A method and device for assisting right ventricular angiography based on pressure signal feedback

Abstract

This invention relates to the field of biomedical technology, specifically a method and apparatus for assisting right ventricular angiography based on pressure signal feedback. The method includes: identifying multiple effective pressure acquisition points; acquiring pressure signals from an initial subject based on these points to obtain an initial pressure sequence set; acquiring a second pressure sequence set using a pre-identified respiratory flow sensor; fusing the multiple initial and second pressure sequence sets to obtain a fused pressure sequence; extracting features from the fused pressure sequence to obtain a staged pressure feature set; acquiring a motion quality index sequence; performing pattern recognition matching between the motion quality index sequence and a standard template sequence to obtain a standard motion similarity; and acquiring multimodal motion prompts based on the standard motion similarity. This invention addresses the problem of right ventricular angiography failures requiring multiple manual interventions due to the subject's inability to autonomously, stably, and accurately execute standardized Warburg maneuvers.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a method and device for assisting right ventricular angiography based on pressure signal feedback. Background Technology

[0002] Right ventricular angiography is a commonly used cardiovascular imaging technique in clinical practice. Its imaging quality is highly dependent on the subject's ability to correctly perform the Warburg maneuver. By acquiring a sequence of movement quality indicators based on pressure changes during the performance of the Warburg maneuver, and then using this sequence to generate multimodal movement cues, the study provides the subject with corresponding adjustment methods for the Warburg maneuver.

[0003] Currently, the Valsalva maneuver is primarily guided by verbal instructions from the physician and the subject's subjective feelings. While traditional assistive methods provide adjustment options for the subject, they lack objective, real-time quantitative feedback mechanisms. Therefore, optimizing assistive methods for right ventricular angiography is crucial for improving the success rate of right ventricular angiography. Summary of the Invention

[0004] This invention provides a method for assisting right ventricular angiography based on pressure signal feedback and a computer-readable storage medium. Its main purpose is to solve the problem that right ventricular angiography fails because the subject is unable to autonomously, stably and accurately perform the standardized Valsalva maneuver, resulting in repeated manual guidance.

[0005] To achieve the above objectives, the present invention provides a method for assisting right ventricular angiography based on pressure signal feedback, comprising: Based on the pre-confirmed initial subjects, multiple effective pressure collection points were identified; Perform the following operation for each of the multiple valid pressure acquisition points: Pressure signals were collected from the initial subjects based on effective pressure acquisition points to obtain an initial pressure sequence set. By summing the initial pressure sequence sets, multiple initial pressure sequence sets are obtained; A second pressure sequence set is obtained using a pre-confirmed respiratory flow sensor; Multiple initial pressure sequence sets and a second pressure sequence set are fused to obtain a fused pressure sequence. Feature extraction is performed on the fused pressure sequence to obtain a staged pressure feature set; Obtain action quality index sequences using phased pressure feature sets; The sequence of action quality indicators is matched with the pre-confirmed standard template sequence by pattern recognition to obtain the standard action similarity. Multimodal action prompts are obtained based on the standard action similarity.

[0006] Optionally, the pre-identified initial subject identifies multiple valid pressure collection points, including: Based on the initial subjects, several key body surface locations were identified; Perform the following operation for each of the multiple key body surface locations: Pressure is detected at key body surface locations at a preset pressure acquisition frequency to obtain a test pressure set. A fast Fourier transform is performed on the test pressure set to obtain the pressure signal spectrum; Effective signal energy and noise energy are obtained based on the pressure signal spectrum; Calculate signal quality indicators based on effective signal energy and noise energy; Compare the signal quality index with the preset signal quality threshold. If the signal quality index is greater than or equal to the signal quality threshold, then mark the key body surface location as an effective pressure acquisition point. If the signal quality index is less than the signal quality threshold, then an optimized sampling point is obtained and used as an effective pressure sampling point. By summing up the effective pressure acquisition points, the multiple effective pressure acquisition points are obtained.

[0007] Optionally, obtaining optimized collection points includes: Obtain the neighborhood range of key body surface locations; The neighborhood range is divided into grids according to a preset grid spacing to obtain multiple candidate grid points; Multiple test signal qualities are obtained using multiple candidate grid points, where each test signal quality corresponds one-to-one with a candidate grid point; Each of the plurality of test signal qualities is compared with a signal quality threshold. If among multiple test signal qualities, there is a test signal quality greater than or equal to the signal quality threshold, then the optimal acquisition point is identified; If there is no test signal quality greater than or equal to the signal quality threshold among multiple test signal quality, then the maximum test signal quality is obtained, and the initial sub-advantage is determined based on the maximum test signal quality. Multiple secondary candidate points are obtained by utilizing the initial suboptimal advantage and the preset suboptimal adjustment length; Multiple secondary test signal qualities are obtained using multiple secondary candidate points, where each secondary test signal quality corresponds one-to-one with a secondary candidate point; Extract the maximum secondary test signal quality from the plurality of secondary test signal quality; If the maximum secondary test signal quality is greater than the maximum test signal quality, then the secondary candidate point corresponding to the maximum secondary test signal quality is used as the optimized acquisition point. If the maximum secondary test signal quality is less than or equal to the maximum test signal quality, then the initial secondary advantage point will be used as the optimized acquisition point.

[0008] Optionally, the step of acquiring pressure signals from the initial subject based on effective pressure acquisition points to obtain an initial pressure sequence set includes: The target sensor is acquired based on the initial subjects and effective pressure acquisition points; The original pressure signal sequence is obtained based on the target sensor and the preset sampling frequency; The original pressure signal sequence is subjected to baseline drift removal to obtain a corrected signal sequence; The correction signal sequence is filtered to obtain a filtered signal sequence; The filtered signal sequence is normalized to obtain a standardized pressure sequence; The standardized pressure sequence is subjected to phase identification to obtain the inhalation start point, breath-holding start point, and exhalation start point; Based on the inhalation start point, breath-holding start point, and exhalation start point, the standardized pressure sequence is segmented to obtain the inhalation phase pressure sequence, breath-holding phase pressure sequence, and exhalation phase pressure sequence. The pressure sequences during the inhalation phase, breath-holding phase, and exhalation phase are summarized to obtain an initial pressure sequence set.

[0009] Optionally, the step of performing phase identification on the standardized pressure sequence to obtain the inspiratory start point, breath-holding start point, and expiratory start point includes: The standardized pressure sequence is subjected to first-order difference calculation to obtain the pressure change rate sequence; The pressure change rate sequence was subjected to sliding window peak detection to obtain multiple positive peak points and multiple negative peak points; The multiple positive peak points are filtered based on a preset inhalation amplitude threshold to obtain a candidate positive peak point set, wherein the candidate positive peak point set includes multiple candidate positive peak points; The multiple negative peak points are filtered based on a preset exhalation amplitude threshold to obtain a candidate negative peak point set, wherein the candidate negative peak point set includes multiple candidate negative peak points. The main positive peak point is obtained based on multiple candidate positive peak points in the candidate positive peak point set; The main negative peak point is obtained based on multiple candidate negative peak points in the candidate negative peak point set; The inhalation start point is obtained by performing a reverse search using the main positive peak point, a preset backtracking time window, and a pressure change rate sequence. The breath-holding start point is obtained based on the main positive peak point, the main negative peak point, and the preset stable threshold. The exhalation initiation point is obtained using the main negative peak point, the retrospective time window, and the pressure change rate sequence.

[0010] Optionally, the step of extracting features from the fused pressure sequence to obtain a staged pressure feature set includes: Based on the fused pressure sequence, an inspiratory pressure sequence, a breath-holding pressure sequence, and an expiratory pressure sequence are obtained; Inspiratory peak pressure characteristics were obtained based on the inspiratory pressure value sequence; Based on the inspiratory pressure sequence, the number of inspiratory pressures, multiple inspiratory pressure values, and multiple inspiratory times were identified, with each inspiratory pressure value corresponding to an inspiratory time. The inspiratory pressure rise characteristic is calculated using inspiratory pressure counts, multiple inspiratory pressure values, and multiple inspiratory times. The formula for calculating the inspiratory pressure rise characteristic is shown below: in, This indicates a rise in inspiratory pressure. Indicates the quantity of inhalation pressure. Indicates the first of multiple inspiratory times One inhalation time, This represents the first of multiple inspiratory pressure values. One inhalation pressure value; By summarizing the inspiratory peak pressure characteristics and inspiratory pressure rise characteristics, an inspiratory phase feature set is obtained; The breath-holding stage feature set is obtained based on the breath-holding pressure sequence, which includes breath-holding duration, average breath-holding pressure and standard deviation of breath-holding pressure. Calculation of expiratory pressure drop characteristics based on expiratory pressure sequences; By summarizing the inspiratory phase feature set, the breath-holding phase feature set, and the expiratory pressure decrease feature set, a phased pressure feature set is obtained.

[0011] Optionally, the step of obtaining the action quality index sequence using a phased pressure feature set includes: The first quality index is calculated based on the peak inhalation pressure characteristics in the aforementioned staged pressure feature set and the preset inhalation target pressure. The second quality index is calculated based on the inhalation pressure rise characteristics in the aforementioned staged pressure feature set and the preset reference rise slope. A third quality index is obtained based on the average breath-holding pressure, standard deviation of breath-holding pressure, and preset ideal breath-holding pressure range of the aforementioned stage-specific pressure characteristic set. The fourth quality index is calculated based on the breath-holding duration of the aforementioned stage-specific pressure characteristic set and the preset shortest effective breath-holding duration. The fifth quality index is calculated based on the expiratory pressure drop characteristics in the aforementioned staged pressure feature set and the preset reference drop slope. The first, second, third, fourth, and fifth quality indicators are arranged in a preset physiological order to obtain a sequence of action quality indicators.

[0012] Optionally, the step of performing pattern recognition matching between the action quality index sequence and the pre-confirmed standard template sequence to obtain standard action similarity includes: The difference value vector is obtained using the action quality index sequence and the standard template sequence; The total distance value is calculated using the difference vector and the pre-confirmed dimension weight vector; The standard action similarity is calculated based on the total distance value, and the formula for calculating the standard action similarity is as follows: in, Indicates the similarity of standard movements. This represents the natural exponential function. This indicates the preset scaling factor. This represents the total distance value.

[0013] Optionally, obtaining multimodal action prompts based on the standard action similarity includes: The similarity of standard actions is compared with a preset similarity threshold; If the similarity of the standard action is greater than or equal to the similarity threshold, then the action qualification instruction is obtained and the action qualification instruction is used as a multimodal action prompt instruction. If the similarity of the standard actions is less than the similarity threshold, an abnormal indicator set is identified based on the difference value vector and the preset difference threshold. The abnormal feature set includes one or more abnormal indicators. The set of adjustment schemes is obtained based on the set of abnormal indicators and the preset correction strategy mapping table; The set of adjustment schemes will be used as multimodal action prompts.

[0014] To achieve the above objectives, the present invention also provides a right ventricular angiography action assist device based on pressure signal feedback, comprising: The data acquisition location confirmation module is used to identify multiple effective pressure acquisition points based on the pre-confirmed initial subject. The pressure signal acquisition module performs the following operation on each of the multiple valid pressure acquisition points: Pressure signals were collected from the initial subjects based on effective pressure acquisition points to obtain an initial pressure sequence set. By summing the initial pressure sequence sets, multiple initial pressure sequence sets are obtained; A pressure feature extraction module is used to acquire a second pressure sequence set using a pre-confirmed respiratory flow sensor; Multiple initial pressure sequence sets and a second pressure sequence set are fused to obtain a fused pressure sequence. Feature extraction is performed on the fused pressure sequence to obtain a staged pressure feature set; The motion instruction feedback module is used to obtain a sequence of motion quality indicators using a phased pressure feature set; The sequence of action quality indicators is matched with the pre-confirmed standard template sequence by pattern recognition to obtain the standard action similarity. Multimodal action prompts are obtained based on the standard action similarity.

[0015] To address the above problems, the present invention also provides an electronic device, the electronic device comprising: A memory that stores at least one instruction; and a processor that executes the instruction stored in the memory to implement the aforementioned right ventricular angiography action-assisted method based on pressure signal feedback.

[0016] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned right ventricular angiography action-assisted method based on pressure signal feedback.

[0017] To address the problems described in the background art, this invention identifies multiple effective pressure acquisition points based on pre-confirmed initial subjects. This demonstrates that the embodiments of this invention automatically adapt to individual differences in subjects, locating key body surface locations with optimal signal quality, providing a stable and reliable data source for subsequent analysis, and resolving the problem of poor signal quality caused by arbitrary placement of pressure sensors. Next, this invention performs the following operations on each of the multiple effective pressure acquisition points: acquiring pressure signals from the initial subject based on the effective pressure acquisition point to obtain an initial pressure sequence set; summarizing the initial pressure sequence sets to obtain multiple initial pressure sequence sets. This embodiment of the invention obtains multiple initial pressure sequence sets reflecting the pressure distribution in the thoracic and abdominal cavities through multi-point synchronous acquisition of pressure signals, providing a data foundation for assessing the mechanical consistency of the Valsalva maneuver. Furthermore, this invention acquires a second pressure sequence set using a pre-confirmed respiratory flow sensor, fuses the multiple initial pressure sequence sets and the second pressure sequence set to obtain a fused pressure sequence, and extracts features from the fused pressure sequence to obtain a staged pressure feature set. This invention, through the cascaded processing of sequence fusion and feature extraction, eliminates redundancy, improves data consistency, and simultaneously achieves data dimensionality reduction and highlights key features. Furthermore, this invention obtains a sequence of action quality indicators by utilizing a phased stress feature set, performs pattern recognition matching between this sequence and a pre-confirmed standard template sequence to obtain standard action similarity, and generates multimodal action prompts based on this standard action similarity. Thus, this invention transforms stress features into calculable indicators and performs template matching, achieving accurate quantification of action deviation while generating multimodal action prompts. Therefore, this invention can solve the problem of right ventricular angiography failure in subjects due to their inability to autonomously, stably, and accurately perform standardized Valsalva maneuvers, requiring multiple manual interventions. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for assisting right ventricular angiography based on pressure signal feedback, according to an embodiment of the present invention. Figure 2 A functional block diagram of a right ventricular angiography action assist device based on pressure signal feedback provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the right ventricular angiography action assistance method based on pressure signal feedback, according to an embodiment of the present invention.

[0019] Explanation of reference numerals in the attached figures: 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0022] This application provides a method for assisting right ventricular angiography based on pressure signal feedback. The execution entity of this method includes, but is not limited to, at least one electronic device configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or server device, and the software may be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0023] Reference Figure 1 The diagram shown is a flowchart illustrating a right ventricular angiography procedure assistance method based on pressure signal feedback according to an embodiment of the present invention. In this embodiment, the right ventricular angiography procedure assistance method based on pressure signal feedback includes: S1. Based on the pre-confirmed initial subjects, multiple effective pressure collection points were identified.

[0024] It should be explained that the pre-identified initial subjects identified multiple valid pressure collection points, including: Based on the initial subjects, several key body surface locations were identified; Perform the following operation for each of the multiple key body surface locations: Pressure is detected at key body surface locations at a preset pressure acquisition frequency to obtain a test pressure set. A fast Fourier transform is performed on the test pressure set to obtain the pressure signal spectrum; Effective signal energy and noise energy are obtained based on the pressure signal spectrum; Calculate signal quality indicators based on effective signal energy and noise energy; Compare the signal quality index with the preset signal quality threshold. If the signal quality index is greater than or equal to the signal quality threshold, then mark the key body surface location as an effective pressure acquisition point. If the signal quality index is less than the signal quality threshold, then an optimized sampling point is obtained and used as an effective pressure sampling point. By summing up the effective pressure acquisition points, the multiple effective pressure acquisition points are obtained.

[0025] Furthermore, the initial subjects are those about to perform the Valsalva maneuver. Based on these initial subjects, multiple key surface locations are identified, referring to multiple locations on the chest and abdomen of the initial subjects used to monitor intrapleural pressure or abdominal pressure. These key surface locations can be obtained through human surface anatomy and atlases. Key surface locations are locations used to monitor intrapleural pressure or abdominal pressure, such as the parasternal and rectus abdominis areas. The pressure acquisition frequency refers to the number of pressure measurements per second, for example, 100 Hz. Pressure measurement refers to the process of measuring intrapleural pressure or abdominal pressure at the key surface locations of the initial subjects. Optionally, a piezoresistive pressure sensor is used as the pressure sensor. The test pressure set refers to the collection obtained by sorting the multiple intrapleural pressures or multiple abdominal pressures obtained through pressure measurement in chronological order. Fast Fourier Transform (FFT) is an algorithm for converting the test pressure set into a frequency domain signal; FFT is existing technology and will not be elaborated upon here. The pressure signal spectrum refers to the frequency domain signal obtained by performing a fast Fourier transform on the test pressure set. The pressure signal spectrum includes multiple elements, including frequency and amplitude, with a one-to-one correspondence between amplitude and frequency.

[0026] Understandably, obtaining effective signal energy and noise energy based on the pressure signal spectrum refers to summing the squared amplitudes of all elements within a pre-defined low-frequency band (e.g., 0.1 Hz to 0.5 Hz) in the pressure signal spectrum; this sum represents the effective signal energy. Conversely, the average value is calculated by squaring the signal amplitudes of all elements outside the low-frequency band in the pressure signal spectrum; this average value represents the noise energy. The signal quality index is the ratio obtained by dividing the effective signal energy by the noise energy. The signal quality threshold is a threshold value used to determine whether a key body surface location is an effective pressure acquisition point; for example, 10. An effective pressure acquisition point is a key body surface location corresponding to a signal quality index greater than or equal to the signal quality threshold.

[0027] It should be explained that obtaining optimized collection points includes: Obtain the neighborhood range of key body surface locations; The neighborhood range is divided into grids according to a preset grid spacing to obtain multiple candidate grid points; Multiple test signal qualities are obtained using multiple candidate grid points, where each test signal quality corresponds one-to-one with a candidate grid point; Each of the plurality of test signal qualities is compared with a signal quality threshold. If among multiple test signal qualities, there is a test signal quality greater than or equal to the signal quality threshold, then the optimal acquisition point is identified; If there is no test signal quality greater than or equal to the signal quality threshold among multiple test signal quality, then the maximum test signal quality is obtained, and the initial sub-advantage is determined based on the maximum test signal quality. Multiple secondary candidate points are obtained by utilizing the initial suboptimal advantage and the preset suboptimal adjustment length; Multiple secondary test signal qualities are obtained using multiple secondary candidate points, where each secondary test signal quality corresponds one-to-one with a secondary candidate point; Extract the maximum secondary test signal quality from the plurality of secondary test signal quality; If the maximum secondary test signal quality is greater than the maximum test signal quality, then the secondary candidate point corresponding to the maximum secondary test signal quality is used as the optimized acquisition point. If the maximum secondary test signal quality is less than or equal to the maximum test signal quality, then the initial secondary advantage point will be used as the optimized acquisition point.

[0028] Furthermore, the neighborhood refers to a square area with a side length of 4 cm, extending 2 cm upwards, downwards, leftwards, and rightwards from the key body surface location as the center. The grid spacing refers to the distance between two adjacent candidate grid points during grid division, for example, 0.5 cm. Grid division refers to the process of dividing the neighborhood area, using the key body surface location as the origin, and according to the grid spacing, into all coordinate points in both the horizontal and vertical directions. Candidate grid points are coordinate points located within the neighborhood area obtained through grid division.

[0029] For example, when the neighborhood is a square with a side length of 4 cm and the grid spacing is 0.5 cm, a Cartesian coordinate system is constructed with the key surface location as the center. Optionally, a Cartesian coordinate system is used as the Cartesian coordinate system. First, multiple horizontal coordinates are determined: -2.0, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2.0. Then, multiple vertical coordinates are determined: -2.0, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2.0. Finally, each horizontal coordinate and each vertical coordinate are combined to obtain multiple coordinate points, such as (-2.0, -2.0), (-2.0, -1.5), ... (0, 0), ..., (2.0, 2.0). Thus, a total of 81 coordinate points are obtained, which are the candidate grid points.

[0030] Understandably, acquiring multiple test signal qualities using multiple candidate grid points refers to the process of performing pressure detection at the candidate grid points and calculating the signal quality index of the candidate grid points. The method for acquiring multiple test signal qualities using multiple candidate grid points is the same as the method for acquiring signal quality indices using key body surface locations, and will not be elaborated here. Test signal quality is the signal quality index corresponding to the candidate grid point. Identifying the optimal acquisition point refers to summarizing the candidate grid points corresponding to test signal qualities greater than or equal to the signal quality threshold to obtain multiple qualified points. Multiple straight-line distances are calculated using these qualified points and the key body surface location, and the candidate grid point corresponding to the smallest straight-line distance among these distances is taken as the optimal acquisition point. Qualified points are candidate grid points corresponding to test signal qualities greater than or equal to the signal quality threshold. Straight-line distance is the distance between the qualified point and the key body surface location. The calculation method for straight-line distance is existing technology and will not be elaborated here. The maximum test signal quality is the largest test signal quality among the multiple test signal qualities when no test signal quality greater than or equal to the signal quality threshold exists. The initial secondary advantage is the candidate grid point corresponding to the maximum test signal quality.

[0031] Furthermore, the suboptimal adjustment length is the length extending upwards, downwards, leftwards, or rightwards from the initial suboptimal point. Obtaining multiple secondary candidate points using the initial suboptimal point and the preset suboptimal adjustment length refers to constructing a secondary search region centered on the initial suboptimal point, with sides equal to twice the suboptimal adjustment length. Multiple secondary candidate points are then obtained using this secondary search region. The method for obtaining multiple secondary candidate points using the secondary search region is the same as the method for obtaining multiple candidate grid points using a neighborhood range, and will not be repeated here. The secondary search region is a square region centered on the initial suboptimal point, with sides equal to twice the suboptimal adjustment length. Secondary candidate points are coordinate points obtained by meshing the secondary search region. The secondary test signal quality set refers to the test signal quality corresponding to the secondary candidate points. The method for obtaining multiple secondary test signal qualities using multiple secondary candidate points is the same as the method for obtaining multiple test signal qualities using multiple candidate grid points, and will not be repeated here. The maximum secondary test signal quality is the largest among the multiple secondary test signal qualities.

[0032] S2. For each of the multiple effective pressure acquisition points, perform the following operations: acquire pressure signals from the initial subject based on the effective pressure acquisition point to obtain an initial pressure sequence set, and summarize the initial pressure sequence set to obtain multiple initial pressure sequence sets.

[0033] It should be explained that the initial pressure sequence set obtained by collecting pressure signals from the initial subject based on effective pressure acquisition points includes: The target sensor is acquired based on the initial subjects and effective pressure acquisition points; The original pressure signal sequence is obtained based on the target sensor and the preset sampling frequency; The original pressure signal sequence is subjected to baseline drift removal to obtain a corrected signal sequence; The correction signal sequence is filtered to obtain a filtered signal sequence; The filtered signal sequence is normalized to obtain a standardized pressure sequence; The standardized pressure sequence is subjected to phase identification to obtain the inhalation start point, breath-holding start point, and exhalation start point; Based on the inhalation start point, breath-holding start point, and exhalation start point, the standardized pressure sequence is segmented to obtain the inhalation phase pressure sequence, breath-holding phase pressure sequence, and exhalation phase pressure sequence. The pressure sequences during the inhalation phase, breath-holding phase, and exhalation phase are summarized to obtain an initial pressure sequence set.

[0034] Furthermore, acquiring the target sensor based on the initial subject and effective pressure acquisition points refers to deploying a pressure sensor at the effective pressure acquisition points on the initial subject's body surface to obtain the target sensor. The target sensor is the pressure sensor deployed at the effective pressure acquisition points on the initial subject's body surface. Acquiring the original pressure signal sequence based on the target sensor and a preset sampling frequency refers to using the target sensor to collect pressure at the effective pressure acquisition points of the initial subject performing the Valsalva maneuver according to the sampling frequency, thus obtaining the original pressure signal sequence. The sampling frequency refers to the number of times the pressure at the effective pressure acquisition point is collected per second, for example, 100 Hz. The original pressure signal sequence is a sequence obtained by arranging multiple pressures collected by the target sensor in chronological order, with each pressure corresponding to a pressure acquisition time. The pressure acquisition time is the time the target sensor spends collecting pressure.

[0035] For example, pressure is collected from time 0.00 seconds. At pressure collection time of 0.00 seconds, the corresponding pressure is 2.15, at pressure collection time of 0.01 seconds, the corresponding pressure is 2.22, at pressure collection time of 0.02 seconds, the corresponding pressure is 2.30, at pressure collection time of 0.03 seconds, the corresponding pressure is 2.25, and at pressure collection time of 0.04 seconds, the corresponding pressure is 2.18. Then the original pressure signal sequence is [2.15 (0.00), 2.22 (0.01), 2.30 (0.02), 2.25 (0.03), 2.18 (0.04)].

[0036] Further, baseline drift removal is a process used to eliminate the effects of initial subject positional movement or sensor drift in the original pressure signal sequence. Optionally, high-pass filtering is used as the baseline drift removal method. The corrected signal sequence is the original pressure signal sequence after baseline drift removal. Filtering is a process of removing high-frequency noise (e.g., muscle tremors) from the original pressure signal sequence. Optionally, low-pass filtering is used as the filtering method. The filtered signal sequence is the corrected signal sequence after filtering. Normalization is a process of mapping multiple pressures in the filtered signal sequence to the range [-1, 1]. Optionally, Min-Max normalization is used as the normalization method. The normalized pressure sequence is the filtered signal sequence after normalization. Sequence segmentation is a process of segmenting the normalized pressure sequence after the inspiratory initiation point, starting from the expiratory initiation point, from the breath-holding initiation point and the expiratory initiation point respectively, to obtain three sequences. The inspiratory phase pressure sequence is a segment of the normalized pressure sequence from the inspiratory initiation point to the breath-holding initiation point. A breath-hold phase pressure sequence is a segment of a standardized pressure sequence that begins at the start of breath-holding and ends at the start of expiration. A expiratory phase pressure sequence is a segment of a standardized pressure sequence that begins at the start of expiration and ends at the end of the Valsalva maneuver.

