A method, device, equipment and medium for detecting cardiopulmonary resuscitation parameters

Abstract

This invention relates to the field of medical testing technology, and more particularly to a method, apparatus, device, and medium for detecting cardiopulmonary resuscitation (CPR) parameters. When the CPR device is activated, a system self-test is initiated. If the self-test passes, real-time sampling data during CPR is acquired. The system then determines whether the real-time sampling data meets preset conditions. If it does, the compression depth, compression frequency, and compression-release ratio are calculated based on the data. Furthermore, a CPR parameter detection result corresponding to the target subject is generated based on the compression frequency, compression frequency, and compression-release ratio. Therefore, this application, by determining whether the real-time sampling data meets preset conditions, calculates compression depth, compression frequency, and compression-release ratio, thereby generating CPR parameter detection results. This addresses the problem of low accuracy in existing technologies due to the reliance on single detection parameters.

Description

Technical Field

[0001] This invention relates to the field of medical testing technology, and in particular to a method, apparatus, equipment and medium for detecting cardiopulmonary resuscitation parameters. Background Technology

[0002] Cardiopulmonary resuscitation (CPR) is a crucial part of emergency treatment for cardiac and respiratory arrest. CPR requires specific frequencies and depths of chest compressions; insufficient pressure fails to promote blood circulation, while excessive pressure can cause fractures and further injuries. Therefore, high-quality CPR training is essential, and the quality of assistive device-assisted CPR is paramount.

[0003] Currently, CPR devices on the market detect compression depth directly through a single sensor, without using time tracking or compression count functions. Furthermore, due to the limited range of detection parameters, they cannot accurately match springs of different lengths, resulting in low accuracy in CPR device detection. Summary of the Invention

[0004] Therefore, it is necessary to address the above-mentioned technical problems by providing a method, device, equipment, and medium for detecting cardiopulmonary resuscitation parameters in this invention, in order to solve the problem that the existing technology has only one detection parameter, resulting in a low accuracy of CPR equipment detection.

[0005] A first aspect of this application provides a method for detecting cardiopulmonary resuscitation (CPR) parameters, the method comprising:

[0006] When turning on the CPR equipment, enable system self-test;

[0007] If the system self-test passes, it acquires real-time sampling data when performing cardiopulmonary resuscitation on the target object, wherein the real-time sampling data includes N sampling data.

[0008] Determine whether the real-time sampled data meets preset conditions;

[0009] If the real-time sampling data meets the preset conditions, then the pressing depth, pressing frequency, and pressing release ratio are calculated based on the real-time sampling data.

[0010] Based on the compression depth, compression frequency, and compression-release ratio, a cardiopulmonary resuscitation parameter detection result corresponding to the target object is generated.

[0011] A second aspect of this application provides a cardiopulmonary resuscitation (CPR) parameter detection device, the CPR parameter detection device comprising:

[0012] The self-test module is used to enable system self-test when the CPR device is turned on;

[0013] The acquisition module is used to acquire real-time sampling data when performing cardiopulmonary resuscitation on the target object if the system self-test passes, wherein the real-time sampling data includes N sampling data.

[0014] The judgment module is used to determine whether the real-time sampled data meets preset conditions;

[0015] The calculation module is used to calculate the pressing depth, pressing frequency, and pressing release ratio based on the real-time sampling data if the real-time sampling data meets the preset conditions.

[0016] The generation module is used to generate cardiopulmonary resuscitation parameter detection results corresponding to the target object based on the compression depth, the compression frequency, and the compression-release ratio.

[0017] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the cardiopulmonary resuscitation parameter detection method as described in the first aspect.

[0018] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the cardiopulmonary resuscitation parameter detection method as described in the first aspect.

[0019] In summary, this invention provides a method, apparatus, device, and medium for detecting cardiopulmonary resuscitation (CPR) parameters. When the CPR device is activated, a system self-test is initiated. If the self-test passes, real-time sampling data during CPR is acquired. The system then determines whether the real-time sampling data meets preset conditions. If it does, the compression depth, compression frequency, and compression-release ratio are calculated based on the data. Furthermore, a CPR parameter detection result corresponding to the target object is generated based on the compression frequency, compression frequency, and compression-release ratio. Therefore, this application achieves the calculation of compression depth, compression frequency, and compression-release ratio by determining whether the real-time sampling data meets preset conditions, thereby generating CPR parameter detection results. This addresses the problem of low accuracy in CPR device detection due to the reliance on single detection parameters in existing technologies. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic flowchart of a cardiopulmonary resuscitation parameter detection method provided in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of a cardiopulmonary resuscitation parameter detection device provided in an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0026] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," or "in response to determination." Similarly, the phrase "if determined" or "if matched to [described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once matched to [described condition or event]," or "in response to matched to [described condition or event]."

[0028] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless otherwise specifically emphasized.

[0030] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0031] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0032] See Figure 1 This is a flowchart illustrating a method for detecting cardiopulmonary resuscitation parameters according to an embodiment of the present invention, as shown below. Figure 1 As shown, this method for detecting cardiopulmonary resuscitation parameters can be achieved through the following steps.

[0033] S201: When the CPR device is turned on, enable system self-test.

[0034] In step S201, the CPR equipment system includes an interface display system, a parameter monitoring system, a spring system, a time statistics system, a data processing system, and a control system. Here, the interface display system can be used to display the operating status of the CPR equipment, compression time, tooling depth, compression frequency, compression-release ratio, number of compressions, and real-time infrared data acquisition via a display screen. The parameter monitoring system can be used to monitor real-time sampling data acquired by the infrared sensor. The spring system can be used to provide stable compression pressure and depth. The time statistics system can be used to calculate the cumulative compression time of the system and store and manage the time. The data processing system can be used to collect, process, and analyze data from the parameter monitoring system and the time statistics system. The control system can be used to control the CPR equipment. The system self-test can be used to check the availability of the sensors and memory chip in the CPR equipment system. Here, the memory chip can be a chip capable of storing system data. During the self-test, the CPR device's interface will display the progress, such as "Checking power connection" or "Checking sensor connection." If the self-test shows everything is normal, the CPR device will prompt the user to proceed to the next step, namely step S202, to obtain real-time sampling data during CPR, which will then be processed. If the self-test shows a fault or abnormality, the user needs to troubleshoot and handle the issue according to the instructions in the CPR device's manual. For example, if the interface displays "Sensor connection abnormality," the user needs to check if the sensor connection cable is securely plugged in or damaged.

[0035] In this embodiment of the application, when the CPR device is turned on, a self-test program can promptly detect and handle any potential faults or abnormalities in the CPR device, ensuring that the CPR device can work normally at critical moments, thereby improving the stability and reliability of the CPR device.

[0036] S202: If the system self-test passes, real-time sampling data is obtained when performing cardiopulmonary resuscitation on the target object, wherein the real-time sampling data includes N sampling data.

[0037] In step S202, when the system self-test passes, during CPR operations on the target subject using the CPR device, real-time sampling data is collected using an infrared sensor for medical personnel to reference and adjust the resuscitation operation. The target subject typically refers to a patient or individual requiring CPR to sustain life. These patients may have suffered severe respiratory and / or cardiac dysfunction due to cardiac arrest, drowning, suffocation, or other reasons, requiring immediate emergency measures to restore their vital signs. The real-time sampling data is sensor data output from the infrared sensor in the CPR device, such as heart rate, blood pressure, blood oxygen saturation, and electrocardiogram waveforms. This real-time sampling data includes N sampling data points; for example, N can be equal to 5 or 7, and the specific setting can be determined according to actual circumstances. This application does not impose any limitations on this.

[0038] In one embodiment of the invention, after acquiring real-time sampling data during cardiopulmonary resuscitation (CPR) of the target object, the process includes:

[0039] Based on the pre-calibrated mapping relationship between real-time sampling data and actual spring height, a first actual spring height is obtained, wherein the first actual spring height represents the maximum depth of the spring when it is at rest.

[0040] Specifically, a database or mapping table is pre-established, recording the relationship between different real-time sampling data (such as pressure, displacement, etc.) and the corresponding spring height. This is usually obtained through experimental measurement, i.e., measuring and recording the actual height of the spring under different conditions (such as different pressures or displacements). This application collects and organizes the mapping relationship between pre-calibrated real-time sampling data and actual spring height, and uses regression analysis, machine learning algorithms, and other methods to establish a mapping model based on the collected data. This model should be able to accurately predict the actual height of the spring based on the real-time sampling data. During cardiopulmonary resuscitation (CPR), sensors collect spring-related data in real time, and the real-time collected data is input into the mapping model to obtain the corresponding spring height. In this process, data preprocessing and filtering may need to be considered to reduce the influence of noise and errors. Based on the output of the mapping model, the maximum depth of the spring when stationary is determined, i.e., the first actual spring height, so that the compression depth can be quickly and accurately calculated using the first actual spring height, thereby improving the efficiency and accuracy of CPR parameter detection.

[0041] It should be noted that the mapping relationship between real-time sampling data and actual spring height is established in a non-linear manner (e.g., using a machine learning model), and can also be obtained through other methods; this application does not impose any limitations on this.

[0042] In this embodiment, after the system self-check passes, real-time sampling data during the cardiopulmonary resuscitation (CPR) operation can be acquired. Based on this real-time sampling data, the operation techniques and intensity can be adjusted to improve the success rate of resuscitation. Furthermore, the data can be used for subsequent analysis and summarization, providing strong support for improving CPR techniques.

[0043] S203: Determine whether the real-time sampling data meets the preset conditions.

[0044] In step S203, because patients with different symptoms will have varying data during cardiopulmonary resuscitation (CPR) procedures, and these data are not necessarily fixed parameters, and real-time sampling data may be affected by various factors such as sensor errors and environmental interference, leading to detection errors, abnormal or erroneous data can be filtered out by determining whether the data meets preset conditions, ensuring the accuracy of subsequent analysis. For example, during CPR, if real-time sampling data shows a chest compression rate of 90 compressions / min, while the preset condition is 100-120 compressions / min, then 90 compressions / min is lower than the preset range and does not meet the preset condition. In this case, it is determined that the compression rate is insufficient, and the compression speed is increased to meet the preset condition. Therefore, if the real-time sampling data meets the preset conditions, step S104 is executed, calculating the compression depth, compression rate, and compression-release ratio based on the real-time sampling data. If the real-time sampling data does not meet the preset conditions, the subsequent steps of the CPR parameter detection method are not executed, and real-time sampling data needs to be acquired again.

[0045] In one embodiment of the invention, determining whether real-time sampling data meets preset conditions includes:

[0046] Calculate the amount of data change between the first sampled data and the second sampled data;

[0047] Determine whether the amount of data change exceeds a preset range;

[0048] If the amount of data change exceeds a preset range, then it is determined whether the amount of data change is continuous.

[0049] If the data change is continuous, then the real-time sampled data is determined to meet the preset conditions;

[0050] If the data change is not continuous, it is determined that the real-time sampling data does not meet the preset conditions, and then the real-time sampling data during the cardiopulmonary resuscitation operation on the target object is reacquired.

[0051] Specifically, sampling data refers to data obtained by sampling the compression signal. During CPR on a target, the compression signal is sampled by a sensor. This means that the analog signal is sampled at equal time intervals within a certain time interval to better reproduce the true state of the analog signal. The analog signal is then converted into a digital signal, resulting in at least one set of two adjacent sampling data points: the first sampling data and the second sampling data. The second sampling data is the previous sampling data point preceding the first sampling data. The first and second sampling data points are any two adjacent sampling data points from N sampling data points. During the sampling process, an appropriate sampling rate is used to ensure that complete information of the analog signal is captured. By sampling the compression signal, adjacent sampling values ​​are obtained, thereby determining the current sampling value and the previous sampling value. First, the specific values ​​of the two sampling data points are determined. Then, the difference between the first and second sampling data points is calculated. This difference represents the amount of data change between the two sampling data points. This amount of data change (denoted as ΔX) can be calculated using the following formula: ΔX = X2 - X1, where X2 represents the second sampling data point and X1 represents the first sampling data point. Then, a preset range of change is set (e.g., ±ΔX_threshold). The calculated data change ΔX is compared with the preset range. If |ΔX| > ΔX_threshold, the data change exceeds the preset range. Further analysis is needed to determine if the data change is continuous. A display window containing multiple continuous sampling points can be set to observe whether the data change within the window remains at a similar level or trend. If the data change within the observation window consistently exceeds the preset range, it can be determined as continuous change. If the data change is continuous, the real-time sampling data is deemed to meet preset conditions (e.g., in cardiopulmonary resuscitation, this may indicate a continuous change in a physiological parameter, requiring appropriate resuscitation measures). If the data change is not continuous, the real-time sampling data is deemed not to meet preset conditions, and real-time sampling data needs to be acquired again for further data analysis.

[0052] For example, during cardiopulmonary resuscitation (CPR), we monitored the depth of chest compressions, with a preset depth variation range (ΔX_threshold) of ±2 cm. Below are sampling data from two different time points:

[0053] First sample data (X1): Chest compression depth is 5cm; Second sample data (X2): The next sample, chest compression depth is 8cm. The change in this data is: ΔX = X2 - X1 = 8cm - 5cm = 3cm. We determine whether the change in data exceeds the preset range, i.e., |ΔX| = 3cm > ΔX_threshold = 2cm. Therefore, we determine that the change in data exceeds the preset range, and then we set a display window to contain the next 5 sample data (assuming a sampling frequency of once per second):

[0054] Sampling 3: 8cm (ΔX2 = 8cm - 8cm = 0cm, the same as X2, because it is the next sampling)

[0055] Sample 4: 7.5cm (ΔX3 = 7.5cm - 8cm = -0.5cm)

[0056] Sampling 5: 8cm (ΔX4 = 8cm - 7.5cm = 0.5cm)

[0057] Sample 6: 7.8cm (ΔX5 = 7.8cm - 8cm = -0.2cm)

[0058] Sample 7: 7.7cm (ΔX6 = 7.7cm - 7.8cm = -0.1cm)

[0059] Within the display window, although the data fluctuated, the overall chest compression depth remained between 7.5cm and 8cm, not returning to the initial 5cm level, and the fluctuation range was relatively small (maximum change was 0.5cm), which can be considered a continuous change (despite minor fluctuations, the overall trend remained consistent). Since the data change was continuous (chest compression depth remained at a relatively stable level, but exceeded the preset depth variation range), we can determine that the real-time sampling data meets the preset conditions. In this case, it may mean that the force or frequency of chest compressions needs to be adjusted to ensure the effectiveness of the resuscitation operation. However, if the data change within the display window is not continuous (for example, the data suddenly returns to the initial level or the fluctuation range is very large), then the real-time sampling data will be determined to not meet the preset conditions, and real-time sampling data needs to be acquired again for further analysis and judgment.

[0060] It should be noted that the preset conditions and preset range of variation can be set according to the actual situation, and this application does not impose any restrictions on them.

[0061] In this embodiment, determining whether the real-time sampling data meets the preset conditions is an important step in ensuring the accuracy and reliability of the data. This reduces the interference of sampling errors, helps support subsequent analysis and decision-making, and thus improves the accuracy of subsequent cardiopulmonary resuscitation parameter detection.

[0062] S204: If the real-time sampling data meets the preset conditions, then calculate the pressing depth, pressing frequency and pressing release ratio based on the real-time sampling data.

[0063] In step S204, if the real-time sampling data meets preset conditions, the compression depth, compression frequency, and compression-to-release ratio are calculated based on the real-time sampling data. Compression depth refers to the depth to which the sternum depresses during chest compressions; this depth directly affects the pressure within the chest cavity, thus affecting the effectiveness of cardiac compression and the recovery of blood circulation. Compression frequency refers to the number of chest compressions performed per unit time; this frequency directly affects the effectiveness of the resuscitation operation and the patient's survival rate. The compression-to-release ratio is the ratio of the duration of continuous compressions to the duration of the release action during chest compressions; this ratio is primarily to ensure that the chest cavity has sufficient time to recover after compressions.

[0064] In one embodiment of the invention, calculating the compression depth based on real-time sampling data includes:

[0065] Obtain N sampled data points, and select a first maximum sampled data point from the N sampled data points, wherein the first maximum sampled data point is the maximum value among the N sampled data points;

[0066] Repeat the above steps until a predetermined number of M first maximum sampled data are obtained. Then, select a second maximum sampled data from the M first maximum sampled data, where M is less than N, and the second maximum sampled data is the maximum value among the M first maximum sampled data.

[0067] The second actual spring height is obtained based on the pre-calibrated mapping relationship between real-time sampling data and actual spring height, and the second maximum sampling data;

[0068] The pressing depth is calculated based on the second actual spring height and the first actual spring height.

[0069] Specifically, the smaller the real-time sampling data acquired by infrared sensors, the larger the actual spring height; conversely, the larger the real-time sampling data acquired by infrared sensors, the smaller the actual spring height. Therefore, after starting compressions, N sample data points are acquired. These N sample data points may be real-time measurements of spring compression. The maximum value among the N sample data points is found, which is the first maximum sample data point. This first maximum sample data point is stored, and this step is repeated until a predetermined number M first maximum sample data points are acquired, forming a new sample data set. The maximum value among this new sample data set is found, which is the second maximum sample data point. Then, based on a pre-calibrated mapping relationship between real-time sample data and actual spring height, the second maximum sample data point is converted into the corresponding spring height, i.e., the second actual spring height. This mapping relationship may be obtained through experiments or calibration, for example, by measuring the spring height corresponding to different sample data points. Then, the compression depth is quickly and accurately calculated based on the first actual spring height (the highest height of the spring when it is at rest) and the second actual spring height, i.e., compression depth = first actual spring height - second actual spring height. This allows the rescuer or therapist to receive real-time feedback, thereby adjusting the compression pressure and improving the overall treatment effect.

[0070] For example, if 20 sample data points are collected, namely [5, 12, 8, 15, 3, 10, 7, 14, 2, 11, 1, 4, 6, 9, 11, 13, 16, 20, 31, 18], and the maximum value is selected as 31, this step is repeated to collect another 20 sample data points for maximum value selection. Since the predetermined number is 10, the 10 first maximum sample data points are [10, 15, 8, 12, 18, 16, 20, 31, 5, 13]. These 10 values ​​form the first maximum sample data set: {10, 15, 8, 12, 18, 16, 20, 31, 5, 13}. The largest value, 31, is found from the set {10, 15, 8, 12, 18, 16, 20, 31, 5, 13}. Assuming the spring height corresponding to the second maximum sampled data 31 is H2 (obtained through mapping), and the highest height of the spring when it is at rest is H1, then the pressing depth = H1 - H2.

[0071] In one embodiment of the invention, calculating the press frequency and press-release ratio based on real-time sampling data includes:

[0072] Select the third maximum sample data and the minimum sample data from the N sample data, wherein the third maximum sample data is the maximum value among the N sample data, and the minimum sample data is the minimum value among the N sample data;

[0073] Determine whether the absolute difference between the third maximum sampled data and the minimum sampled data is greater than a preset threshold;

[0074] If the absolute difference between the third maximum sampled data and the minimum sampled data is greater than a preset threshold, then the median is calculated based on the third maximum sampled data and the minimum sampled data.

[0075] If the real-time sampled data is greater than the median value, it is determined that the spring is in a descending state when pressed.

[0076] If the real-time sampled data is less than the median value, it is determined that the spring is in an upward state when pressed.

[0077] The pressing time corresponding to the rising state and the pressing time corresponding to the falling state are obtained through a preset timing program;

[0078] Calculate the pressing frequency and pressing-release ratio based on the pressing time corresponding to the rising state and the pressing time corresponding to the falling state.

[0079] Specifically, after compressions begin, N sample data points are acquired. These N sample data points may be real-time measurements of spring compression. A third maximum and a minimum sample data point are selected from these N sample data points, where the third maximum sample data point is the maximum value among the N sample data points, and the minimum sample data point is the minimum value among the N sample data points. The absolute difference between the third maximum and minimum sample data points is calculated, and it is determined whether the absolute difference is greater than a preset threshold. If the absolute difference is not greater than the threshold, subsequent steps of the CPR parameter detection method are not performed. If the absolute difference is greater than the threshold, the median of the third maximum and minimum sample data points is calculated. The system compares real-time sampled data with the median. If the real-time sampled data is greater than the median, the spring is determined to be in a descending state during compression; if the real-time sampled data is less than the median, the spring is determined to be in an ascending state during compression. Each time the data exceeds the median, the number of compressions is incremented. A timer at the millisecond level is initialized to start counting after compressions begin, recording the compression time corresponding to the ascending state and the compression time corresponding to the descending state. Based on these compression times, the compression frequency and compression-to-release ratio are quickly and accurately calculated, thus improving the overall effectiveness of CPR. Compression frequency = total number of compressions / total time (number of compressions completed per unit time), and compression-to-release ratio = compression time / (compression time + release time), where compression time and release time refer to the compression times corresponding to the ascending and descending states, respectively.

[0080] For example, if there are 50 sampled data: [5, 12, 8, 15, 3, 10, 7, 14, 2, 11, 1, 4, 6, 9, 11, 13, 16, 20, 32, 17, 41, 54, 68, 199, 100, 66, 77, 61, 24, 28, 35, 46, 18, 121, 29, 38, 132, 57, 62, 33, 72, 84, 99, 81, 39, 91, 88, 44, 59, 76], select the smallest sampled data: 1, the third largest sampled data: 199, the absolute difference = |199 - 1| = 198, if the preset threshold is 150, then 198>150, which satisfies the condition, the median = (199+ 1) / 2 = 100. If the real-time sampling data is 130, then 130>100, and the spring is in the descending state. If the real-time sampling data is 31, then 31<100, and the spring is in the ascending state. If the pressing time corresponding to the ascending state is recorded as T1 and the pressing time corresponding to the descending state is recorded as T2 through the timing program, and the total number of pressing times is Z, then the pressing frequency = Z / (T1 + T2) (the total time is the sum of T1 and T2), and the pressing-release ratio = T2 / (T1 + T2) (T1 is the pressing time, and T2 is the release time).

[0081] In this embodiment, when the real-time sampling data meets the preset conditions, the compression depth, compression frequency, and compression-release ratio can be quickly and accurately calculated based on the real-time sampling data. This can help us to understand the actual situation in the cardiopulmonary resuscitation process more accurately and adjust the resuscitation operation in a timely manner to ensure the best effect.

[0082] S205: Generate cardiopulmonary resuscitation parameter detection results corresponding to the target object based on the compression depth, the compression frequency, and the compression-release ratio.

[0083] In step S205, the cardiopulmonary resuscitation (CPR) parameter detection results refer to the detection results of various parameters during CPR operations on the target subject, which solves the problem of relying on a single detection parameter. Specifically, after obtaining the compression depth, compression frequency, and compression-release ratio, CPR parameter detection results corresponding to the target subject are generated based on these parameters. This means obtaining preset standard depth ranges, preset standard frequency ranges, and preset standard release ratio ranges. The compression depth is compared with the preset standard depth range to determine if it falls within the preset standard depth range; the compression frequency is compared with the preset standard frequency range to determine if it falls within the preset standard frequency range; and the compression-release ratio is compared with the preset standard release ratio range to determine if it falls within the preset standard release ratio range. Then, the compression quality is evaluated based on the depth comparison results, frequency comparison results, and release ratio comparison results, and the parameters such as compression depth, compression frequency, compression-release ratio, compression pressure, and compression quality are determined as the CPR parameter detection results.

[0084] In one embodiment of the invention, before generating the cardiopulmonary resuscitation parameter detection results corresponding to the target object based on the compression frequency, compression rate, and compression-release ratio, the method includes:

[0085] When the CPR device is in the compression state, the compression time when performing cardiopulmonary resuscitation on the target object is acquired.

[0086] Determine whether the duration of the pressing time meets a preset duration threshold;

[0087] If the duration of the compression time meets a preset duration threshold, then the step of generating cardiopulmonary resuscitation parameter detection results corresponding to the target object based on the compression depth, the compression frequency, and the compression-release ratio is executed.

[0088] Specifically, in order to ensure that the CPR device accurately records the compression time, a time statistics system is provided, which can be a timer or a sensor, and this application does not make any limitation on it. After obtaining the compression pressure for performing CPR on the target object, a preset pressure threshold is retrieved from the database or client. This preset pressure threshold is then compared with the compression pressure collected during CPR to obtain a pressure comparison result. If the pressure comparison result indicates that the compression pressure is greater than or equal to the preset pressure threshold, the CPR device is determined to be in compression mode. If the pressure comparison result indicates that the compression pressure is less than the preset pressure threshold, the CPR device is determined to be out of compression mode. When the CPR device is in compression mode, the total time from the start to the end of compression is automatically recorded to obtain the compression time. A compression time threshold is preset (e.g., the duration of CPR is 30 minutes). The duration of compression is then checked to see if it meets the preset duration threshold. If the duration of compression meets the preset duration threshold, the step of generating CPR parameter detection results corresponding to the target object based on compression depth, compression frequency, and compression-release ratio continues. If the duration of compression does not meet the preset duration threshold, the subsequent steps of the CPR parameter detection method are not executed. For example, if the preset duration threshold is 5 minutes, and the emergency responder continuously performs chest compressions for 6 minutes, then the duration of compressions meets the preset duration threshold. Through the above steps, the system detects whether chest compressions are being performed and whether the preset duration threshold is met, ensuring that subsequent parameter checks are conducted while chest compressions are in progress and the preset duration threshold is met.

[0089] It should be noted that the preset pressure threshold and preset duration threshold can be set according to the actual situation, and this application does not impose any restrictions on them.

[0090] In this embodiment, the compression frequency, compression-release ratio, and compression-release ratio are used to generate the cardiopulmonary resuscitation (CPR) parameter detection results, reducing errors caused by improper operation or misjudgment, thereby improving the efficiency and accuracy of CPR parameter detection.

[0091] In summary, this invention provides a method, apparatus, device, and medium for detecting cardiopulmonary resuscitation (CPR) parameters. When the CPR device is activated, a system self-test is initiated. If the self-test passes, real-time sampling data during CPR is acquired. The system then determines whether the real-time sampling data meets preset conditions. If it does, the compression depth, compression frequency, and compression-release ratio are calculated based on the data. Furthermore, a CPR parameter detection result corresponding to the target object is generated based on the compression frequency, compression frequency, and compression-release ratio. Therefore, this application achieves the calculation of compression depth, compression frequency, and compression-release ratio by determining whether the real-time sampling data meets preset conditions, thereby generating CPR parameter detection results. This addresses the problem of low accuracy in CPR device detection due to the reliance on single detection parameters in existing technologies.

[0092] Please see Figure 2 , Figure 2 This is a schematic diagram of the cardiopulmonary resuscitation parameter detection device provided in an embodiment of the present invention. In this embodiment, the terminal includes units used for execution... Figure 1 The steps in the corresponding embodiments. Please refer to the details. Figure 1 as well as Figure 1 The relevant descriptions in the corresponding embodiments are shown below. For ease of explanation, only the parts relevant to this embodiment are shown. See also... Figure 2 The cardiopulmonary resuscitation parameter detection device 20 includes: a self-test module 21, an acquisition module 22, a judgment module 23, a calculation module 24, and a generation module 25.

[0093] Self-test module 21 is used to enable system self-test when the CPR device is turned on;

[0094] The acquisition module 22 is used to acquire real-time sampling data when performing cardiopulmonary resuscitation on the target object if the system self-test passes, wherein the real-time sampling data includes N sampling data.

[0095] The judgment module 23 is used to determine whether the real-time sampling data meets the preset conditions;

[0096] The calculation module 24 is used to calculate the pressing depth, pressing frequency and pressing release ratio based on the real-time sampling data if the real-time sampling data meets the preset conditions.

[0097] The generation module 25 is used to generate cardiopulmonary resuscitation parameter detection results corresponding to the target object based on the compression depth, the compression frequency and the compression-release ratio.

[0098] