge of the endotracheal tube cuff). The leakage rate increases dramatically, especially during the high-pressure burst of a cough. Directly reading the expiratory flow sensor value without leakage compensation will result in a significantly lower measured CPF value than the patient's actual cough capacity, leading to misdiagnosis.

[0006] The Influence of Baseline Flow Rate and Tube Resistance: To maintain tubing patency or provide PEEP (Positive End-Expiratory Pressure), ventilators typically employ a baseline flow rate. Simultaneously, the ventilator tubing and artificial airway (intubation) themselves contain physical resistance. This resistance causes flow rate to attenuate during conduction. Current technology only measures the flow rate at the sensor end and cannot reproduce the patient's true burst of force at the airway opening. This results in measurements that do not accurately reflect the patient's airway clearance capacity, limiting the precise assessment of respiratory muscle function. Summary of the Invention

[0007] Therefore, the purpose of this invention is to provide a method, system, and medical device for measuring cough peak flow rate, so as to fundamentally solve the problems of inaccurate and inefficient measurement results of existing cough peak flow rate methods.

[0008] A method for measuring cough peak flow rate according to an embodiment of the present invention, the method comprising:

[0009] Real-time acquisition of expiratory flow rate signals, inspiratory flow rate signals, and airway pressure signals in the respiratory pathway, and monitoring of the changing characteristics of the expiratory flow rate signals to identify whether an explosive cough event with preset characteristics has occurred in the respiratory pathway.

[0010] If the burst cough event is identified, the sampling time period corresponding to the burst cough event is locked as the cough analysis window, and the difference between the expiratory flow rate signal and the inspiratory flow rate signal at each sampling time in the cough analysis window is calculated to obtain the basic cough flow rate sequence after removing the basic flow deviation interference.

[0011] The airway pressure signal within the cough analysis window is calculated using a preset system leakage factor to obtain a leakage flow rate compensation sequence for each moment.

[0012] Based on the ratio between the obtained current respiratory airway resistance parameters and the patient's airway resistance parameters, a resistance compensation ratio coefficient is determined, and the resistance compensation ratio coefficient is used to multiply the expiratory flow rate signal at each sampling moment in the cough analysis window to obtain the airway resistance compensation sequence at each moment.

[0013] The basic cough flow rate sequence, the leakage flow rate compensation sequence, and the tubing resistance compensation sequence are superimposed according to the sampling time sequence to obtain the corrected flow rate sequence that reflects the true state of the patient's airway outlet.

[0014] Extreme value features are extracted and effectiveness is evaluated from the corrected flow rate sequence generated for a preset number of times to determine the final cough peak flow rate value.

[0015] In addition, the cough peak flow rate measurement method according to the above embodiments of the present invention may also have the following additional technical features:

[0016] Furthermore, the step of monitoring the changes in the expiratory flow rate signal to identify whether a burst cough event meeting preset characteristics has occurred in the respiratory airway includes:

[0017] The real-time acquired expiratory flow rate signal is subjected to moving average filtering to remove high-frequency noise interference;

[0018] Calculate the first time derivative of the filtered expiratory flow rate signal to generate a flow rate change sequence;

[0019] The values ​​in the velocity change rate sequence are compared with a preset explosive force threshold.

[0020] When the number of sampling points where the rate of change of flow rate is greater than the burst force threshold exceeds a preset number of frames, and the instantaneous amplitude of the expiratory flow rate signal is greater than the cough initiation threshold, it is determined that a burst cough event with preset characteristics has occurred in the current respiratory airway, and the current moment is marked as the event start point of the burst cough event.

[0021] Furthermore, the step of locking the sampling time period corresponding to the explosive cough event as the cough analysis window includes:

[0022] Based on the event initiation point of the explosive cough event, search backward for the first local maximum point of the expiratory flow velocity signal and mark it as the flow velocity peak point.

[0023] Continue searching backward from the peak flow point until the instantaneous amplitude of the expiratory flow signal falls below the cough initiation threshold or an inspiratory flow signal rise is detected, and mark this as the end point of the event.

[0024] The cough analysis window is constructed by extracting a data segment from a first preset time period before the starting point to a second preset time period after the end point of the event.

[0025] Furthermore, the step of calculating the airway pressure signal within the cough analysis window using a preset system leakage factor to obtain the leakage flow rate compensation sequence at each moment includes:

[0026] Obtain a pre-stored system leakage factor or a system leakage factor generated by a self-test of the airway, which describes the sealing characteristics of the current breathing airway;

[0027] The square root of the airway pressure signal at each sampling time within the cough analysis window is calculated to obtain the pressure square root sequence.

[0028] The pressure square root sequence is multiplied with the system leakage factor to generate a leakage velocity compensation sequence that dynamically changes with pressure, wherein each element in the leakage velocity compensation sequence represents the instantaneous leakage velocity at the corresponding sampling time.

[0029] Furthermore, before the step of calculating the airway pressure signal within the cough analysis window using a preset system leakage factor to obtain the leakage flow rate compensation sequence at each moment, the method further includes:

[0030] During the routine ventilation phase prior to measuring cough peak flow, select one complete respiratory cycle;

[0031] The inspiratory flow rate data and expiratory flow rate data during the respiratory cycle are integrated over time to calculate the total inhaled tidal volume and the total exhaled tidal volume.

[0032] Calculate the difference between the total inhaled tidal volume and the total exhaled tidal volume as the total leakage over the cycle;

[0033] Calculate the weighted average of the airway pressure signals during the respiratory cycle, and solve for the system leakage factor based on the ratio of the total leakage of the cycle to the square root of the weighted average.

[0034] Furthermore, the step of extracting extreme value features and evaluating the effectiveness of the corrected flow rate sequence generated a preset number of times to determine the final cough peak flow rate value includes:

[0035] The generated corrected flow velocity sequence is smoothed and all local maxima are extracted;

[0036] Remove abnormal noise points with amplitudes less than a preset effective flow velocity threshold from all local maxima, and calculate the time interval between adjacent local maxima.

[0037] If the time interval is less than the preset consecutive cough threshold, then only the local maximum point with the largest amplitude among the adjacent local maximum points is retained as the valid result of a single measurement.

[0038] The largest value among the valid results of the preset number of tests is selected as the final cough peak flow rate value.

[0039] Furthermore, prior to the step of monitoring the changes in the expiratory flow rate signal to identify burst cough events in the respiratory tract that meet preset characteristics, the method further includes:

[0040] Real-time monitoring and analysis of expiratory flow rate signals to determine whether the patient is in a stable end-expiratory state;

[0041] If the patient is determined to be in a stable end-expiratory state, a flow rate monitoring baseline is established, and the airway unit is controlled to maintain a preset airway pressure baseline and baseline flow rate.

[0042] Furthermore, the step of real-time monitoring and analysis of the expiratory flow rate signal to determine whether the patient is in a stable end-expiratory state includes:

[0043] The instantaneous amplitude of the expiratory flow rate signal is monitored in real time and then subjected to moving average filtering.

[0044] Determine whether the instantaneous amplitude of the filtered expiratory flow rate signal is less than the preset resting respiratory threshold;

[0045] If the respiratory rate is below the resting respiratory threshold, a state timer is started.

[0046] The duration of the state timer is continuously monitored. If the duration exceeds the preset end-expiratory determination time threshold and the instantaneous amplitude during the timing period is always less than the respiratory resting threshold, an end-expiratory stable state determination signal is generated.

[0047] Another embodiment of the present invention aims to provide a cough peak flow rate measurement system, the system comprising:

[0048] The signal acquisition and recognition module is used to acquire the expiratory flow rate signal, inspiratory flow rate signal and airway pressure signal in the respiratory airway in real time, and monitor the change characteristics of the expiratory flow rate signal to identify whether an explosive cough event that meets the preset characteristics has occurred in the respiratory airway.

[0049] The basic flow rate processing module is used to lock the sampling time period corresponding to the burst cough event as the cough analysis window if the burst cough event is detected, and calculate the difference between the expiratory flow rate signal and the inspiratory flow rate signal at each sampling time in the cough analysis window to obtain the basic cough flow rate sequence after removing the basic flow deviation interference.

[0050] The leakage compensation module is used to calculate the airway pressure signal within the cough analysis window using a preset system leakage factor to obtain the leakage flow rate compensation sequence at each moment.

[0051] The resistance compensation module is used to determine the resistance compensation ratio coefficient based on the ratio between the obtained tubing resistance parameters of the current respiratory path and the patient's airway resistance parameters, and to use the resistance compensation ratio coefficient to perform a product operation on the expiratory flow velocity signal at each sampling moment in the cough analysis window to obtain the tubing resistance compensation sequence at each moment.

[0052] The corrected flow rate determination module is used to superimpose the basic cough flow rate sequence, the leakage flow rate compensation sequence, and the tubing resistance compensation sequence according to the sampling time sequence to obtain a corrected flow rate sequence that reflects the true state of the patient's airway outlet.

[0053] The cough peak flow rate determination module is used to extract extreme value features and evaluate the effectiveness of the corrected flow rate sequence generated by a preset number of times, and determine the final cough peak flow rate value.

[0054] Another embodiment of the present invention aims to provide a medical device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the cough peak flow rate measurement method as described above.

[0055] The cough peak flow rate measurement method provided in this invention achieves millisecond-level capture and automatic identification of burst cough events by real-time monitoring of the first-order time derivative of the expiratory flow rate signal and comparing it with a preset burst force threshold. This fundamentally solves the problems of cumbersome operation, poor real-time performance, and large subjective errors caused by nurses relying on visual observation of respiratory waveforms in existing technologies, significantly improving the automation level and evaluation efficiency of clinical measurements. Furthermore, by calculating the difference between the expiratory and inspiratory flow rate signals at each sampling moment within the cough analysis window, it realizes the control of the baseline offset flow supplied by the ventilator. Precise removal of disturbances ensures that the extracted baseline flow rate sequence accurately and purely reflects the net flow generated by the patient's spontaneous cough, greatly improving the reliability of the raw measurement data. Furthermore, by introducing a dynamic leakage compensation mechanism based on the square root of the airway pressure signal, combined with the system leakage factor pre-solved during routine ventilation, adaptive correction of the instantaneous leakage flow rate under the high pressure generated by a cough burst is achieved. This effectively compensates for flow loss caused by insufficient airtightness of the artificial airway cuff or mask, solving the problem of significantly lower measurement results in non-absolutely sealed environments using existing technologies. Moreover, by establishing… A resistance compensation model based on the proportional relationship between external tubing resistance parameters and patient airway resistance parameters, and by multiplying the sampled flow rates, was developed to simulate and reconstruct the attenuation of flow rates due to physical obstructions during airway conduction. This successfully reconstructed a corrected flow rate sequence reflecting the true state of the patient's airway outlet (lung burst power), providing core data support for the accurate clinical assessment of patients' respiratory muscle function. Furthermore, by performing Gaussian smoothing, local maxima extraction, and time interval filtering based on a consecutive cough threshold on the corrected flow rate sequence, intelligent removal of invalid sampling noise and interference from continuous coughing actions was achieved. This ensures the extraction of unique and most representative effective results from complex physiological signals, greatly improving the algorithm's anti-interference ability and the robustness of measurement values. By analyzing the instantaneous amplitude of flow rate in real time and using a state timer to determine the end-expiratory steady state, and controlling the airway unit accordingly to establish a unified flow rate monitoring benchmark, the measurement process is triggered at a standardized respiratory mechanics starting point. This effectively avoids repeated measurement deviations caused by incomplete exhalation or airflow fluctuations, ensuring the all-weather stability of the guidance effect and the consistency of data. It also solves the problems of inaccurate and inefficient measurement results of existing cough peak flow rates. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the cough peak flow rate measurement method in the first embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of the measurement process of the cough peak flow rate measurement method in the first embodiment of the present invention;

[0058] Figure 3This is a schematic diagram of the cough peak flow rate measurement system in the second embodiment of the present invention;

[0059] Figure 4 This is a schematic diagram of the structure of the medical device in the third embodiment of the present invention;

[0060] The following detailed description of the embodiments will further illustrate the present invention in conjunction with the above-described accompanying drawings. Detailed Implementation

[0061] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0062] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0064] Example 1

[0065] Please see Figure 1 The image shows a method for measuring cough peak flow rate in the first embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown. The method for measuring cough peak flow rate provided by the embodiment of the present invention includes:

[0066] Step S10: Real-time acquisition of expiratory flow rate signal, inspiratory flow rate signal and airway pressure signal in the respiratory airway, and monitoring of the change characteristics of expiratory flow rate signal to identify whether an explosive cough event with preset characteristics has occurred in the respiratory airway.

[0067] In one embodiment of the present invention, the method is applied to a medical device (specifically, a respiratory therapy device), which includes invasive ventilators, non-invasive ventilators, anesthesia machines, or pulmonary function testing instruments. Specifically, the respiratory therapy device in this embodiment is actually an intensive care unit ventilator, primarily used in intensive care units or operating rooms, but can also be used for respiratory support in general wards. Furthermore, the respiratory therapy device can be used in conjunction with artificial airways (such as endotracheal tubes or tracheostomy tubes) or masks to establish closed or semi-closed gas circuits. Further, the hardware architecture of the respiratory therapy device includes, but is not limited to, a central processing unit, a data acquisition module, a gas path control unit, a sensor detection unit, and a human-machine interface module. The central processing unit (CPU) serves as the core control unit, connecting all modules. The data acquisition module includes a high-precision analog-to-digital converter. The airway control unit includes an inspiratory valve, an expiratory valve, and a turbine or air supply pump. The sensing unit includes an inspiratory flow sensor installed at the inspiratory branch port, an expiratory flow sensor installed at the expiratory branch port, and an airway pressure sensor located at the patient's airway opening or the machine's outlet. These sensors are connected to the data acquisition module via analog signal lines or a digital bus to convert gas physical quantities into electrical signals in real time. The human-machine interface module displays waveforms and receives operating commands from medical personnel.

[0068] In one embodiment of the present invention, the step of monitoring the changes in the expiratory flow rate signal to identify whether a burst cough event meeting preset characteristics has occurred in the respiratory airway includes:

[0069] The real-time acquired expiratory flow rate signal is subjected to moving average filtering to remove high-frequency noise interference;

[0070] Calculate the first time derivative of the filtered expiratory flow rate signal to generate a flow rate change sequence;

[0071] The values ​​in the velocity change rate sequence are compared with a preset explosive force threshold.

[0072] When the number of sampling points where the rate of change of flow rate is greater than the burst force threshold exceeds the preset number of frames, and the instantaneous amplitude of the expiratory flow rate signal is greater than the cough initiation threshold, it is determined that a burst cough event with preset characteristics has occurred in the current respiratory airway, and the current moment is marked as the event start point of the burst cough event.

[0073] Specifically, the central processing unit (CPU) first allocates a fixed-length first-in-first-out (FIFO) data buffer queue in its internal memory to store the raw expiratory flow rate data from the most recent sampling periods. Whenever the data acquisition module sends in new sampling point data, the CPU pushes it to the end of the queue while removing the oldest data from the head of the queue. Next, the CPU calculates the arithmetic mean of all the data in the buffer queue and uses this average as the filtered expiratory flow rate signal for the current moment. This sliding window averaging process effectively smooths out transient spikes caused by airflow turbulence or circuit interference, preserving the true trend of airflow changes.

[0074] Next, the central processing unit (CPU) calculates the first-order time derivative of the filtered expiratory flow rate signal. Specifically, the CPU reads the filtered flow rate value at the current sampling moment and the filtered flow rate value at the previous sampling moment, calculates the difference between the two, and divides this difference by the sampling time interval. This calculation process is performed in real time within each sampling cycle, thereby generating a series of values ​​reflecting the rate of change of expiratory flow rate, i.e., a flow rate change sequence. This sequence physically represents the acceleration of airflow and can sensitively capture the explosive increase in airflow at the onset of a cough.

[0075] Furthermore, the CPU calls the burst force threshold stored in the configuration register, which is a pre-set acceleration threshold used to distinguish between normal breathing and burst coughing. The CPU compares the real-time calculated rate of change of flow rate with this burst force threshold. Simultaneously, the CPU monitors the instantaneous amplitude of the filtered expiratory flow rate signal and compares it with a preset cough initiation threshold, which is used to eliminate minor airflow disturbances.

[0076] Finally, the central processing unit (CPU) starts a status counter to determine the event. If the rate of change of the current flow rate is greater than the burst force threshold, the counter is incremented; if it is less, the counter is reset to zero. When the cumulative value of the counter exceeds a preset number of frames (e.g., three consecutive sampling cycles), it indicates that the airflow is in a state of continuous and violent acceleration, and at the same time, the instantaneous amplitude of the expiratory flow rate signal also exceeds the cough initiation threshold, indicating that there has been substantial exhalation. At this time, the CPU confirms that all triggering conditions are met, immediately updates the system status flag to the cough event detection status, determines that a burst cough event with preset characteristics has occurred in the current respiratory airway, and reads the current system clock value, recording it as the event start point of the burst cough event for subsequent data window positioning and interception.

[0077] Furthermore, in one embodiment of the present invention, the step of monitoring the changes in the expiratory flow rate signal to identify a burst cough event in the respiratory tract that meets preset characteristics further includes, before the above-mentioned step:

[0078] Real-time monitoring and analysis of expiratory flow rate signals to determine whether the patient is in a stable end-expiratory state;

[0079] If the patient is determined to be in a stable end-expiratory state, a flow rate monitoring baseline is established, and the airway unit is controlled to maintain a preset airway pressure baseline and baseline flow rate.

[0080] Furthermore, in one embodiment of the present invention, the step of real-time monitoring and analysis of the expiratory flow rate signal to determine whether the patient is in a stable end-expiratory state includes:

[0081] Real-time monitoring of the instantaneous amplitude of the expiratory flow rate signal and processing by moving average filtering;

[0082] Determine whether the instantaneous amplitude of the filtered expiratory flow rate signal is less than the preset resting respiratory threshold;

[0083] If the respiratory rate is below the resting respiratory threshold, a state timer is started.

[0084] The duration of the state timer is continuously monitored. If the duration exceeds the preset end-expiratory determination time threshold and the instantaneous amplitude during the timing period is always less than the resting respiratory threshold, a determination signal for end-expiratory stable state is generated.

[0085] Specifically, the central processing unit (CPU) first monitors the expiratory flow rate signal transmitted by the data acquisition module in real time. To eliminate signal fluctuations caused by weak vibrations or electronic noise in the airway, the CPU uses a moving average algorithm to smooth the raw flow rate data, calculating the arithmetic mean of the values ​​at the most recent sampling points to obtain a smooth and stable instantaneous amplitude. Next, the CPU compares this filtered instantaneous amplitude with a preset resting respiratory threshold in real time. This resting respiratory threshold is a small positive value close to zero, used to define whether the airflow is in a near-static state.

[0086] Next, if the central processing unit (CPU) detects that the current instantaneous amplitude is below the resting respiratory threshold, it indicates that the patient may have completed the exhalation of the previous breath, and the airflow is about to return to zero. At this point, the CPU immediately triggers an internal software or hardware state timer to start timing. During the timing process, the CPU continuously monitors new flow rate sampling points. Once it detects that the instantaneous amplitude jumps above the resting respiratory threshold at any time (e.g., the patient suddenly speaks or begins to inhale), it immediately resets the state timer and waits for the next low flow rate moment.

[0087] Furthermore, the central processing unit (CPU) only confirms that the patient's airway status is completely stable and free from airflow disturbance when the accumulated time of the status timer exceeds a preset end-expiratory time threshold (e.g., lasting for several hundred milliseconds) and the flow rate remains within the resting range during this period. At this point, the CPU generates and outputs an end-expiratory stabilization status determination signal, which serves as a trigger to activate the subsequent baseline establishment process.

[0088] Finally, in response to the end-expiratory steady-state determination signal, the central processing unit sends a command to the airway control unit. The airway control unit adjusts the opening of the inspiratory and expiratory valves according to the command, stabilizing the pressure within the breathing circuit at a preset airway pressure reference (e.g., positive end-expiratory pressure level), while simultaneously controlling the inspiratory branch to output a constant, low-flow baseline velocity. This baseline velocity not only flushes out dead space gas in the tubing but also provides a known and stable physical reference for removing machine-induced flow bias interference when subsequently calculating the cough peak flow rate, thus ensuring the consistency and accuracy of the measurement environment.

[0089] Step S20: If a burst cough event is identified, the sampling time period corresponding to the burst cough event is locked as the cough analysis window, and the difference between the expiratory flow rate signal and the inspiratory flow rate signal at each sampling time in the cough analysis window is calculated to obtain the basic cough flow rate sequence after removing the basic skew flow interference.

[0090] In one embodiment of the present invention, when the event recognition logic of the central processing unit (CPU) confirms the detection of a burst cough event, it immediately triggers a data locking command. The CPU suspends overwrite operations on the current circulating data buffer, or copies the original data segment containing a period of time before and after the event from the circulating buffer to a dedicated analysis memory area. Next, the CPU calls its internal arithmetic logic unit to read the corresponding expiratory flow rate and inspiratory flow rate signal values ​​for each sampling point in the analysis memory area. The CPU performs a subtraction operation, subtracting the inspiratory flow rate signal from the expiratory flow rate signal, thereby eliminating the baseline flow component actively supplied by the ventilator. This operation traverses all data points in the analysis memory area, ultimately generating a new digital sequence, namely, the baseline cough flow rate sequence after removing baseline flow interference.

[0091] In one embodiment of the present invention, the step of locking the sampling time period corresponding to the burst cough event as the cough analysis window includes:

[0092] Based on the event initiation point of the burst cough event, search backward for the first local maximum point of the expiratory flow velocity signal and mark it as the flow velocity peak point;

[0093] Continue searching backward from the peak flow point until the instantaneous amplitude of the expiratory flow signal falls below the cough initiation threshold or an increase in the inspiratory flow signal is detected, and mark this as the end point of the event.

[0094] Extract the data segment from the first preset duration before the start point to the second preset duration after the end point of the event, and construct a cough analysis window.

[0095] Specifically, the CPU first accesses the event start point address recorded in memory, which corresponds to the moment the explosive coughing action just begins. Starting from this address, the CPU reads the sampled values ​​of the expiratory flow rate signal in ascending chronological order and compares the magnitudes of adjacent sampled values. When the CPU finds that a later sampled value is less than a previous sampled value, and a previous sampled value is greater than any value preceding it, it determines that a local maximum of the waveform has been found. The CPU then locks the time position corresponding to this local maximum as the peak flow rate point, which typically represents the moment when the coughing airflow is most intense.

[0096] Next, the central processing unit (CPU) uses this peak flow rate as a new starting point and continues traversing the data. During this traversal, the CPU performs a dual condition check: firstly, it compares the current expiratory flow rate value with a preset cough initiation threshold, which is typically set as a low flow rate value close to zero; secondly, it monitors the inspiratory flow rate data to determine if an inspiratory action has occurred. Once the expiratory flow rate decreases below the threshold, it indicates that the cough exhalation has ended; or the inspiratory flow rate suddenly increases, indicating that the patient has begun the next inhalation. As soon as either of these conditions is met, the CPU immediately terminates the search and records this moment as the event end point.

[0097] Finally, to ensure that subsequent signal compensation algorithms have sufficient boundary data, the CPU does not directly use the data from the start point to the end point, but instead performs forward and backward expansion. The CPU subtracts a first preset duration (e.g., several hundred milliseconds) from the timestamp of the event start point to obtain the window start pointer; it adds a second preset duration to the timestamp of the event end point to obtain the window end pointer. Based on these two pointers, the CPU completely extracts this segment of data from the original data stream, encompassing the entire cough process and its preceding and following resting states, defining it as the cough analysis window and using it as the sole data source for subsequent leakage compensation and resistance compensation.

[0098] Step S30: Calculate the airway pressure signal within the cough analysis window using a preset system leakage factor to obtain the leakage flow rate compensation sequence at each moment.