[0037] It should be explained that the step-by-step identification of the standardized pressure sequence to obtain the inspiratory start point, breath-holding start point, and expiratory start point includes: The standardized pressure sequence is subjected to first-order difference calculation to obtain the pressure change rate sequence; The pressure change rate sequence was subjected to sliding window peak detection to obtain multiple positive peak points and multiple negative peak points; The multiple positive peak points are filtered based on a preset inhalation amplitude threshold to obtain a candidate positive peak point set, wherein the candidate positive peak point set includes multiple candidate positive peak points; The multiple negative peak points are filtered based on a preset exhalation amplitude threshold to obtain a candidate negative peak point set, wherein the candidate negative peak point set includes multiple candidate negative peak points. The main positive peak point is obtained based on multiple candidate positive peak points in the candidate positive peak point set; The main negative peak point is obtained based on multiple candidate negative peak points in the candidate negative peak point set; The inhalation start point is obtained by performing a reverse search using the main positive peak point, a preset backtracking time window, and a pressure change rate sequence. The breath-holding start point is obtained based on the main positive peak point, the main negative peak point, and the preset stable threshold. The exhalation initiation point is obtained using the main negative peak point, the retrospective time window, and the pressure change rate sequence.

[0038] Furthermore, first-order differencing is the process of calculating the difference between two adjacent elements in the standardized pressure sequence. The pressure rate of change sequence is the sequence obtained after first-order differencing of the pressure rate of change sequence, where the pressure rate of change sequence includes multiple pressure rates of change. The pressure rate of change is the difference between two adjacent elements in the standardized pressure sequence. Sliding window peak detection refers to the process of using a fixed-length sliding window (e.g., 10 elements) to slide across the pressure rate of change sequence at a preset step size (e.g., 1 element), and finding the maximum and minimum values ​​among the multiple pressure rates of change contained in the pressure rate of change sequence within each sliding window. A positive peak point is the maximum value among the multiple pressure rates of change contained within the sliding window during peak detection. A negative peak point is the minimum value among the multiple pressure rates of change contained within the sliding window during peak detection. The inspiratory amplitude threshold is a threshold value used to filter candidate positive peak points, for example, 0.15. When the rate of pressure change corresponding to a positive peak point is greater than the inspiratory amplitude threshold, the positive peak point is considered a candidate positive peak point. The expiratory amplitude threshold is a threshold value used to filter candidate negative peak points, for example, -0.12. When the rate of pressure change corresponding to a negative peak point is less than the inspiratory amplitude threshold, the negative peak point is considered a candidate negative peak point. The candidate positive peak point set is a collection containing multiple candidate positive peak points. The candidate negative peak point set is a collection containing multiple candidate negative peak points. A candidate positive peak point is a positive peak point with a rate of pressure change greater than the inspiratory amplitude threshold. A candidate negative peak point is a negative peak point with a rate of pressure change less than the inspiratory amplitude threshold. Obtaining the master positive peak point based on multiple candidate positive peak points in the candidate positive peak point set refers to selecting the candidate positive peak point with the largest rate of pressure change from the candidate positive peak point set as the master candidate positive peak point. The master positive peak point is the candidate positive peak point with the largest rate of pressure change in the candidate positive peak point set. The process of identifying the primary negative peak point from multiple candidate negative peak points in a candidate negative peak point set refers to selecting the candidate negative peak point with the smallest rate of pressure change from the set. The primary negative peak point is the candidate negative peak point with the smallest rate of pressure change within the set of candidate negative peak points.

[0039] Understandably, the backtracking time window is the time range used for reverse search, for example, 0.3 seconds. Reverse search refers to starting from the main positive peak point and, within the backtracking time window, searching multiple pressure change rates in the pressure change rate sequence from late to early in time, to obtain the time point at which the pressure change rate changes from a pre-confirmed negative number to a pre-confirmed positive number. A positive number refers to a real number greater than 0. A negative number refers to a real number less than 0. The inhalation start point is the time point at which the inhalation phase begins in the Valsalva maneuver; specifically, it refers to the critical point within the backtracking time window before the main positive peak point where the pressure change rate changes from negative to positive (i.e., the pressure begins to rise). Obtaining the breath-holding start point based on the main positive peak point, the main negative peak point, and a preset stability threshold refers to finding the longest subsequence in the pressure change rate sequence between the main positive and negative peak points where the absolute value of the pressure change rate is continuously less than or equal to the stability threshold; the starting point of this subsequence is the breath-holding start point. The stability threshold is a threshold value used to determine whether the Valsalva maneuver has entered the breath-holding phase, for example, 0.02. The breath-holding phase is the stage in the Valsalva maneuver where, after a forceful inhalation, the initial subject closes the glottis and forcefully exhales without exhaling any air, causing a rapid increase in intrathoracic pressure that remains stable. The breath-holding initiation point is the time point at which the breath-holding phase begins in the Valsalva maneuver. The exhalation initiation point is the time point at which the exhalation phase begins in the Valsalva maneuver. Specifically, it refers to the critical point within the retrospective time window before the main negative peak point where the pressure change rate changes from positive to negative (i.e., pressure begins to decrease). The method for obtaining the exhalation initiation point using the main negative peak point, the retrospective time window, and the pressure change rate sequence is the same as the method for obtaining the inspiratory initiation point by performing a reverse search using the main positive peak point, the preset retrospective time window, and the pressure change rate sequence, and will not be elaborated further here.

[0040] S3. Use the pre-confirmed respiratory flow sensor to obtain the second pressure sequence set, and perform sequence fusion on multiple initial pressure sequence sets and the second pressure sequence set to obtain the fused pressure sequence.

[0041] Furthermore, the acquisition of the second pressure sequence set using a pre-confirmed respiratory flow sensor includes: Raw flow rate signal sequences were obtained using a respiratory flow sensor and initial subjects; The original flow velocity signal sequence is normalized to obtain a standardized flow velocity signal sequence; The standardized flow rate signal sequence was subjected to flow rate phase identification to obtain the second inspiratory initiation point, the second breath-holding initiation point, and the second expiratory initiation point; The inspiratory flow sequence, the breath-hold flow sequence, and the expiratory flow sequence were obtained using the second inspiratory initiation point, the second breath-hold initiation point, and the second expiratory initiation point. The flow rate sequence during inhalation, breath-holding, and exhalation was converted into a flow rate-pressure sequence to obtain the second inhalation pressure sequence, the second breath-holding pressure sequence, and the second exhalation pressure sequence. The pressure sequences of the second inspiratory phase, the second breath-holding phase, and the second expiratory phase are combined to obtain the second pressure sequence set.

[0042] Understandably, obtaining the raw flow rate signal sequence using a respiratory flow sensor and an initial subject refers to placing the respiratory flow sensor at the initial subject's mouth and nose before the initial subject performs the Warburg maneuver, instructing the initial subject to perform a complete Warburg maneuver, and simultaneously activating the respiratory flow sensor to acquire signals according to the sampling frequency, thus obtaining the raw flow rate signal sequence. The raw flow rate signal sequence refers to a sequence acquired by the respiratory flow sensor, arranged chronologically from earliest to latest, containing multiple flow rate values ​​and their corresponding acquisition times. Here, the flow rate value represents the speed and direction of the airflow generated by the Warburg maneuver (positive values ​​for inhalation, negative values ​​for exhalation), and the flow rate acquisition time is the moment the flow rate value was acquired. The standardized flow rate signal sequence is the raw flow rate signal sequence after normalization. Flow phase identification refers to the process of analyzing the standardized flow signal sequence to identify the starting points of each phase of the Valsalva maneuver. Specifically, the moment when the flow value in the standardized flow signal sequence first changes from a positive value (inhalation) to 0 is taken as the second breath-hold starting point; the moment when the flow value in the standardized flow signal sequence first changes from 0 to a negative value (exhalation) is taken as the second exhalation starting point; and the moment when the flow value in the standardized flow signal sequence first changes from 0 to a positive value is taken as the second inhalation starting point. The second inhalation starting point is the moment when the inhalation phase begins, identified based on the standardized flow signal sequence. The second breath-hold starting point is the moment when the breath-hold phase begins, identified based on the standardized flow signal sequence. The second exhalation starting point is the moment when the exhalation phase begins, identified based on the standardized flow signal sequence. The inhalation phase flow sequence is a subsequence of the standardized flow signal sequence from the second inhalation starting point to the second breath-hold starting point. The breath-hold phase flow sequence is a subsequence of the standardized flow signal sequence from the second breath-hold starting point to the second exhalation starting point. The expiratory flow sequence is a subsequence of the standardized flow signal sequence from the second expiratory initiation point to the end of the Valsalva maneuver.

[0043] Furthermore, the velocity-pressure conversion is the process of converting velocity values ​​into equivalent pressure using a pre-built conversion function, as shown below: in, Represents the normalized flow velocity signal sequence The equivalent pressure corresponding to a given moment. This represents the preset equivalent drag coefficient. Represents the normalized flow velocity signal sequence The flow velocity value corresponding to the time. This indicates the preset baseline pressure.

[0044] Understandably, the equivalent drag coefficient is a weighted coefficient used to measure the degree to which flow velocity affects pressure, for example, 0.4. Baseline pressure represents the pressure of the initial subject in a no-flow state (i.e., flow velocity of 0), for example, 0.5. The equivalent drag coefficient and baseline pressure can be obtained through physiological knowledge or experimental data, and will not be elaborated here.

[0045] Understandably, the second inspiratory phase pressure sequence is a sequence containing multiple pressures obtained by performing a flow-to-pressure conversion on the inspiratory phase flow sequence from the standardized flow signal sequence. The second breath-hold phase pressure sequence is a sequence containing multiple pressures obtained by performing a flow-to-pressure conversion on the breath-hold phase flow sequence from the standardized flow signal sequence. The second expiratory phase pressure sequence is a sequence containing multiple pressures obtained by performing a flow-to-pressure conversion on the expiratory phase flow sequence from the standardized flow signal sequence. The second pressure sequence set is a collection containing the second inspiratory phase pressure sequence, the second breath-hold phase pressure sequence, and the second expiratory phase pressure sequence.

[0046] It needs to be explained that sequence fusion is the process of fusing multiple initial pressure sequence sets and a second pressure sequence set collected from multiple effective pressure acquisition points along the time dimension to obtain a fused pressure sequence. Specifically, because the sampling frequency of the initial pressure sequence set is the same as and synchronized with the sampling frequency of the second pressure sequence set, the pressure acquisition time corresponding to the element in the multiple initial pressure sequence sets corresponds one-to-one with and is the same as the flow velocity value acquisition time corresponding to the element in the second pressure sequence set. Here, the pressure acquisition time and flow velocity value acquisition time are unified as the acquisition time, and the initial pressure sequence set contains multiple acquisition times. Multiple acquisition times are extracted, and the following operations are performed on each of the multiple acquisition times: based on the acquisition time, multiple synchronous acquisition elements are extracted from the multiple initial pressure sequence sets and the second pressure sequence set; the fused pressure value is calculated using the multiple synchronous acquisition elements; the fused pressure value is summarized to obtain multiple fused pressure values; the multiple fused pressure values ​​are sorted in ascending order of time to obtain the fused pressure sequence. The synchronous acquisition element is an element at a certain acquisition time in the initial pressure sequence set or the second pressure sequence set. Extracting multiple synchronously acquired elements from multiple initial pressure sequence sets and a second pressure sequence set based on acquisition time refers to extracting elements at the same acquisition time from multiple initial pressure sequence sets and a second pressure sequence set to obtain multiple synchronously acquired elements. The fused pressure value is the arithmetic mean of the multiple synchronously acquired elements. The fused pressure sequence refers to the sequence obtained through sequence fusion, used to characterize the pressure changes during this Warburg maneuver.

[0047] For example, suppose there are three valid pressure acquisition points (first acquisition point, second acquisition point, and third acquisition point). The initial pressure sequence set corresponding to the first acquisition point is [-0.50 (0.00), -0.30 (0.01), -0.10 (0.02), 0.20 (0.03), 0.50 (0.04), 0.70 (0.05)], and the initial pressure sequence set corresponding to the second acquisition point is [−0.80 (0.00), −0.60 (0.01), −0.30 (0.02), 0. The initial pressure sequence set corresponding to the third acquisition point is [−0.20(0.00), 0.00(0.01), 0.30(0.02), 0.60(0.03), 0.80(0.04), 0.90(0.05)], and the second pressure sequence set is [−1.00(0.00), −0.80(0.01), −0.40(0.02), 0.00(0.03), 0.40(0.05)]. If 0.04), 0.70(0.05)], then multiple acquisition times (0.00, 0.01, 0.02, 0.03, 0.04, 0.05) are extracted. Assuming one acquisition time (e.g., 0.00 seconds) is extracted, then multiple synchronous acquisition elements are -0.50, -0.80, -0.20, -1.00. The fusion pressure value corresponding to the acquisition time is the arithmetic mean of -0.50, -0.80, -0.20, -1.00 (-0.625). The multiple fusion pressure values ​​corresponding to the multiple acquisition times are -0.625 (0.00), -0.425 (0.01), -0.125 (0.02), 0.200 (0.03), 0.500 (0.04), and 0.725 (0.05), respectively. The fusion pressure sequence is [-0.625 (0.00), -0.425 (0.01), -0.125 (0.02), 0.200 (0.03), 0.500 (0.04), 0.725 (0.05)].

[0048] S4. Extract features from the fused pressure sequence to obtain a staged pressure feature set.

[0049] It should be explained that the feature extraction of the fused pressure sequence to obtain the staged pressure feature set includes: Based on the fused pressure sequence, an inspiratory pressure sequence, a breath-holding pressure sequence, and an expiratory pressure sequence are obtained; Inspiratory peak pressure characteristics were obtained based on the inspiratory pressure value sequence; Based on the inspiratory pressure sequence, the number of inspiratory pressures, multiple inspiratory pressure values, and multiple inspiratory times were identified, with each inspiratory pressure value corresponding to an inspiratory time. The inspiratory pressure rise characteristic is calculated using inspiratory pressure counts, multiple inspiratory pressure values, and multiple inspiratory times. The formula for calculating the inspiratory pressure rise characteristic is shown below: in, This indicates a rise in inspiratory pressure. Indicates the quantity of inhalation pressure. Indicates the first of multiple inspiratory times One inhalation time, This represents the first of multiple inspiratory pressure values. One inhalation pressure value; By summarizing the inspiratory peak pressure characteristics and inspiratory pressure rise characteristics, an inspiratory phase feature set is obtained; The breath-holding stage feature set is obtained based on the breath-holding pressure sequence, which includes breath-holding duration, average breath-holding pressure and standard deviation of breath-holding pressure. Calculation of expiratory pressure drop characteristics based on expiratory pressure sequences; By summarizing the inspiratory phase feature set, the breath-holding phase feature set, and the expiratory pressure decrease feature set, a phased pressure feature set is obtained.

[0050] Furthermore, obtaining the inspiratory pressure sequence, breath-holding pressure sequence, and expiratory pressure sequence based on the fused pressure sequence refers to segmenting the fused pressure sequence to obtain these sequences. The method for obtaining these sequences using the fused pressure sequence is the same as the method for obtaining the inspiratory phase pressure sequence, breath-holding phase pressure sequence, and expiratory phase pressure sequence using the standardized pressure sequence, and will not be repeated here. The inspiratory pressure sequence is the sequence of pressures extracted from the fused pressure sequence corresponding to the inspiratory phase of the Valsalva maneuver. The breath-holding pressure sequence is the sequence of pressures extracted from the fused pressure sequence corresponding to the breath-holding phase of the Valsalva maneuver. The expiratory pressure sequence is the sequence of pressures extracted from the fused pressure sequence corresponding to the expiratory phase of the Valsalva maneuver. The peak inspiratory pressure characteristic refers to the maximum pressure value in the inspiratory pressure sequence. The number of inspiratory pressures refers to the number of elements contained in the inspiratory pressure sequence. The inspiratory pressure value refers to the numerical value (pressure) of the pressure in the inspiratory pressure sequence. The inspiratory time refers to the time point at which the pressure in the inspiratory pressure sequence is collected. The inspiratory pressure rise characteristic refers to the slope of the straight line obtained by fitting an inspiratory pressure sequence (with time on the x-axis and pressure value on the y-axis), used to characterize the average rate of pressure rise during the inspiratory phase. The inspiratory phase feature set is a collection that includes peak inspiratory pressure characteristics and inspiratory pressure rise characteristics. The breath-holding phase feature set is a collection that includes breath-holding duration, mean breath-holding pressure, and standard deviation of breath-holding pressure.

[0051] Understandably, breath-hold duration refers to the length of time between the time point corresponding to the acquisition of the pressure signal for the first element in the breath-hold pressure sequence and the time point corresponding to the acquisition of the pressure signal for the last element in the breath-hold pressure sequence. For example, if the breath-hold pressure sequence contains multiple elements from 5.20 seconds to 7.80 seconds, then the breath-hold duration is 2.60 seconds. The average breath-hold pressure is the arithmetic mean of all pressures in the breath-hold pressure sequence. The standard deviation of the breath-hold pressure is the standard deviation of all pressures in the breath-hold pressure sequence. The expiratory pressure drop characteristic is a numerical value used to characterize the average rate of pressure drop during the expiratory phase. The method for obtaining the expiratory pressure drop characteristic using the expiratory pressure sequence is the same as the method for calculating the inspiratory pressure rise characteristic using the number of inspiratory pressures, multiple inspiratory pressure values, and multiple inspiratory times, and will not be repeated here. The phased pressure feature set is a collection that includes the inspiratory phase feature set, the breath-hold phase feature set, and the expiratory pressure drop characteristic.

[0052] S5. Obtain the sequence of action quality indicators using the phased pressure feature set.

[0053] It should be explained that the process of obtaining the action quality index sequence using a phased pressure feature set includes: The first quality index is calculated based on the peak inhalation pressure characteristics in the aforementioned staged pressure feature set and the preset inhalation target pressure. The second quality index is calculated based on the inhalation pressure rise characteristics in the aforementioned staged pressure feature set and the preset reference rise slope. A third quality index is obtained based on the average breath-holding pressure, standard deviation of breath-holding pressure, and preset ideal breath-holding pressure range of the aforementioned stage-specific pressure characteristic set. The fourth quality index is calculated based on the breath-holding duration of the aforementioned stage-specific pressure characteristic set and the preset shortest effective breath-holding duration. The fifth quality index is calculated based on the expiratory pressure drop characteristics in the aforementioned staged pressure feature set and the preset reference drop slope. The first, second, third, fourth, and fifth quality indicators are arranged in a preset physiological order to obtain a sequence of action quality indicators.

[0054] Furthermore, the inspiratory target pressure is a parameter used to calculate the first quality index, for example, 0.7. The first quality index is a numerical value used to quantify the degree of matching between the peak inspiratory pressure characteristic and the inspiratory target pressure, wherein the formula for calculating the first quality index is as follows: in, This indicates the primary quality indicator. This indicates taking the minimum value. Indicates peak inspiratory pressure characteristics. This indicates the target inhalation pressure.

[0055] Understandably, the reference rise slope is the ideal rate of pressure rise during the inhalation phase of a standard Warburg maneuver, for example, 2. The second quality index is a quantitative indicator of the inhalation velocity during the inhalation phase of the Warburg maneuver, calculated using the inhalation pressure rise characteristics and the reference rise slope. The formula for calculating the second quality index is as follows: in, This indicates the second quality indicator. This indicates a rise in inspiratory pressure. This indicates the reference rising slope.

[0056] Furthermore, obtaining a third quality index based on the average breath-holding pressure, the standard deviation of breath-holding pressure, and the preset ideal breath-holding pressure range from the aforementioned staged pressure characteristic set refers to using the average breath-holding pressure and the ideal breath-holding pressure range (e.g., The interval compliance is calculated, and the third quality index is calculated using the interval compliance, the average breath-hold pressure, and the standard deviation of the breath-hold pressure. The formula for calculating the interval compliance is as follows: in, Indicates the degree of agreement between intervals. This indicates the average pressure during breath-holding. This represents the ideal air pressure range. This indicates that it does not belong to [the group / organization]. It indicates that it belongs to.

[0057] Understandably, the formula for calculating the third quality indicator is as follows: in, This represents the third quality indicator. This represents the standard deviation of breath-holding pressure.

[0058] Furthermore, the ideal breath-holding pressure range is the ideal pressure range during the breath-holding phase of a standard Valsalva maneuver, for example, The third quality indicator is a quantitative measure of stability during the breath-holding phase.

[0059] Understandably, the shortest effective breath-holding time is the shortest breath-holding time required to achieve one effective Valsalva maneuver, for example, 10 seconds. The fourth quality metric is a quantitative indicator of breath-holding time calculated using the breath-holding duration and the shortest effective breath-holding time. The formula for calculating the fourth quality metric is as follows: in, This represents the fourth quality indicator. This indicates taking the absolute value. Indicates the duration of breath-holding. This indicates the shortest effective breath-holding time.

[0060] Furthermore, the reference descent slope is the ideal rate of pressure decrease during the expiratory phase of a standard Valverde maneuver, for example, -1.5. The fifth quality index is a quantitative indicator of the speed of exhalation during the expiratory phase of the Valverde maneuver, calculated using the expiratory pressure decrease characteristics and the reference descent slope. The formula for calculating the fifth quality index is as follows: in, This represents the fifth quality indicator. This indicates a decrease in expiratory pressure. This indicates the reference descent slope.

[0061] Understandably, the physiological sequence follows the order of the phases of the Valsalva maneuver, for example, inhalation phase - breath-holding phase - exhalation phase. The sequence of action quality indicators refers to the sequence obtained by arranging the first, second, third, fourth, and fifth quality indicators in the physiological order, for example, [first quality indicator, second quality indicator, third quality indicator, fourth quality indicator, fifth quality indicator].

[0062] S6. Perform pattern recognition matching between the action quality index sequence and the pre-confirmed standard template sequence to obtain the standard action similarity.

[0063] It should be explained that the step of performing pattern recognition matching between the action quality index sequence and the pre-confirmed standard template sequence to obtain the standard action similarity includes: The difference value vector is obtained using the action quality index sequence and the standard template sequence; The total distance value is calculated using the difference vector and the pre-confirmed dimension weight vector; The standard action similarity is calculated based on the total distance value, and the formula for calculating the standard action similarity is as follows: in, Indicates the similarity of standard movements. This represents the natural exponential function. This indicates the preset scaling factor. This represents the total distance value.

[0064] Furthermore, the standard template sequence is a sequence of movement quality indicators generated when a subject performs a standard Valsalva maneuver under the guidance of clinical skill operation guidelines. For example, [1.0, 1.0, 1.0, 1.0, 1.0], where each element in the standard template sequence corresponds one-to-one with each element in the movement quality indicator sequence. The difference vector is a vector composed of the absolute values ​​of the differences obtained by subtracting multiple elements in the standard template sequence from multiple elements in the movement quality indicator sequence.

[0065] For example, if the action quality index sequence is [0.8, 1.2, 0.9, 0.7, 1.1] and the standard template sequence is [1.0, 1.0, 1.0, 1.0, 1.0], then the difference value vector is [0.2, 0.2, 0.1, 0.3, 0.1].

[0066] Understandably, the dimension weight vector is a vector with the same dimensions as multiple elements (first quality index, second quality index, third quality index, fourth quality index, and fifth quality index) in the action quality index sequence, for example, [0.2, 0.2, 0.3, 0.2, 0.1]. Each element in the dimension weight vector represents the importance of each element in the corresponding action quality index sequence when calculating the total distance value. Optionally, the analytic hierarchy process (AHP) can be used to set the dimension weight vector. The total distance value is a value obtained by calculating the dot product (sum of the products of each element) of the difference value vector and the dimension weight vector, used to quantify the degree of difference between the action quality index sequence and the standard template sequence. The standard action similarity is a value in the range (0-1) obtained from the total distance value calculation, used to represent the similarity between the current initial subject's Valsalva maneuver and the standard Valsalva maneuver. The scaling factor is a parameter used to scale the total distance value to the range (0-1), for example, 5.

[0067] S7. Obtain multimodal action prompts based on the standard action similarity.

[0068] It should be explained that the step of obtaining multimodal action prompts based on the standard action similarity includes: The similarity of standard actions is compared with a preset similarity threshold; If the similarity of the standard action is greater than or equal to the similarity threshold, then the action qualification instruction is obtained and the action qualification instruction is used as a multimodal action prompt instruction. If the similarity of the standard actions is less than the similarity threshold, an abnormal indicator set is identified based on the difference value vector and the preset difference threshold. The abnormal feature set includes one or more abnormal indicators. The set of adjustment schemes is obtained based on the set of abnormal indicators and the preset correction strategy mapping table; The set of adjustment schemes will be used as multimodal action prompts.

[0069] Furthermore, the similarity threshold is a limit value used to determine whether the initial subject's Vascular maneuver is qualified, for example, 0.8. The qualified maneuver instruction is a voice prompt generated when the similarity to the standard maneuver is greater than or equal to the similarity threshold, for example, "Maneuver is qualified." If the similarity to the standard maneuver is less than the similarity threshold, an abnormal indicator set is identified based on the difference value vector and the preset difference threshold. This means comparing multiple difference values ​​in the difference value vector with the difference threshold; if there is a difference value greater than or equal to the difference threshold, the quality indicator corresponding to that difference value is taken as an abnormal indicator, and the abnormal indicator set is obtained by summarizing these abnormal indicators. The abnormal indicator set is a collection containing multiple abnormal indicators. Each difference value in the difference value vector corresponds to a quality indicator (for example, the first quality indicator, the second quality indicator, the third quality indicator, the fourth quality indicator, and the fifth quality indicator). The difference value refers to an element in the difference value vector.

[0070] For example, the difference value vector is [0.2, 0.1, 0.05, 0.25, 0.18], the difference threshold is 0.15, and the abnormal indicator set is {second quality indicator, third quality indicator}.

[0071] Furthermore, the correction strategy mapping table is a table containing multiple abnormal indicators and corresponding adjustment schemes. This correction strategy mapping table is pre-constructed based on clinical and empirical medicine. Optionally, a lookup table method can be used to obtain the adjustment scheme set. The adjustment scheme set includes a collection of multiple adjustment schemes. Each adjustment scheme is a method for correcting an abnormal indicator as described in the correction strategy mapping table. For example, if the abnormal indicator in the abnormal indicator set is the first quality indicator, the corresponding adjustment scheme is to increase the inhalation force; if the abnormal indicator in the abnormal indicator set is the fourth quality indicator, the corresponding adjustment scheme is to extend the breath-holding time to more than 5 seconds. Multimodal action prompts are voice reminders used to guide initial subjects in correcting the Valsalva maneuver.

[0072] To address the problems described in the background art, this invention identifies multiple effective pressure acquisition points based on pre-confirmed initial subjects. This demonstrates that the embodiments of this invention automatically adapt to individual differences in subjects, locating key body surface locations with optimal signal quality, providing a stable and reliable data source for subsequent analysis, and resolving the problem of poor signal quality caused by arbitrary placement of pressure sensors. Next, this invention performs the following operations on each of the multiple effective pressure acquisition points: acquiring pressure signals from the initial subject based on the effective pressure acquisition point to obtain an initial pressure sequence set; summarizing the initial pressure sequence sets to obtain multiple initial pressure sequence sets. This embodiment of the invention obtains multiple initial pressure sequence sets reflecting the pressure distribution in the thoracic and abdominal cavities through multi-point synchronous acquisition of pressure signals, providing a data foundation for assessing the mechanical consistency of the Valsalva maneuver. Furthermore, this invention acquires a second pressure sequence set using a pre-confirmed respiratory flow sensor, fuses the multiple initial pressure sequence sets and the second pressure sequence set to obtain a fused pressure sequence, and extracts features from the fused pressure sequence to obtain a staged pressure feature set. This invention, through the cascaded processing of sequence fusion and feature extraction, eliminates redundancy, improves data consistency, and simultaneously achieves data dimensionality reduction and highlights key features. Furthermore, this invention obtains a sequence of action quality indicators by utilizing a phased stress feature set, performs pattern recognition matching between this sequence and a pre-confirmed standard template sequence to obtain standard action similarity, and generates multimodal action prompts based on this standard action similarity. Thus, this invention transforms stress features into calculable indicators and performs template matching, achieving accurate quantification of action deviation while generating multimodal action prompts. Therefore, this invention can solve the problem of right ventricular angiography failure in subjects due to their inability to autonomously, stably, and accurately perform standardized Valsalva maneuvers, requiring multiple manual interventions.

[0073] like Figure 2 The diagram shown is a functional block diagram of a right ventricular angiography action assist device based on pressure signal feedback provided in an embodiment of the present invention.

[0074] The right ventricular angiography motion assist device 100 based on pressure signal feedback described in this invention can be installed in an electronic device. Depending on the functions implemented, the right ventricular angiography motion assist device 100 based on pressure signal feedback may include a position confirmation module 101, a pressure signal acquisition module 102, a pressure feature extraction module 103, and a motion command feedback module 104. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0075] The acquisition location confirmation module 101 is used to confirm multiple effective pressure acquisition points based on the pre-confirmed initial subject. The pressure signal acquisition module 102 is used to perform the following operation on each of the multiple valid pressure acquisition points: Pressure signals were collected from the initial subjects based on effective pressure acquisition points to obtain an initial pressure sequence set. By summing the initial pressure sequence sets, multiple initial pressure sequence sets are obtained; The pressure feature extraction module 103 is used to acquire a second pressure sequence set using a pre-confirmed respiratory flow sensor; Multiple initial pressure sequence sets and a second pressure sequence set are fused to obtain a fused pressure sequence. Feature extraction is performed on the fused pressure sequence to obtain a staged pressure feature set; The action instruction feedback module 104 is used to obtain an action quality index sequence using a phased pressure feature set. The sequence of action quality indicators is matched with the pre-confirmed standard template sequence by pattern recognition to obtain the standard action similarity. Multimodal action prompts are obtained based on the standard action similarity.

[0076] In detail, the modules in the right ventricular angiography action assist device 100 based on pressure signal feedback described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The technique used is the same as the pressure signal feedback-based right ventricular angiography action assistance method described in the previous article, and can produce the same technical effect, so it will not be repeated here.

[0077] like Figure 3 The diagram shown is a schematic diagram of an electronic device for implementing a pressure signal feedback-based method for assisting right ventricular angiography, according to an embodiment of the present invention.

[0078] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a right ventricular angiography action assistance method program based on pressure signal feedback.

[0079] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as the portable hard drive of the electronic device 1. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a right ventricular angiography action assistance method program based on pressure signal feedback, but also to temporarily store data that has been output or will be output.

[0080] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a right ventricular angiography action assistance method program based on pressure signal feedback) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0081] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0082] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0083] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0084] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0085] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0086] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0087] The right ventricular angiography action assistance method program based on pressure signal feedback stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When run in the processor 10, it can achieve the following: Based on the pre-confirmed initial subjects, multiple effective pressure collection points were identified; Perform the following operation for each of the multiple valid pressure acquisition points: Pressure signals were collected from the initial subjects based on effective pressure acquisition points to obtain an initial pressure sequence set. By summing the initial pressure sequence sets, multiple initial pressure sequence sets are obtained; A second pressure sequence set is obtained using a pre-confirmed respiratory flow sensor; Multiple initial pressure sequence sets and a second pressure sequence set are fused to obtain a fused pressure sequence. Feature extraction is performed on the fused pressure sequence to obtain a staged pressure feature set; Obtain action quality index sequences using phased pressure feature sets; The sequence of action quality indicators is matched with the pre-confirmed standard template sequence by pattern recognition to obtain the standard action similarity. Multimodal action prompts are obtained based on the standard action similarity.

[0088] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0089] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0090] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following: Based on the pre-confirmed initial subjects, multiple effective pressure collection points were identified; Perform the following operation for each of the multiple valid pressure acquisition points: Pressure signals were collected from the initial subjects based on effective pressure acquisition points to obtain an initial pressure sequence set. By summing the initial pressure sequence sets, multiple initial pressure sequence sets are obtained; A second pressure sequence set is obtained using a pre-confirmed respiratory flow sensor; Multiple initial pressure sequence sets and a second pressure sequence set are fused to obtain a fused pressure sequence. Feature extraction is performed on the fused pressure sequence to obtain a staged pressure feature set; Obtain action quality index sequences using phased pressure feature sets; The sequence of action quality indicators is matched with the pre-confirmed standard template sequence by pattern recognition to obtain the standard action similarity. Multimodal action prompts are obtained based on the standard action similarity.

[0091] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0092] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0093] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0094] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0095] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device through software or hardware. The term "second class" is used to indicate names and does not indicate any specific order.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for assisting right ventricular angiography based on pressure signal feedback, characterized in that, The method includes: Based on the pre-confirmed initial subjects, multiple effective pressure collection points were identified; Perform the following operation for each of the multiple valid pressure acquisition points: Pressure signals were collected from the initial subjects based on effective pressure acquisition points to obtain an initial pressure sequence set. By summing the initial pressure sequence sets, multiple initial pressure sequence sets are obtained; A second pressure sequence set is obtained using a pre-confirmed respiratory flow sensor; Multiple initial pressure sequence sets and a second pressure sequence set are fused to obtain a fused pressure sequence. Feature extraction is performed on the fused pressure sequence to obtain a staged pressure feature set; Obtain action quality index sequences using phased pressure feature sets; The sequence of action quality indicators is matched with the pre-confirmed standard template sequence by pattern recognition to obtain the standard action similarity. Multimodal action prompts are obtained based on the standard action similarity.

2. The method for assisting right ventricular angiography based on pressure signal feedback as described in claim 1, characterized in that, The pre-identified initial subjects identified multiple effective pressure collection points, including: Based on the initial subjects, several key body surface locations were identified; Perform the following operation for each of the multiple key body surface locations: Pressure is detected at key body surface locations at a preset pressure acquisition frequency to obtain a test pressure set. A fast Fourier transform is performed on the test pressure set to obtain the pressure signal spectrum; Effective signal energy and noise energy are obtained based on the pressure signal spectrum; Calculate signal quality indicators based on effective signal energy and noise energy; Compare the signal quality index with the preset signal quality threshold. If the signal quality index is greater than or equal to the signal quality threshold, then mark the key body surface location as an effective pressure acquisition point. If the signal quality index is less than the signal quality threshold, then an optimized sampling point is obtained and used as an effective pressure sampling point. By summing up the effective pressure acquisition points, the multiple effective pressure acquisition points are obtained.

3. The method for assisting right ventricular angiography based on pressure signal feedback as described in claim 2, characterized in that, The acquisition of optimized collection points includes: Obtain the neighborhood range of key body surface locations; The neighborhood range is divided into grids according to a preset grid spacing to obtain multiple candidate grid points; Multiple test signal qualities are obtained using multiple candidate grid points, where each test signal quality corresponds one-to-one with a candidate grid point; Each of the plurality of test signal qualities is compared with a signal quality threshold. If among multiple test signal qualities, there is a test signal quality greater than or equal to the signal quality threshold, then the optimal acquisition point is identified; If there is no test signal quality greater than or equal to the signal quality threshold among multiple test signal quality, then the maximum test signal quality is obtained, and the initial sub-advantage is determined based on the maximum test signal quality. Multiple secondary candidate points are obtained by utilizing the initial suboptimal advantage and the preset suboptimal adjustment length; Multiple secondary test signal qualities are obtained using multiple secondary candidate points, where each secondary test signal quality corresponds one-to-one with a secondary candidate point; Extract the maximum secondary test signal quality from the plurality of secondary test signal quality; If the maximum secondary test signal quality is greater than the maximum test signal quality, then the secondary candidate point corresponding to the maximum secondary test signal quality is used as the optimized acquisition point. If the maximum secondary test signal quality is less than or equal to the maximum test signal quality, then the initial secondary advantage point will be used as the optimized acquisition point.

4. The method for assisting right ventricular angiography based on pressure signal feedback as described in claim 3, characterized in that, The initial pressure sequence set is obtained by collecting pressure signals from the initial subject based on effective pressure acquisition points, including: The target sensor is acquired based on the initial subjects and effective pressure acquisition points; The original pressure signal sequence is obtained based on the target sensor and the preset sampling frequency; the baseline drift of the original pressure signal sequence is removed to obtain the corrected signal sequence; The correction signal sequence is filtered to obtain a filtered signal sequence; The filtered signal sequence is normalized to obtain a standardized pressure sequence; The standardized pressure sequence is subjected to phase identification to obtain the inhalation start point, breath-holding start point, and exhalation start point; Based on the inhalation start point, breath-holding start point, and exhalation start point, the standardized pressure sequence is segmented to obtain the inhalation phase pressure sequence, breath-holding phase pressure sequence, and exhalation phase pressure sequence. The pressure sequences during the inhalation phase, breath-holding phase, and exhalation phase are summarized to obtain an initial pressure sequence set.

5. The method for assisting right ventricular angiography based on pressure signal feedback as described in claim 4, characterized in that, The step of performing phase identification on the standardized pressure sequence to obtain the inspiratory initiation point, breath-holding initiation point, and expiratory initiation point includes: The standardized pressure sequence is subjected to first-order difference calculation to obtain the pressure change rate sequence; The pressure change rate sequence was subjected to sliding window peak detection to obtain multiple positive peak points and multiple negative peak points; The multiple positive peak points are filtered based on a preset inhalation amplitude threshold to obtain a candidate positive peak point set, wherein the candidate positive peak point set includes multiple candidate positive peak points; The multiple negative peak points are filtered based on a preset exhalation amplitude threshold to obtain a candidate negative peak point set, wherein the candidate negative peak point set includes multiple candidate negative peak points. The main positive peak point is obtained based on multiple candidate positive peak points in the candidate positive peak point set; The main negative peak point is obtained based on multiple candidate negative peak points in the candidate negative peak point set; The inhalation start point is obtained by performing a reverse search using the main positive peak point, a preset backtracking time window, and a pressure change rate sequence. The breath-holding start point is obtained based on the main positive peak point, the main negative peak point, and the preset stable threshold. The exhalation initiation point is obtained using the main negative peak point, the retrospective time window, and the pressure change rate sequence.

6. The method for assisting right ventricular angiography based on pressure signal feedback as described in claim 5, characterized in that, The feature extraction of the fusion pressure sequence yields a staged pressure feature set, including: Based on the fused pressure sequence, an inspiratory pressure sequence, a breath-holding pressure sequence, and an expiratory pressure sequence are obtained; Inspiratory peak pressure characteristics were obtained based on the inspiratory pressure value sequence; Based on the inspiratory pressure sequence, the number of inspiratory pressures, multiple inspiratory pressure values, and multiple inspiratory times were identified, with each inspiratory pressure value corresponding to an inspiratory time. The inspiratory pressure rise characteristic is calculated using inspiratory pressure counts, multiple inspiratory pressure values, and multiple inspiratory times. The formula for calculating the inspiratory pressure rise characteristic is shown below: in, This indicates a rise in inspiratory pressure. Indicates the quantity of inhalation pressure. Indicates the first of multiple inspiratory times One inhalation time, This represents the first of multiple inspiratory pressure values. One inhalation pressure value; By summarizing the inspiratory peak pressure characteristics and inspiratory pressure rise characteristics, an inspiratory phase feature set is obtained; The breath-holding stage feature set is obtained based on the breath-holding pressure sequence, which includes breath-holding duration, average breath-holding pressure and standard deviation of breath-holding pressure. Calculation of expiratory pressure drop characteristics based on expiratory pressure sequences; By summarizing the inspiratory phase feature set, the breath-holding phase feature set, and the expiratory pressure decrease feature set, a phased pressure feature set is obtained.

7. The method for assisting right ventricular angiography based on pressure signal feedback as described in claim 6, characterized in that, The method of obtaining the action quality index sequence using a phased pressure feature set includes: The first quality index is calculated based on the peak inhalation pressure characteristics in the aforementioned staged pressure feature set and the preset inhalation target pressure. The second quality index is calculated based on the inhalation pressure rise characteristics in the aforementioned staged pressure feature set and the preset reference rise slope. A third quality index is obtained based on the average breath-holding pressure, standard deviation of breath-holding pressure, and preset ideal breath-holding pressure range of the aforementioned stage-specific pressure characteristic set. The fourth quality index is calculated based on the breath-holding duration of the aforementioned stage-specific pressure characteristic set and the preset shortest effective breath-holding duration. The fifth quality index is calculated based on the expiratory pressure drop characteristics in the aforementioned staged pressure feature set and the preset reference drop slope. The first, second, third, fourth, and fifth quality indicators are arranged in a preset physiological order to obtain a sequence of action quality indicators.

8. The method for assisting right ventricular angiography based on pressure signal feedback as described in claim 7, characterized in that, The step of performing pattern recognition matching between the action quality index sequence and the pre-confirmed standard template sequence to obtain standard action similarity includes: The difference value vector is obtained using the action quality index sequence and the standard template sequence; The total distance value is calculated using the difference vector and the pre-confirmed dimension weight vector; The standard action similarity is calculated based on the total distance value, and the formula for calculating the standard action similarity is as follows: in, Indicates the similarity of standard movements. This represents the natural exponential function. This indicates the preset scaling factor. This represents the total distance value.

9. The method for assisting right ventricular angiography based on pressure signal feedback as described in claim 8, characterized in that, The process of obtaining multimodal action prompts based on the standard action similarity includes: The similarity of standard actions is compared with a preset similarity threshold; If the similarity of the standard action is greater than or equal to the similarity threshold, then the action qualification instruction is obtained and the action qualification instruction is used as a multimodal action prompt instruction. If the similarity of the standard actions is less than the similarity threshold, an abnormal indicator set is identified based on the difference value vector and the preset difference threshold. The abnormal feature set includes one or more abnormal indicators. The set of adjustment schemes is obtained based on the set of abnormal indicators and the preset correction strategy mapping table; The set of adjustment schemes will be used as multimodal action prompts.

10. A right ventricular angiography action assist device based on pressure signal feedback, characterized in that, The device includes: The data acquisition location confirmation module is used to identify multiple effective pressure acquisition points based on the pre-confirmed initial subject. The pressure signal acquisition module performs the following operation on each of the multiple valid pressure acquisition points: Pressure signals were collected from the initial subjects based on effective pressure acquisition points to obtain an initial pressure sequence set. By summing the initial pressure sequence sets, multiple initial pressure sequence sets are obtained; A pressure feature extraction module is used to acquire a second pressure sequence set using a pre-confirmed respiratory flow sensor; Multiple initial pressure sequence sets and a second pressure sequence set are fused to obtain a fused pressure sequence. Feature extraction is performed on the fused pressure sequence to obtain a staged pressure feature set; The motion instruction feedback module is used to obtain a sequence of motion quality indicators using a phased pressure feature set; The sequence of action quality indicators is matched with the pre-confirmed standard template sequence by pattern recognition to obtain the standard action similarity. Multimodal action prompts are obtained based on the standard action similarity.

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