Method and apparatus for analyzing lung function indicators

Abstract

This invention provides a method and device for analyzing lung function indicators. The method includes: acquiring chest and abdominal displacement signals of a target subject during respiration; determining peaks and troughs in the respiratory waveform based on the chest and abdominal displacement signals; constructing a continuous baseline curve based on the peaks and troughs; identifying the deep inspiration and rapid expiration segments based on the continuous baseline curve; and calculating the target subject's lung function indicators based on the feature values ​​in the rapid expiration segment. By implementing this invention, corresponding parameters and feature values ​​are extracted based on the detected respiratory waveform, and lung function indicators are analyzed and calculated based on these parameters and feature values. Compared with traditional lung function testing methods, this method does not require the use of traditional contact equipment or the collection of dynamic volume parameters that are difficult to obtain, thus enabling safer, more convenient, and more accurate analysis and calculation of lung function indicators.

Description

Technical Field

[0001] This invention relates to the field of medical data processing technology, specifically to a method and device for analyzing lung function indicators. Background Technology

[0002] Traditional methods for assessing lung function primarily rely on devices such as spirometers and flow meters, which require regular calibration to ensure data accuracy. Traditional spirometers and turbine-type devices are bulky, poorly portable, and difficult to widely adopt. Furthermore, traditional lung function testing, which depends on contact-based devices like spirometers, requires the simultaneous acquisition of FEV1 (forced expiratory volume in one second) and FVC (forced vital capacity) volume data. This process necessitates sophisticated airflow sensors, resulting in high costs and complex operation. The measurement of volume units (ml) is easily affected by fluctuations in respiratory flow rate. Calculating the FEV1 / FVC ratio requires simultaneous acquisition of two dynamic volume parameters, demanding high levels of subject cooperation, making data acquisition difficult, and leading to lower accuracy in the calculated results. Summary of the Invention

[0003] In view of this, the present invention provides a method and device for analyzing lung function indicators.

[0004] In a first aspect, embodiments of the present invention propose a method for analyzing lung function indicators, comprising: acquiring chest and abdominal displacement signals of a target subject during respiration; determining peaks and troughs in a respiratory waveform based on the chest and abdominal displacement signals; constructing a continuous baseline curve based on the peaks and troughs; identifying deep inhalation and rapid exhalation segments based on the continuous baseline curve; and calculating lung function indicators of the target subject based on feature values ​​in the rapid exhalation segment.

[0005] Secondly, embodiments of the present invention propose a lung function index analysis device, comprising: a signal acquisition module configured to acquire chest and abdominal displacement signals of a target subject during respiration; a waveform extraction module configured to determine peaks and troughs in a respiratory waveform based on the chest and abdominal displacement signals; a baseline curve construction module configured to construct a continuous baseline curve based on the peaks and troughs; a curve recognition module configured to recognize a deep inhalation segment and a rapid exhalation segment based on the continuous baseline curve; and an index calculation module configured to calculate the lung function index of the target subject based on the feature values ​​in the rapid exhalation segment.

[0006] Thirdly, embodiments of the present invention provide a lung function index analysis device, the lung function index analysis device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the lung function index analysis method as described in any implementation of the first aspect.

[0007] The lung function index analysis method and device provided in this invention collects chest and abdominal displacement signals, extracts corresponding parameters and feature values ​​based on the detected respiratory waveform, and performs lung function index analysis and calculation based on these parameters and feature values. Compared with traditional lung function testing methods, it does not require the use of traditional contact equipment or the collection of dynamic volume parameters that are difficult to obtain, and can achieve lung function index analysis and calculation more safely, conveniently and accurately.

[0008] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0009] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0010] Figure 1 This is an exemplary system architecture in which the present invention can be applied;

[0011] Figure 2 A flowchart of a lung function index analysis method provided in an embodiment of the present invention;

[0012] Figure 3 A flowchart of another lung function index analysis method provided in an embodiment of the present invention;

[0013] Figure 4 A flowchart of another lung function index analysis method provided in an embodiment of the present invention;

[0014] Figure 5 A structural block diagram of a lung function index analysis device provided in an embodiment of the present invention;

[0015] Figure 6 This is a schematic diagram of a lung function index analysis device suitable for performing lung function index analysis methods, provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0018] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can also refer to the internal connection of two components; and they can refer to a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0019] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0020] Figure 1 An exemplary system architecture 100 is shown, in which embodiments of the lung function index analysis method, apparatus, lung function index analysis device, and computer-readable storage medium of the present invention can be applied.

[0021] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0022] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various applications for enabling information communication between the terminal devices 101, 102, and 103 and server 105 can be installed. These applications include instant messaging applications.

[0023] Terminal devices 101, 102, and 103 and server 105 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices, and can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here. When server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here.

[0024] Server 105 can provide various services through its built-in applications. It should be noted that the data or information required to provide these services can be obtained from terminal devices 101, 102, and 103 via network 104, or it can be pre-stored locally on server 105 through various means. Therefore, when server 105 detects that this data is already stored locally, it can choose to retrieve it directly from the local storage. In this case, the exemplary system architecture 100 may not include terminal devices 101, 102, and 103 and network 104.

[0025] Since processing the corresponding data or information may require significant computing resources and strong computing power, the lung function index analysis methods provided in the subsequent embodiments of this invention are generally executed by a server 105 with strong computing power and abundant computing resources. Correspondingly, the lung function index analysis device is also generally located in the server 105. However, it should also be noted that when terminal devices 101, 102, and 103 also possess sufficient computing power and resources, they can also complete the aforementioned calculations performed by the server 105 through their installed applications, thereby outputting the same results as the server 105. Especially when multiple terminal devices with different computing capabilities exist simultaneously, but the relevant application determines that the terminal device has strong computing power and abundant remaining computing resources, the terminal device can perform the aforementioned calculations, thereby appropriately reducing the computing pressure on the server 105. Correspondingly, the lung function index analysis device can also be located in the terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may also exclude the server 105 and the network 104.

[0026] It should be understood that Figure 1The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0027] Please refer to Figure 2 , Figure 2 A flowchart of a lung function index analysis method provided in an embodiment of the present invention, wherein process 200 includes the following steps:

[0028] Step 201: Obtain the chest and abdominal displacement signal of the target object during the breathing process.

[0029] This step is intended for the entity performing the lung function index analysis method (e.g., Figure 1 The server 105 shown acquires the chest and abdominal displacement signal of the target object during breathing. In this embodiment, the target object is a subject undergoing lung function testing. For this target object, a frequency-modulated continuous wave (FMCW) signal can be transmitted to the target object via radar. The echo signal received by the radar is mixed with the transmitted signal to obtain a difference frequency signal, which is then high-pass filtered, low-noise amplified, and sampled by an ADC to obtain a digitized echo signal. The distance gate (representing the time range for acquiring the echo signal) of the chest and abdomen is determined based on the location of the strongest echo energy. Then, the original phase signal is extracted along the distance gate, and the extracted original phase signal is unwrapped to obtain the unwrapped phase signal, which is the chest and abdominal displacement signal.

[0030] In this embodiment, during signal acquisition, the direction of the radar's main beam is basically consistent with the direction of the normal to the thoracic plane, and the main lobe is aligned with the midpoint of the line connecting the sternal angle and the xiphoid process (focusing on the sensitive area of ​​anterior chest and abdominal respiratory movement). The subject is seated with their back pressed firmly against the chair back. Because the backrest is relatively fixed, there is no displacement between the back and the radar; all displacements generated during breathing are directly reflected in the anterior chest and abdomen illuminated by the radar. During the test, the subject remains stable except for breathing movements, avoiding other body movements.

[0031] Step 202: Determine the peaks and troughs in the respiratory waveform based on the chest and abdomen displacement signal.

[0032] This step is intended for the aforementioned executing entity to determine the peak and trough positions and corresponding peak and trough values ​​in the respiratory waveform of the chest and abdominal displacement signal. For example, in this respiratory waveform, the peak can be a local maximum point (exhalation peak), and the trough can be a local minimum point that immediately follows (inspiratory trough).

[0033] Step 203: Construct a continuous baseline curve based on the peaks and troughs.