[0099] In one embodiment of the invention, after the central processing unit (CPU) completes the data capture of the cough analysis window, it initiates a leakage compensation calculation program. The CPU invokes its internal mathematical operation logic to perform point-by-point calculations on the pressure change trajectory within the analysis window, combined with characteristic parameters reflecting the airway sealing performance. This step aims to simulate the amount of gas loss caused by the inability of the airway system to achieve absolute sealing under high-pressure cough conditions, thereby providing corrective data for accurately reconstructing the actual airflow expelled from the patient's lungs.

[0100] In one embodiment of the present invention, the step of calculating the airway pressure signal within the cough analysis window using a preset system leakage factor to obtain the leakage flow rate compensation sequence at each moment includes:

[0101] Obtain the system leakage factor, which is pre-stored or generated by the airway self-test. The system leakage factor describes the sealing characteristics of the current breathing airway.

[0102] The square root of the airway pressure signal at each sampling time within the cough analysis window is calculated to obtain the pressure square root sequence.

[0103] The pressure square root sequence is multiplied with the system leakage factor to generate a leakage velocity compensation sequence that dynamically changes with pressure. Each element in the leakage velocity compensation sequence represents the instantaneous leakage velocity at the corresponding sampling time.

[0104] Specifically, the central processing unit (CPU) first accesses its associated memory unit to retrieve the pre-stored system leakage factor. This system leakage factor is a quantitative parameter whose value is determined during the device's self-test process or automatically calculated and updated by the CPU based on the deviation between inhaled and exhaled tidal volumes during normal ventilation. The magnitude of this factor directly reflects the degree of leakage in the entire physical circuit consisting of the ventilator, breathing tubing, humidifier, and artificial airway, and serves as the fundamental coefficient for dynamic compensation.

[0105] Next, the central processing unit (CPU) extracts the airway pressure signals stored in the cough analysis window. Since, according to aerodynamic principles, the velocity of gas leaking through a gap is directly proportional to the square root of the pressure difference, the CPU invokes its internal mathematical operation instruction set to perform a square root operation on the airway pressure signal values ​​at each sampling point within the analysis window. This operation converts the pressure signal into a numerical sequence reflecting the characteristics of the leakage driving force—the pressure square root sequence.

[0106] Finally, the central processing unit (CPU) performs a sequence-weighted calculation. At each sampling moment, the CPU reads the square root value of the pressure at that moment and multiplies it with the retrieved system leakage factor. Through this point-by-point multiplication, the CPU transforms the static leakage factor into a dynamic flow rate compensation value linked to real-time airway pressure fluctuations. Each product result represents the amount of flow rate lost due to leakage at that sampling moment. The product results from all moments are arranged in chronological order to form a complete leakage flow rate compensation sequence. This sequence can accurately track the instantaneous leakage fluctuations caused by drastic pressure changes during coughing, ensuring the real-time performance and accuracy of the compensation calculation.

[0107] Furthermore, in one embodiment of the present invention, before the step of calculating the airway pressure signal within the cough analysis window using a preset system leakage factor to obtain the leakage flow rate compensation sequence at each moment, the method further includes:

[0108] During the routine ventilation phase prior to measuring cough peak flow, select one complete respiratory cycle;

[0109] Integrate the inspiratory and expiratory flow rate data over time during the respiratory cycle to calculate the total inhaled tidal volume and the total exhaled tidal volume.

[0110] Calculate the difference between the total inhaled tidal volume and the total exhaled tidal volume as the total leakage over the cycle;

[0111] Calculate the weighted average of the airway pressure signal during the respiratory cycle, and solve for the system leakage factor based on the ratio of the total leakage of the cycle to the square root of the weighted average.

[0112] Specifically, during the patient's normal spontaneous breathing or mechanical ventilation, the central processing unit (CPU) continuously monitors the flow rate waveform of the breathing circuit via the data acquisition module. The CPU identifies the start and end points of the flow rate in the waveform, locking onto a respiratory cycle containing a complete inspiratory and expiratory phase as the calculation sample. This step ensures that subsequent calculations are based on a closed respiratory cycle, thus guaranteeing the applicability of the law of conservation of mass in airway analysis.

[0113] Next, the central processing unit (CPU) processes the digitized flow rate sequence within the locked period. Specifically, the CPU calls its internal accumulator to accumulate the product of each sampled flow rate value during the inspiratory phase and the sampling period, calculating the total volume of gas entering the airway during that inhalation, i.e., the total inhaled tidal volume. Simultaneously, the CPU performs the same accumulation and integration operation on each sampled flow rate value during the expiratory phase, calculating the volume of gas expelled from the airway during that exhalation, i.e., the total exhaled tidal volume.

[0114] Subsequently, the central processing unit performs a subtraction operation, subtracting the total exhaled tidal volume from the total inhaled tidal volume. In the physical model of the respiratory system, if sensor errors are not considered, the difference between inhaled and exhaled volumes is mainly caused by air leakage from physical gaps at airway connections, mask edges, or artificial airway cuffs. Therefore, this difference directly represents the total leakage within that cycle.

[0115] Finally, the CPU statistically analyzes the pressure data within the cycle, calculating the time-weighted average of the airway pressure signal over the entire cycle to obtain the average pressure load borne by the airway during that phase. To establish a mathematical mapping between leakage and pressure, the CPU first performs a square root operation on this weighted average pressure. Next, the CPU calculates the ratio of the total leakage over the cycle to this square root value. This ratio eliminates the influence of pressure fluctuations, abstracts the inherent physical leakage characteristics of the airway system, and the CPU stores it as a system leakage factor in its internal registers, providing a data benchmark for subsequent precise real-time leakage flow rate compensation under cough-induced high-pressure conditions.

[0116] Step S40: Based on the ratio of the obtained current respiratory airway resistance parameters to the patient's airway resistance parameters, determine the resistance compensation ratio coefficient, and use the resistance compensation ratio coefficient to perform a product operation on the expiratory flow rate signal at each sampling moment in the cough analysis window to obtain the airway resistance compensation sequence at each moment.

[0117] In one embodiment of the present invention, the steps of determining the resistance compensation ratio coefficient based on the ratio between the obtained current respiratory airway resistance parameters and the patient's airway resistance parameters, and using the resistance compensation ratio coefficient to multiply the expiratory flow velocity signal at each sampling moment in the cough analysis window to obtain the airway resistance compensation sequence at each moment include:

[0118] Retrieve pre-stored external tubing resistance parameters and patient airway resistance parameters corresponding to the current artificial airway specifications;

[0119] Calculate the ratio of external tubing resistance parameters to patient airway resistance parameters to determine the resistance compensation ratio coefficient;

[0120] Extract the expiratory flow velocity signal within the cough analysis window, and multiply the flow velocity value at each sampling time with the resistance compensation ratio coefficient to generate a pipeline resistance compensation sequence.

[0121] Specifically, the central processing unit (CPU) first retrieves relevant impedance data from non-volatile memory. The external tubing resistance parameter is automatically calculated during the ventilator's self-test procedure by delivering a known flow rate and measuring the pressure drop across the tubing. The patient airway resistance parameter, on the other hand, is obtained by the CPU from a pre-defined hardware resistance characteristic table based on the intubation type or inner diameter input by healthcare personnel via the interactive interface. These two parameters quantify the degree of obstruction to airflow in the physical channel between the patient's lung exit and the ventilator sensors.

[0122] Next, the central processing unit (CPU) invokes its internal mathematical processing module to perform the division logic. The CPU uses the value representing the resistance of the external tubing as the dividend and the value representing the patient's own airway resistance as the divisor. By calculating the ratio between the two, it obtains a constant reflecting the characteristics of flow velocity conduction loss, namely the resistance compensation ratio coefficient. Physically, this coefficient represents the attenuation ratio of the flow velocity measured by the sensor relative to the actual flow velocity at the patient's airway opening due to the presence of the external tubing.

[0123] Furthermore, the central processing unit (CPU) processes the cough analysis window data locked in the analysis memory area point by point. At each sampling moment, the CPU reads the instantaneous expiratory flow rate signal value. To compensate for measurement loss caused by tubing resistance, the CPU drives its multiplier circuit to multiply the instantaneous flow rate value with the aforementioned determined resistance compensation ratio.

[0124] Finally, the central processing unit (CPU) rearranges each calculated product result according to the original sampling time sequence to generate a complete pipeline resistance compensation sequence. Each element in this sequence represents the supplementary flow velocity value that was not directly detected by the sensor at a specific sampling instant due to external pipeline obstruction. Through this proportional correction based on the physical loop impedance model, the CPU achieves a software simulation of the energy loss of airflow along the transmission path, providing crucial correction data for the final unbiased synthesis of the true cough peak flow velocity.

[0125] Step S50: The basic cough flow rate sequence, the leakage flow rate compensation sequence, and the tubing resistance compensation sequence are superimposed according to the sampling time sequence to obtain the corrected flow rate sequence that reflects the true state of the patient's airway outlet.

[0126] In one embodiment of the present invention, the step of superimposing the baseline cough flow rate sequence, the leakage flow rate compensation sequence, and the tubing resistance compensation sequence according to the sampling time sequence to obtain the corrected flow rate sequence reflecting the true state of the patient's airway outlet includes:

[0127] An empty data buffer with the exact same length as the cough analysis window is created in memory to serve as the storage medium for the corrected flow rate sequence;

[0128] Simultaneously, three data address pointers are invoked to point to the starting positions of the basic cough flow rate sequence, leakage flow rate compensation sequence, and pipeline resistance compensation sequence stored in different memory areas.

[0129] According to the chronological order of sampling time, extract the values ​​at corresponding times in the above three sequences and perform superposition operation;

[0130] Each instantaneous composite value obtained from the superposition operation is sequentially stored into an empty data buffer to generate a corrected flow rate sequence that reflects the true state of the patient's airway outlet.

[0131] Specifically, to ensure the logical rigor of the computation, the CPU first examines the three numerical sequences to be fused. Since these three sequences were all calculated within the same cough analysis window and based on the same original sampling timestamps, they naturally possess consistency along the timeline. The CPU allocates a contiguous block of storage for the corrected flow rate data and initializes the address indexes, preparing for the point-by-point merging operation across sequences.

[0132] Next, the central processing unit (CPU) performs a multi-channel synchronous read operation. Within each tiny sampling interval, the CPU extracts the baseline cough flow rate value at that moment from the first memory area (this value has removed the interference from the machine's active air delivery) via the data bus, the leakage flow rate compensation value at the same moment from the second memory area (this value corresponds to the flow loss under pressure), and the pipeline resistance compensation value at the same moment from the third memory area (this value corresponds to the flow rate attenuation caused by physical impedance).

[0133] Furthermore, the central processing unit (CPU) drives its internal high-speed adder circuit to sum the three velocity components with the same physical dimensions. In physical logic, this step re-fills the base velocity waveform with the flow components that were previously missed or mismeasured by the sensors at the device end. Specifically, the addition of leakage compensation corrects airflow losses caused by poor sealing, and the addition of resistance compensation restores the velocity energy levels that failed to be conducted to the sensor end due to pipeline obstructions. Through this three-pronged superposition, the CPU virtually recreates the full picture of the explosive airflow actually generated by the patient at the artificial airway opening at the software algorithm level.

[0134] Finally, the central processing unit (CPU) stores the calculated real-time synthetic flow rate value into the corresponding position in the calibrated flow rate sequence. As the time pointer traverses the entire cough analysis window, the CPU ultimately constructs a continuous, multi-dimensionally corrected calibrated flow rate sequence. This sequence accurately depicts the true dynamic trajectory of airflow intensity changes over time during the patient's cough, eliminating measurement biases caused by the physical characteristics of the ventilator and the airway environment, providing the most reliable data foundation for subsequent extraction of peak data with clinical diagnostic value.

[0135] Step S60: Extract extreme value features and evaluate the effectiveness of the corrected flow rate sequence generated for a preset number of times to determine the final cough peak flow rate value;

[0136] In one embodiment of the present invention, the step of extracting extreme value features and evaluating the effectiveness of the corrected flow rate sequence generated a preset number of times to determine the final cough peak flow rate value includes:

[0137] The generated corrected flow velocity sequence is smoothed and all local maxima are extracted.

[0138] Remove abnormal noise points with amplitudes less than a preset effective flow velocity threshold from all local maxima, and calculate the time interval between adjacent local maxima.

[0139] If the time interval is less than the preset consecutive cough threshold, then only the local maximum point with the largest amplitude among the adjacent local maximum points is retained as the valid result of a single measurement.

[0140] The largest value among the valid results of the preset number of tests is selected as the final cough peak flow rate value.

[0141] Specifically, the CPU first performs a secondary smoothing operation on the corrected flow rate sequence. Although the previous steps have already performed preliminary correction, in order to eliminate digital quantization noise that may accumulate during the calculation process, the CPU calls a Gaussian smoothing algorithm or a multi-point moving average algorithm to further denoise the sequence, making the flow rate curve smoother. Subsequently, the CPU uses the first-order differential sign judgment method to traverse the entire sequence, find the critical points where the values ​​change from an upward trend to a downward trend, and identify and mark these points as local maxima.

[0142] Next, the central processing unit (CPU) performs validity filtering. The CPU compares the instantaneous amplitude of each local maximum point with a preset effective flow rate threshold. This threshold is typically set as a minimum flow rate limit representing a substantial coughing action; any minute fluctuations below this limit are considered abnormal noise caused by baseline drift or airway oscillations and are discarded. For the remaining effective maxima, the CPU records their positions on the time axis and calculates the time difference between two adjacent maxima points, i.e., the time interval.

[0143] Furthermore, the central processing unit (CPU) executes the chain cough determination logic. In clinical practice, a patient's single coughing effort may manifest as multiple consecutive bursts of air. If the time interval between two adjacent maximum values ​​is less than a preset chain cough threshold (e.g., several hundred milliseconds), the CPU determines that these two peaks belong to continuous fluctuations within the same coughing attempt. At this point, the CPU drives its internal numerical comparison circuit to determine the magnitude of the two values, retaining only the one with the larger value and ignoring the smaller value as a secondary peak. In this way, the CPU ensures that each independent coughing action corresponds to only one representative and valid result.

[0144] Finally, the central processing unit (CPU) executes a multi-cycle optimization strategy. According to clinical measurement guidelines, patients typically need to repeat a preset number of cough attempts (e.g., three). The CPU stores the valid results from each measurement sequentially into a result buffer. After completing the preset number of acquisitions, the CPU iterates through all values ​​in the buffer, using numerical sorting or a maximum value retrieval algorithm to identify the value with the highest absolute value. This value is defined as the optimal value reflecting the patient's respiratory muscle burst potential, and the CPU determines it as the final cough peak flow rate value, transmitting it to the human-computer interface for result display. This process automates data filtering and optimization, completely eliminating subjective biases introduced by manual estimation.

[0145] To more intuitively understand the application of the embodiments of the present invention in actual testing scenarios, please refer to... Figure 2 The pressure and flow rate waveforms in the measurement process are shown in the figure. The complete test process of this embodiment is described in detail. The measurement process is completed synchronously by the central processing unit of the respiratory therapy device, which coordinates the airway control unit and the sensing and detection unit. The whole process can be divided into the benchmark establishment stage, the action recognition stage, the data compensation stage, and the result selection stage.

[0146] First, in the benchmark establishment phase (corresponding to) Figure 2(Area before "Expiration complete, begin measurement"): The respiratory therapy device initially operates in conventional ventilation mode, with the central processing unit (CPU) continuously monitoring the patient's respiratory waveform via the data acquisition module. When the CPU detects that the instantaneous amplitude of the expiratory flow rate signal is consistently below the preset resting respiratory threshold, and the duration exceeds the end-expiratory time threshold, it determines that the patient has completed natural exhalation and is in a resting lung state. At this point, the CPU issues a command to drive the expiratory and inspiratory valves to coordinate and adjust, maintaining the pressure within the airway at a preset airway pressure baseline (such as positive end-expiratory pressure) and establishing a stable baseline flow rate. Figure 2 As shown by the vertical dashed line at the "Start Measurement" point, the flow velocity curve enters a stable low-amplitude plateau period at this time, providing a unified measurement starting point for subsequent explosive movements.

[0147] Next, in the action recognition stage (corresponding to) Figure 2 In the "deep inspiration" and "forced cough" regions: After the baseline environment is established, the central processing unit maintains high-frequency sampling. When the patient performs a deep inspiration as clinically instructed, the airway pressure signal shows a momentary drop, while the inspiratory flow rate signal shows a significant increase (e.g., Figure 2 (The flow rate fluctuation below "deep inhalation" is shown). Immediately following, when the patient experiences a sudden, explosive cough, the expiratory flow rate signal exhibits an extremely steep upward trend. The central processing unit (CPU) calculates the first-order time derivative of the flow rate signal in real time, detecting that the rate of change of flow rate instantaneously exceeds a preset burst force threshold, and the amplitude surpasses the cough initiation threshold, thus accurately identifying the occurrence of an explosive cough event. At this moment, the CPU immediately activates data locking logic, using this burst moment as the core, extracting a sampled data segment containing the complete ascending and descending phases to construct a cough analysis window.

[0148] Subsequently, in the data compensation phase (for each sampling point within the analysis window): the central processing unit calls its internal arithmetic logic unit to perform real-time three-dimensional correction on the data within the locked window to restore the original data. Figure 2 The physical quantity marked as cough peak flow (CPF):

[0149] 1. Flow offset removal: Since the ventilator maintains a baseline flow rate during the measurement, the central processing unit calculates the difference between the flow rate signals at the expiratory and inspiratory ends at the same time to eliminate background interference caused by the machine's active ventilation.

[0150] 2. Leakage Compensation: In response to the drastic fluctuations in airway pressure during coughing (as shown by the spikes in the pressure waveform), the central processing unit extracts the pressure signal and performs a square root operation. Combined with the pre-stored system leakage factor, it calculates the flow rate lost at each moment due to insufficient airway tightness and compensates it back into the flow rate sequence.

[0151] 3. Resistance Correction: The central processing unit determines a compensation coefficient using the ratio of the external pipeline resistance parameter to the artificial airway resistance parameter, and then multiplies this coefficient by the real-time flow velocity. This step aims to restore the flow velocity energy level in the waveform that failed to be conducted to the sensor due to physical obstructions in the pipeline.

[0152] Through the above multi-dimensional superposition processing, the central processing unit reconstructs the original flow velocity curve, which is affected by environmental interference, into a corrected flow velocity sequence that can reflect the true state of the patient's airway outlet.

[0153] Furthermore, in the result selection stage (corresponding to...) Figure 2 (In the "Repeatable Measurement" and "End Measurement" areas): such as Figure 2 As indicated by the ellipsis, this embodiment supports multiple repeated measurements during a single tool startup. The central processing unit (CPU) extracts extreme value features from each generated calibrated flow rate sequence, identifying local maxima within the sequence. To ensure data validity, the CPU automatically calculates the time interval between adjacent maxima and applies a chain cough determination logic, retaining only the valid feature point with the largest amplitude. After completing a preset number of measurements, the CPU selects the largest value from the multiple valid results and determines it as the final cough peak flow rate value.

[0154] Finally, at the end of the measurement phase (corresponding to) Figure 2 (In the "End Measurement, Resume Ventilation" section): After acquiring the final value or receiving a stop command, the central processing unit shuts down the measurement tool logic, releases special control over the valve, and reactivates the original ventilation control algorithm. As shown at the end of the waveform diagram, the gas pressure and flow rate return to the normal periodic ventilation state.

[0155] In summary, this embodiment achieves quantitative measurement of the burst force of a patient's cough in a complex dynamic ventilation environment by capturing pressure and flow rate waveforms in real time and correcting them with precise algorithms. This solves the technical problem of inaccurate results caused by too many interfering factors in traditional clinical measurements.

[0156] In summary, the cough peak flow rate measurement method in the above embodiments of the present invention achieves millisecond-level capture and automatic identification of burst cough events by real-time monitoring of the first time derivative of the expiratory flow rate signal and comparing it with a preset burst force threshold. This fundamentally solves the problems of cumbersome operation, poor real-time performance, and large subjective errors caused by medical staff relying on visual observation of respiratory waveforms in existing technologies, significantly improving the automation level and evaluation efficiency of clinical measurements. Furthermore, by calculating the difference between the expiratory and inspiratory flow rate signals at each sampling moment within the cough analysis window, it achieves basic control of the ventilator's active supply. Precise removal of baseline flow interference ensures that the extracted baseline flow rate sequence accurately and purely reflects the net flow generated by the patient's spontaneous cough, greatly improving the reliability of the raw measurement data. Furthermore, by introducing a dynamic leakage compensation mechanism based on the square root of the airway pressure signal, combined with the system leakage factor pre-solved during routine ventilation, adaptive correction of the instantaneous leakage flow rate under the high pressure generated by a cough burst is achieved. This effectively compensates for flow loss caused by insufficient airtightness of the artificial airway cuff or mask, solving the problem of significantly lower measurement results in non-absolutely sealed environments using existing technologies. By establishing a resistance compensation model based on the proportional relationship between external tubing resistance parameters and patient airway resistance parameters, and performing product operations on the sampled flow rates, a software simulation was achieved to reconstruct the attenuation of flow rates due to physical obstructions during airway conduction. This successfully reconstructed a corrected flow rate sequence reflecting the true state of the patient's airway outlet (lung burst power), providing core data support for the accurate clinical assessment of patients' respiratory muscle function. Furthermore, by performing Gaussian smoothing, local maxima extraction, and time interval filtering based on a consecutive cough threshold on the corrected flow rate sequence, intelligent removal of invalid sampling noise and interference from continuous coughing actions was achieved. This ensures the extraction of unique and most representative effective results from complex physiological signals, greatly improving the algorithm's anti-interference ability and the robustness of measurement values. By analyzing the instantaneous amplitude of flow rate in real time and using a state timer to determine the end-expiratory stable state, and controlling the airway unit accordingly to establish a unified flow rate monitoring benchmark, the measurement process is triggered at a standardized respiratory mechanics starting point. This effectively avoids repeated measurement deviations caused by incomplete exhalation or airflow fluctuations, ensuring the all-weather stability of the guidance effect and the consistency of data, and solving the problems of inaccurate and inefficient measurement results of existing cough peak flow rates.

[0157] Example 2

[0158] Please see Figure 3 This is a schematic diagram of a cough peak flow rate measurement system provided in the second embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown. The cough peak flow rate measurement system in the embodiment of the present invention includes:

[0159] The signal acquisition and recognition module 11 is used to acquire the expiratory flow rate signal, the inspiratory flow rate signal and the airway pressure signal in the respiratory airway in real time, and monitor the change characteristics of the expiratory flow rate signal to identify whether an explosive cough event that meets the preset characteristics has occurred in the respiratory airway.

[0160] The basic flow rate processing module 12 is used to lock the sampling time period corresponding to the explosive cough event as the cough analysis window if the explosive cough event is detected, and calculate the difference between the expiratory flow rate signal and the inspiratory flow rate signal at each sampling time in the cough analysis window to obtain the basic cough flow rate sequence after removing the basic flow deviation interference.

[0161] The leakage compensation module 13 is used to calculate the airway pressure signal in the cough analysis window using a preset system leakage factor to obtain the leakage flow rate compensation sequence at each moment.

[0162] The resistance compensation module 14 is used to determine the resistance compensation ratio coefficient based on the ratio between the acquired tubing resistance parameters of the current respiratory path and the patient's airway resistance parameters, and to use the resistance compensation ratio coefficient to perform a product operation on the expiratory flow velocity signal at each sampling moment in the cough analysis window to obtain the tubing resistance compensation sequence at each moment.

[0163] The corrected flow rate determination module 15 is used to superimpose the basic cough flow rate sequence, the leakage flow rate compensation sequence, and the pipeline resistance compensation sequence according to the sampling time sequence to obtain a corrected flow rate sequence that reflects the true state of the patient's airway outlet.

[0164] The cough peak flow rate determination module 16 is used to extract extreme value features and evaluate the effectiveness of the corrected flow rate sequence generated by a preset number of times, and determine the final cough peak flow rate value.

[0165] Furthermore, in one embodiment of the present invention, the signal acquisition and identification module 11 includes:

[0166] The filtering unit is used to perform moving average filtering on the real-time acquired expiratory flow rate signal to remove high-frequency noise interference.

[0167] The flow rate change determination unit is used to calculate the first time derivative of the filtered expiratory flow rate signal and generate a flow rate change sequence.

[0168] The comparison unit is used to compare the values ​​in the flow rate change sequence with a preset explosive force threshold.

[0169] The event determination unit is used to determine that an explosive cough event with preset characteristics has occurred in the current respiratory airway when the number of sampling points where the rate of change of flow rate is greater than the explosive force threshold exceeds a preset number of frames, and the instantaneous amplitude corresponding to the expiratory flow rate signal is greater than the cough initiation threshold, and to mark the current moment as the event start point of the explosive cough event.

[0170] Furthermore, in one embodiment of the present invention, the basic flow rate processing module 12 includes:

[0171] The first marking unit is used to search backwards from the event start point of the explosive cough event, for the first local maximum point of the expiratory flow velocity signal, and mark it as the flow velocity peak point.

[0172] The second marking unit is used to continue searching backward from the peak flow point until the instantaneous amplitude of the expiratory flow signal falls below the cough initiation threshold or an inspiratory flow signal rise is detected, and then marks it as the end point of the event.

[0173] The cough analysis window construction unit is used to extract the data segment from the first preset time before the start point to the second preset time after the end point of the event, and construct the cough analysis window.

[0174] Furthermore, in one embodiment of the present invention, the leakage compensation module 13 includes:

[0175] The system leakage factor acquisition unit is used to acquire the system leakage factor that is pre-stored or generated by the airway self-test. The system leakage factor describes the sealing characteristics of the current breathing airway.

[0176] The pressure square root sequence generation unit is used to perform square root operation on the airway pressure signal at each sampling time within the cough analysis window to obtain the pressure square root sequence.

[0177] The leakage velocity compensation sequence generation unit is used to multiply the pressure square root sequence with the system leakage factor to generate a leakage velocity compensation sequence that dynamically changes with pressure, wherein each element in the leakage velocity compensation sequence represents the instantaneous leakage velocity at the corresponding sampling time.

[0178] Furthermore, in one embodiment of the present invention, the leakage compensation module 13 further includes:

[0179] The respiratory cycle selection unit is used to select a complete respiratory cycle during the routine ventilation phase before performing cough peak flow measurement;

[0180] The time integration unit is used to integrate the inspiratory flow rate data and expiratory flow rate data during the respiratory cycle over time, and to calculate the total inhaled tidal volume and the total exhaled tidal volume.

[0181] The total leakage calculation unit is used to calculate the difference between the total inhaled tidal volume and the total exhaled tidal volume, which is taken as the total leakage of the cycle.

[0182] The system leakage factor determination unit is used to calculate the weighted average value of the airway pressure signal during the respiratory cycle, and to calculate the system leakage factor based on the ratio of the total leakage of the cycle to the square root of the weighted average value.

[0183] Furthermore, in one embodiment of the present invention, the cough peak flow rate determination module 16 includes:

[0184] An extreme value extraction unit is used to smooth the generated corrected flow velocity sequence and extract all local maxima points;

[0185] The time interval calculation unit is used to remove abnormal noise points with amplitudes less than a preset effective flow velocity threshold from all local maxima points, and to calculate the time interval between adjacent local maxima points.

[0186] The valid result determination unit is used to retain only the local maximum point with the largest amplitude among adjacent local maximum points as the valid result of a single measurement if the time interval is less than the preset consecutive cough threshold.

[0187] The cough peak flow rate value determination unit is used to select the largest value among the valid results of a preset number of times as the final cough peak flow rate value.

[0188] Furthermore, in one embodiment of the present invention, the system further includes:

[0189] The status determination unit is used to monitor and analyze the expiratory flow rate signal in real time to determine whether the patient is in a stable end-expiratory state.

[0190] The reference control unit is used to establish a flow rate monitoring reference if the patient is determined to be in a stable end-expiratory state, and to control the airway unit to maintain a preset airway pressure reference and baseline flow rate.

[0191] Furthermore, in one embodiment of the present invention, the state determination unit includes:

[0192] The expiratory flow rate signal monitoring subunit is used to monitor the instantaneous amplitude of the expiratory flow rate signal in real time and perform moving average filtering.

[0193] The amplitude judgment subunit is used to determine whether the instantaneous amplitude of the filtered expiratory flow rate signal is less than the preset resting respiratory threshold.

[0194] If the amplitude judgment subunit determines that the value is less than the respiratory resting threshold, the state timer is started.

[0195] The end-expiratory steady state determination subunit is used to continuously monitor the duration of the state timer. If the duration exceeds the preset end-expiratory determination time threshold and the instantaneous amplitude during the timing period is always less than the respiratory resting threshold, then an end-expiratory steady state determination signal is generated.

[0196] The cough peak flow rate measurement system provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0197] Example 3

[0198] In another aspect, the present invention also proposes a medical device, please refer to [link / reference needed]. Figure 4 The image shows a medical device according to a third embodiment of the present invention, including a memory 20, a processor 10, and a program 30 stored in the memory 20 and executable on the processor 10. When the processor 10 executes the program 30, it implements the cough peak flow rate measurement method as described above.

[0199] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0200] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 may be an internal storage unit of the medical device, such as the hard disk of the medical device. In other embodiments, the memory 20 may be an external storage device of the medical device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the medical device. Furthermore, the memory 20 may include both internal and external storage units of the medical device. The memory 20 can be used not only to store application software and various types of data installed on the medical device, but also to temporarily store data that has been output or will be output.

[0201] It should be pointed out that, Figure 4The structure shown does not constitute a limitation on the medical device. In other embodiments, the medical device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0202] This invention also proposes a computer-readable medium having a program stored thereon, which, when executed by a processor, implements the cough peak flow rate measurement method as described in the foregoing method embodiments.

[0203] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0204] More specific examples of media (a non-exhaustive list) include: electrical connections (electronic devices) with one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0205] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0206] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0207] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for measuring cough peak flow rate, characterized in that, The method includes: Real-time acquisition of expiratory flow rate signals, inspiratory flow rate signals, and airway pressure signals in the respiratory pathway, and monitoring of the changing characteristics of the expiratory flow rate signals to identify whether an explosive cough event with preset characteristics has occurred in the respiratory pathway. If the burst cough event is identified, the sampling time period corresponding to the burst cough event is locked as the cough analysis window, and the difference between the expiratory flow rate signal and the inspiratory flow rate signal at each sampling time in the cough analysis window is calculated to obtain the basic cough flow rate sequence after removing the basic flow deviation interference. The airway pressure signal within the cough analysis window is calculated using a preset system leakage factor to obtain a leakage flow rate compensation sequence for each moment. Based on the ratio between the obtained current respiratory airway resistance parameters and the patient's airway resistance parameters, a resistance compensation ratio coefficient is determined, and the resistance compensation ratio coefficient is used to multiply the expiratory flow rate signal at each sampling moment in the cough analysis window to obtain the airway resistance compensation sequence at each moment. The basic cough flow rate sequence, the leakage flow rate compensation sequence, and the tubing resistance compensation sequence are superimposed according to the sampling time sequence to obtain the corrected flow rate sequence that reflects the true state of the patient's airway outlet. Extreme value feature extraction and effectiveness evaluation are performed on the corrected flow rate sequence generated for a preset number of times to determine the final cough peak flow rate value; The step of monitoring the changes in the expiratory flow rate signal to identify whether a burst cough event meeting preset characteristics has occurred in the respiratory airway includes: The real-time acquired expiratory flow rate signal is subjected to moving average filtering to remove high-frequency noise interference; Calculate the first time derivative of the filtered expiratory flow rate signal to generate a flow rate change sequence; The values ​​in the velocity change rate sequence are compared with a preset explosive force threshold. When the number of sampling points where the rate of change of flow rate is greater than the burst force threshold exceeds the preset number of frames, and the instantaneous amplitude of the expiratory flow rate signal is greater than the cough initiation threshold, it is determined that a burst cough event with preset characteristics has occurred in the current respiratory airway, and the current moment is marked as the event start point of the burst cough event. The step of calculating the airway pressure signal within the cough analysis window using a preset system leakage factor to obtain the leakage flow rate compensation sequence at each moment includes: Obtain a pre-stored system leakage factor or a system leakage factor generated by a self-test of the airway, which describes the sealing characteristics of the current breathing airway; The square root of the airway pressure signal at each sampling time within the cough analysis window is calculated to obtain the pressure square root sequence. The pressure square root sequence is multiplied with the system leakage factor to generate a leakage velocity compensation sequence that dynamically changes with pressure, wherein each element in the leakage velocity compensation sequence represents the instantaneous leakage velocity at the corresponding sampling time. Before the step of calculating the airway pressure signal within the cough analysis window using a preset system leakage factor to obtain the leakage flow rate compensation sequence at each moment, the method further includes: During the routine ventilation phase prior to measuring cough peak flow, select one complete respiratory cycle; The inspiratory flow rate data and expiratory flow rate data during the respiratory cycle are integrated over time to calculate the total inhaled tidal volume and the total exhaled tidal volume. Calculate the difference between the total inhaled tidal volume and the total exhaled tidal volume as the total leakage over the cycle; Calculate the weighted average of the airway pressure signals during the respiratory cycle, and solve for the system leakage factor based on the ratio of the total leakage of the cycle to the square root of the weighted average. The steps of extracting extreme value features and evaluating the effectiveness of the corrected flow rate sequence generated a preset number of times to determine the final cough peak flow rate value include: The generated corrected flow velocity sequence is smoothed and all local maxima are extracted; Remove abnormal noise points with amplitudes less than a preset effective flow velocity threshold from all local maxima, and calculate the time interval between adjacent local maxima. If the time interval is less than the preset consecutive cough threshold, then only the local maximum point with the largest amplitude among the adjacent local maximum points is retained as the valid result of a single measurement. The largest value among the valid results of the preset number of tests is selected as the final cough peak flow rate value.

2. The method for measuring cough peak flow rate according to claim 1, characterized in that, The step of locking the sampling time period corresponding to the burst cough event as the cough analysis window includes: Based on the event initiation point of the explosive cough event, search backward for the first local maximum point of the expiratory flow velocity signal and mark it as the flow velocity peak point. Continue searching backward from the peak flow point until the instantaneous amplitude of the expiratory flow signal falls below the cough initiation threshold or an inspiratory flow signal rise is detected, and mark this as the end point of the event. The cough analysis window is constructed by extracting a data segment from a first preset time period before the starting point to a second preset time period after the end point of the event.

3. The method for measuring cough peak flow rate according to claim 1, characterized in that, The step of monitoring the changes in the expiratory flow rate signal to identify burst cough events in the respiratory tract that meet preset characteristics also includes the following prior steps: Real-time monitoring and analysis of expiratory flow rate signals to determine whether the patient is in a stable end-expiratory state; If the patient is determined to be in a stable end-expiratory state, a flow rate monitoring baseline is established, and the airway unit is controlled to maintain a preset airway pressure baseline and baseline flow rate.

4. The method for measuring cough peak flow rate according to claim 3, characterized in that, The step of real-time monitoring and analysis of the expiratory flow rate signal to determine whether the patient is in a stable end-expiratory state includes: The instantaneous amplitude of the expiratory flow rate signal is monitored in real time and then subjected to moving average filtering. Determine whether the instantaneous amplitude of the filtered expiratory flow rate signal is less than the preset resting respiratory threshold; If the respiratory rate is below the resting respiratory threshold, a state timer is started. The duration of the state timer is continuously monitored. If the duration exceeds the preset end-expiratory determination time threshold and the instantaneous amplitude during the timing period is always less than the respiratory resting threshold, an end-expiratory stable state determination signal is generated.

5. A cough peak flow rate measurement system, characterized in that, The system includes: The signal acquisition and recognition module is used to acquire the expiratory flow rate signal, inspiratory flow rate signal and airway pressure signal in the respiratory airway in real time, and monitor the change characteristics of the expiratory flow rate signal to identify whether an explosive cough event that meets the preset characteristics has occurred in the respiratory airway. The basic flow rate processing module is used to lock the sampling time period corresponding to the burst cough event as the cough analysis window if the burst cough event is detected, and calculate the difference between the expiratory flow rate signal and the inspiratory flow rate signal at each sampling time in the cough analysis window to obtain the basic cough flow rate sequence after removing the basic flow deviation interference. The leakage compensation module is used to calculate the airway pressure signal within the cough analysis window using a preset system leakage factor to obtain the leakage flow rate compensation sequence at each moment. The resistance compensation module is used to determine the resistance compensation ratio coefficient based on the ratio between the obtained tubing resistance parameters of the current respiratory path and the patient's airway resistance parameters, and to use the resistance compensation ratio coefficient to perform a product operation on the expiratory flow velocity signal at each sampling moment in the cough analysis window to obtain the tubing resistance compensation sequence at each moment. The corrected flow rate determination module is used to superimpose the basic cough flow rate sequence, the leakage flow rate compensation sequence, and the tubing resistance compensation sequence according to the sampling time sequence to obtain a corrected flow rate sequence that reflects the true state of the patient's airway outlet. The cough peak flow rate determination module is used to extract extreme value features and evaluate the effectiveness of the corrected flow rate sequence generated by a preset number of times, and determine the final cough peak flow rate value. The signal acquisition and recognition module includes: The filtering unit is used to perform moving average filtering on the real-time acquired expiratory flow rate signal to remove high-frequency noise interference. The flow rate change determination unit is used to calculate the first time derivative of the filtered expiratory flow rate signal and generate a flow rate change sequence. The comparison unit is used to compare the values ​​in the flow rate change sequence with a preset explosive force threshold. The event determination unit is used to determine that an explosive cough event with preset characteristics has occurred in the current respiratory airway when the number of sampling points where the rate of change of flow rate is greater than the explosive force threshold exceeds a preset number of frames, and the instantaneous amplitude of the expiratory flow rate signal is greater than the cough initiation threshold, and marks the current moment as the event start point of the explosive cough event. The leakage compensation module includes: The system leakage factor acquisition unit is used to acquire the system leakage factor that is pre-stored or generated by the airway self-test. The system leakage factor describes the sealing characteristics of the current breathing airway. The pressure square root sequence generation unit is used to perform square root operation on the airway pressure signal at each sampling time within the cough analysis window to obtain the pressure square root sequence. The leakage velocity compensation sequence generation unit is used to multiply the pressure square root sequence with the system leakage factor to generate a leakage velocity compensation sequence that dynamically changes with pressure, wherein each element in the leakage velocity compensation sequence represents the instantaneous leakage velocity at the corresponding sampling time. The leakage compensation module also includes: The respiratory cycle selection unit is used to select a complete respiratory cycle during the routine ventilation phase before performing cough peak flow measurement; The time integration unit is used to integrate the inspiratory flow rate data and expiratory flow rate data during the respiratory cycle over time, and to calculate the total inhaled tidal volume and the total exhaled tidal volume. The total leakage calculation unit is used to calculate the difference between the total inhaled tidal volume and the total exhaled tidal volume, which is taken as the total leakage of the cycle. The system leakage factor determination unit is used to calculate the weighted average value of the airway pressure signal during the respiratory cycle, and to calculate the system leakage factor based on the ratio of the total leakage of the cycle to the square root of the weighted average value. The cough peak flow rate determination module includes: An extreme value extraction unit is used to smooth the generated corrected flow velocity sequence and extract all local maxima points; The time interval calculation unit is used to remove abnormal noise points with amplitudes less than a preset effective flow velocity threshold from all local maxima points, and to calculate the time interval between adjacent local maxima points. The valid result determination unit is used to retain only the local maximum point with the largest amplitude among adjacent local maximum points as the valid result of a single measurement if the time interval is less than the preset consecutive cough threshold. The cough peak flow rate value determination unit is used to select the largest value among the valid results of a preset number of times as the final cough peak flow rate value.

6. A medical device, characterized in that, It includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the cough peak flow rate measurement method as described in any one of claims 1-4.

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