[0034] This step aims to have the aforementioned implementing entity construct a continuous baseline curve based on peaks and troughs. For example, extreme midpoint interpolation can be used, based on respiratory waveform A. signal The respiratory cycle is constructed using adjacent peaks (Pk) and troughs (Tr). The baseline BL_i = (Pk_i + Tr_i) / 2 for each respiratory cycle is calculated, where i is a positive integer. The midpoint between the peak and trough is the theoretical baseline position for that respiratory cycle. Due to the symmetry of respiratory motion, the midpoint between the peak and trough represents the steady-state reference of the current cycle. Then, cubic spline interpolation is performed based on the baseline. By considering the values ​​at the baseline nodes and the continuity conditions of the first and second derivatives, a smooth and continuous baseline curve that meets the interpolation requirements is constructed.

[0035] Step 204: Identify the deep inhalation and rapid exhalation phases based on the continuous baseline curve.

[0036] This step aims to have the aforementioned executing entity identify the deep inhalation segment based on the continuous baseline curve, combined with morphological feature-based detection, and to identify the rapid exhalation segment by combining dynamic slope analysis.

[0037] Step 205: Calculate the lung function indicators of the target subject based on the feature values ​​in the rapid exhalation phase.

[0038] This step aims to calculate the target subject's lung function indicators based on the corresponding characteristic values ​​in the determined rapid exhalation phase using the aforementioned Star Direct Push method.

[0039] The lung function index analysis method provided in this embodiment of the invention collects chest and abdominal displacement signals, extracts corresponding parameters and feature values ​​based on the detected respiratory waveform, and performs lung function index analysis and calculation based on these parameters and feature values. Compared with traditional lung function testing methods, it does not require the use of traditional contact equipment or the collection of dynamic volume parameters that are difficult to obtain, and can achieve lung function index analysis and calculation more safely, conveniently and accurately.

[0040] Please refer to Figure 3 , Figure 3 A flowchart of a lung function index analysis method provided in this disclosure embodiment, namely for... Figure 2 Step 202 in process 200 provided a specific implementation. Other steps in process 200 are not adjusted; a new complete embodiment is obtained by replacing step 202 with the specific implementation provided in this embodiment. Process 300 includes the following steps:

[0041] Step 301: The chest and abdomen displacement signal is filtered using the dynamic threshold sliding window method, and the maximum and minimum values ​​within the dynamic threshold sliding window are calculated.

[0042] This step aims to have the aforementioned execution entity perform Butterworth bandpass filtering (0.1-3Hz) on the chest and abdomen displacement signal using the dynamic threshold sliding window method, with a sliding window length of 0.5s and a step size of 0.1s, and calculate the maximum / minimum value within the window.

[0043] Step 302: Filter valid extreme points from the maximum and minimum values ​​based on preset conditions.

[0044] This step aims to allow the aforementioned executing entity to filter valid extreme points based on preset conditions. These preset conditions primarily include: for peak values, the current dynamic threshold sliding window value must be greater than the average of the values ​​of the preceding and following preset duration sliding windows by a first preset multiple; for example, the current value could be > 1.2 times the average of the preceding / following 0.3s windows. For trough values, the current dynamic threshold sliding window value must be greater than the average of the preceding and following preset duration sliding windows by a second preset multiple; for example, the current value could be < 0.8 times the average of the preceding / following 0.3s windows. The interval between extreme points must be greater than or equal to a second preset duration; for example, this second preset duration could be 1 second. By filtering this extreme point interval, respiratory tremor interference can be eliminated.

[0045] Step 303: Determine the peaks and troughs based on the effective extreme points.

[0046] This step aims to allow the aforementioned executing entity to determine the location and value of the peaks and troughs after filtering out the valid extreme points.

[0047] Please refer to Figure 4 , Figure 4 A flowchart of a lung function index analysis method provided in this disclosure embodiment, namely for... Figure 2 Step 204 in process 200 provided a specific implementation. Other steps in process 200 are not adjusted; a new complete embodiment is obtained by replacing step 204 with the specific implementation provided in this embodiment. Process 400 includes two procedures: identifying the deep inhalation segment based on a continuous baseline curve, and identifying the rapid exhalation segment based on a continuous baseline curve. The process of identifying the deep inhalation segment based on a continuous baseline curve mainly includes:

[0048] Step 401: Locate the maximum value point in the continuous baseline curve to determine the maximum peak point.

[0049] This step aims to perform a two-stage detection (coarse selection stage and fine selection stage) based on morphological features by the aforementioned execution entity. In the coarse selection stage, the maximum peak P_max is located by finding the maximum value point.

[0050] Step 402: Based on the maximum peak point, search forward for the starting point of the first rising edge with a slope greater than a preset value.

[0051] This step aims to have the aforementioned execution entity perform a forward search based on the maximum peak value P_max to find the first rising edge start point T_start with a slope greater than a preset value. For example, this preset value can be 0.5 mm / s.

[0052] Step 403: Based on preset selection conditions, select the deep inhalation segment within the interval between the starting point of the rising edge and the maximum peak point.

[0053] This step aims to allow the aforementioned executing entity to select deep inhalation segments within the interval between the starting point of the rising edge and the maximum peak point based on preset selection criteria during the selection phase. For example, these preset selection criteria include at least one of the following: duration ≥ 1 second, rising amplitude ≥ 3 times the baseline fluctuation amplitude, and no violent oscillations (variance < 0.1) in the velocity curve (differentialized from the respiratory waveform). In this embodiment, duration refers to the deep inhalation process lasting more than 1 second; periods shorter than 1 second are considered non-deep inhalation segments due to body movement, disturbances, etc.; rising amplitude is greater than 3 times the baseline amplitude; and the velocity curve shows no violent oscillations, indicating that the inhalation speed is relatively uniform during deep inhalation without significant fluctuations. These three conditions are sufficient for determining a deep inhalation segment. It should be noted that the selection criteria listed above are merely examples and are not intended to limit the invention.

[0054] In some optional embodiments of this example, the process of identifying the rapid expiratory phase based on a continuous baseline curve mainly includes:

[0055] Step 404: Within a preset time window after the maximum peak point, calculate the first derivative of the chest and abdominal displacement signal and find the minimum point of the first derivative.

[0056] During the rapid expiratory phase identification process, based on the dynamic slope, the first derivative of the chest and abdominal displacement signal is calculated within a preset time window after the maximum peak point P_max, and the minimum point P of the first derivative is found. v_max (This minimum point is also the maximum expiratory rate point). For example, the preset time period can be 2 seconds.

[0057] Step 405: Based on the preset starting point conditions, extend forward with the minimum point as the center to determine the starting point position.

[0058] Step 406: Based on the preset endpoint conditions, expand backward with the minimum point as the center to determine the endpoint position.

[0059] The above process is based on the minimum point P. v_maxCentered on the first point, the expansion extends forward and backward to the starting and ending points: forward expansion occurs when the first derivative is first less than the first threshold th1, and backward expansion occurs when the first derivative is first greater than the second threshold th2. th1 and th2 are obtained using a dual-coefficient adaptive method, acquiring the baseline fluctuation amplitude A_bl = max(BL(t)) - min(BL(t)) and variance σ, where th1 = k1*A_bl*σ and th2 = k2*A_bl*σ, and k1 and k2 are empirical values, and the variance... N is the number of baseline data points. The baseline signal mean. .

[0060] Step 407: Determine the rapid exhalation segment based on the starting and ending positions.

[0061] Once the starting and ending points are determined, the rapid exhalation segment can be determined based on these two points.

[0062] In some optional embodiments of this example, the executing entity may also perform effectiveness verification based on the deep inhalation and rapid exhalation phases. The effectiveness verification process mainly includes:

[0063] Step 1: Perform a joint test on the deep inhalation and rapid exhalation phases.

[0064] For example, the joint verification mainly verifies the following aspects: temporal continuity: the exhalation start should be within 2 seconds after the inhalation end; amplitude matching: the amplitude of the deep inhalation and rapid exhalation phase is ≥ 120% of the baseline amplitude; morphological integrity: the deep inhalation phase and the rapid exhalation phase should contain at least one complete concave waveform.

[0065] Step 2: In response to the verification result being qualified, generate a quality assessment result based on the first derivative.

[0066] If the above joint verification process confirms that the deep inhalation and rapid exhalation phases meet the above conditions, the verification result is qualified. Then, the quality assessment index can be calculated based on the first derivative, and a quality assessment result can be generated based on this index. The quality assessment index includes: Signal-to-noise ratio (SNR). , where A signal A is the average power of the useful signal; noise The average power of the noise. Expiratory burst slope: , That is, the first derivative.

[0067] Based on this quality assessment index, quality can be graded, with grading criteria including: Excellent: SNR ≥ 25dB. ≤-5mm / s; Generally: SNR∈[15,25)dB, ∈(-3,-5)mm / s; Difference: SNR<15dB or >-3mm / s.

[0068] Step 3: Based on the quality assessment results, determine the rapid exhalation segment that meets the quality requirements, and execute step 205 to calculate the lung function index of the target subject based on the characteristic values ​​in the rapid exhalation segment.

[0069] To avoid the randomness of a single measurement and improve measurement accuracy, multiple measurements can be taken, and the results of three measurements with excellent quality grades can be used for subsequent calculation of lung function indicators.

[0070] In some optional embodiments of this example, step 205, the process of calculating the lung function indicators of the target subject based on the feature values ​​in the rapid expiratory phase, mainly includes:

[0071] Step 1: Determine the starting displacement value, the displacement value corresponding to the third preset time after the starting point, and the ending displacement value of the displacement signal based on the rapid exhalation segment. Specifically, record the starting displacement value y of the rapid exhalation segment displacement signal. s Displacement value y1 1 second after the starting point, and displacement value y2 at the end point. e .

[0072] Step 2: Calculate the one-second rate characteristic value based on the displacement value at the starting point of the moving signal, the displacement value corresponding to the third preset time after the starting point, and the displacement value at the ending point to obtain the lung function index.

[0073] Specifically, based on the principle of scale-free displacement ratio, and using the following formula for the lung function index F1 = (S1 / S_total) × 100%, we obtain F1 = (y1-y s ) / (y s -y e )×100%. In this embodiment, based on the correspondence discovered by the inventors: the amount of chest wall displacement is linearly positively correlated with the amount of lung volume change (ΔS1 / ΔS_total = V1 / V_total), the lung function index F1 is obtained as F1 = (S1 / S_total)×100%, where S1 is the amount of chest wall displacement curve displacement in the first second, and S_total is the amount of displacement curve displacement throughout the entire process.

[0074] The lung function index analysis method of this invention has the following advantages:

[0075] 1) It can eliminate the dependence on milliliters and avoid volume measurement errors;

[0076] 2) The calculation is simple. Compared with other methods that use point cloud 3D modeling to calculate the entire lung volume, the implementation method of this invention can significantly reduce the amount of calculation.

[0077] 3) Zero calibration requirement, no need for radar-volume conversion model training.

[0078] 4) Enhanced anti-interference: Eliminates the sensitivity of volume calculation to respiratory flow rate fluctuations, thereby improving the accuracy of calculation results.

[0079] Further reference Figure 5 As an implementation of the methods shown in the above figures, the present invention provides an embodiment of a lung function index analysis device, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0080] like Figure 5 As shown, the lung function index analysis device 500 of this embodiment may include: a signal acquisition module 501, a waveform extraction module 502, a baseline curve construction module 503, a curve recognition module 504, and an index calculation module 505. Specifically, the signal acquisition module 501 is configured to acquire the chest and abdominal displacement signals of the target subject during respiration; the waveform extraction module 502 is configured to determine the peaks and troughs in the respiratory waveform based on the chest and abdominal displacement signals; the baseline curve construction module 503 is configured to construct a continuous baseline curve based on the peaks and troughs; the curve recognition module 504 is configured to identify the deep inhalation segment and the rapid exhalation segment based on the continuous baseline curve; and the index calculation module 505 is configured to calculate the lung function index of the target subject based on the feature values ​​in the rapid exhalation segment.

[0081] In this embodiment, the specific processing and technical effects of the signal acquisition module 501, waveform extraction module 502, baseline curve construction module 503, curve recognition module 504, and index calculation module 505 in the lung function index analysis device 500 can be referred to respectively. Figure 2 The relevant descriptions of steps 201-205 in the corresponding embodiments will not be repeated here.

[0082] In some optional implementations of this embodiment, the waveform extraction module 502 includes: a calculation submodule, configured to filter the chest and abdominal displacement signal using a dynamic threshold sliding window method and calculate the maximum and minimum values ​​within the dynamic threshold sliding window; an extreme point filtering submodule, configured to filter valid extreme points from the maximum and minimum values ​​based on preset conditions; and a peak and trough determination submodule, configured to determine peaks and troughs based on valid extreme points.

[0083] In some optional implementations of this embodiment, the baseline curve construction module 503 includes: a respiratory cycle construction submodule, configured to construct a respiratory cycle based on adjacent peaks and troughs using the extreme midpoint interpolation method; a baseline calculation submodule, configured to calculate the baseline of each respiratory cycle; and a curve construction submodule, configured to construct a continuous baseline curve by performing cubic spline interpolation based on the baseline.

[0084] In some optional implementations of this embodiment, the curve recognition module 504 is further configured to find the maximum value point in the continuous baseline curve and determine the maximum peak point; based on the maximum peak point, search forward for the first rising edge starting point with a slope greater than a preset value; and based on preset selection conditions, filter out the deep inhalation segment within the interval between the rising edge starting point and the maximum peak point.

[0085] Furthermore, the curve recognition module 504 is also configured to calculate the first derivative of the chest and abdominal displacement signal within a preset time window after the maximum peak point, and find the minimum point of the first derivative; based on the preset starting point condition, perform forward expansion with the minimum point as the center to determine the starting point position; based on the preset ending point condition, perform backward expansion with the minimum point as the center to determine the ending point position; and determine the rapid exhalation segment based on the starting point position and the ending point position.

[0086] In some optional implementations of this embodiment, the lung function index analysis device 500 further includes: a verification module configured to perform joint verification of the deep inhalation segment and the rapid exhalation segment; in response to a qualified verification result, generating a quality assessment result based on the first derivative; determining the rapid exhalation segment that meets the quality requirements based on the quality assessment result, and driving the index calculation module 505.

[0087] In some optional implementations of this embodiment, the index calculation module 505 is further configured to determine the starting displacement value of the shift signal, the displacement value corresponding to the third preset time after the starting point, and the ending displacement value based on the rapid exhalation segment; and to calculate the one-second rate feature value based on the starting displacement value of the shift signal, the displacement value corresponding to the third preset time after the starting point, and the ending displacement value to obtain the lung function index.

[0088] In some optional implementations of this embodiment, the signal acquisition module 501 is further configured to acquire the echo signal of the target object based on radar acquisition; determine the distance gate of the chest and abdomen of the target object based on the echo signal; extract the original phase signal based on the distance gate; and unwrap the original phase signal to obtain the chest and abdomen displacement signal.

[0089] This embodiment exists as a device embodiment corresponding to the above method embodiment. The lung function index analysis device provided in this embodiment collects chest and abdominal displacement signals, extracts corresponding parameters and feature values ​​based on the detected respiratory waveform, and performs lung function index analysis and calculation based on the parameters and feature values. Compared with traditional lung function detection methods, it does not require the use of traditional contact equipment or the collection of dynamic volume parameters that are difficult to obtain, and can achieve lung function index analysis and calculation more safely, conveniently and accurately.

[0090] According to embodiments of the present invention, the present invention also provides a lung function index analysis device, the lung function index analysis device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the lung function index analysis method described in any of the above embodiments when executed.

[0091] According to embodiments of the present invention, the present invention also provides a readable storage medium storing computer instructions that enable a computer to implement the lung function index analysis method described in any of the above embodiments when executed.

[0092] According to embodiments of the present invention, the present invention also provides a computer program product, which, when executed by a processor, can implement the lung function index analysis method described in any of the above embodiments.

[0093] Figure 6 A schematic block diagram of an example pulmonary function index analysis device 600 that can be used to implement embodiments of the present invention is shown. The pulmonary function index analysis device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The pulmonary function index analysis device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0094] like Figure 6As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded into random access memory (RAM) 603 from storage unit 608. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0095] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0096] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the lung function index analysis method. For example, in some embodiments, the lung function index analysis method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the lung function index analysis method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the lung function index analysis method by any other suitable means (e.g., by means of firmware).

[0097] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0101] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for analyzing lung function indicators, characterized in that, include: Acquire the chest and abdominal displacement signals of the target object during respiration; The peaks and troughs in the respiratory waveform are determined based on the chest and abdominal displacement signals. A continuous baseline curve is constructed based on the peaks and troughs; The deep inhalation and rapid exhalation phases are identified based on the continuous baseline curve. Calculate the lung function indicators of the target subject based on the feature values ​​in the rapid exhalation phase; The step of calculating the lung function indicators of the target subject based on the feature values ​​in the rapid exhalation phase includes: Based on the rapid exhalation phase, determine the starting displacement value of the displacement signal, the displacement value corresponding to the third preset time after the starting point, and the ending displacement value; Based on the displacement signal starting point displacement value, the displacement value corresponding to the third preset time after the starting point, and the ending point displacement value, the one-second rate characteristic value is calculated to obtain the lung function index. Wherein, the lung function index is calculated according to the following formula: F1 = (y1-y s ) / (y s -y e ) x 100%; y s is the displacement value at the start point of the rapid exhalation segment, y1 is the displacement value corresponding to 1s after the start point, and y e is the end displacement value.

2. The method according to claim 1, characterized in that, The determination of peaks and troughs in the respiratory waveform based on the chest and abdominal displacement signal includes: The chest and abdominal displacement signal is filtered using a dynamic threshold sliding window method, and the maximum and minimum values ​​within the dynamic threshold sliding window are calculated. Valid extreme points are selected from the maximum and minimum values ​​based on preset conditions; The peaks and troughs are determined based on the effective extreme points.

3. The method according to claim 2, characterized in that, The preset conditions include: For peak values, the current value of the dynamic threshold sliding window must be greater than the average value of the values ​​of the preceding and following preset duration sliding windows by a first preset multiple. For the trough value, the current dynamic threshold sliding window value is greater than the second preset multiple of the average value of the previous and subsequent preset duration sliding windows; and The interval between extreme points is greater than or equal to the second preset duration.

4. The method according to claim 1, characterized in that, The construction of a continuous baseline curve based on the peaks and troughs includes: The respiratory cycle is constructed based on adjacent peaks and troughs using the extreme midpoint interpolation method. Calculate the baseline for each respiratory cycle; The continuous baseline curve is constructed by performing cubic spline interpolation based on the baseline.

5. The method according to claim 1, characterized in that, Identifying the deep inhalation phase based on the continuous baseline curve includes: Find the maximum value point in the continuous baseline curve to determine the maximum peak point; Based on the maximum peak point, search forward to find the starting point of the first rising edge with a slope greater than a preset value; The deep inhalation segment is obtained by selecting from the interval between the starting point of the rising edge and the maximum peak point based on preset selection criteria.

6. The method according to claim 5, characterized in that, Identifying the rapid exhalation segment based on the continuous baseline curve includes: Within a preset time window after the maximum peak point, calculate the first derivative of the chest and abdominal displacement signal and find the minimum point of the first derivative. Based on the preset starting point condition, the starting point position is determined by forward expansion centered on the minimum point. Based on the preset endpoint conditions, the endpoint position is determined by expanding backward from the minimum point as the center. The rapid exhalation segment is determined based on the starting and ending positions.

7. The method according to claim 6, characterized in that, Before calculating the lung function indicators of the target subject based on the feature values ​​in the rapid expiratory phase, the method further includes: The deep inhalation and rapid exhalation phases were jointly verified; In response to a passing verification result, a quality assessment result is generated based on the first derivative. Based on the quality assessment results, a rapid exhalation segment that meets the quality requirements is determined, and the step of calculating the lung function index of the target subject based on the feature values ​​in the rapid exhalation segment is performed.

8. The method according to claim 1, characterized in that, The acquisition of the chest and abdominal displacement signal of the target object during respiration includes: Acquire the echo signal of the target object based on radar acquisition; The distance between the chest and abdomen of the target object and the gate is determined based on the echo signal. The original phase signal is extracted based on the distance gate; The original phase signal is unwound to obtain the chest and abdomen displacement signal.

9. A lung function index analysis device, characterized in that, include: A processor and a memory connected to the processor; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to cause the processor to perform the lung function index analysis method as described in any one of claims 1-8.

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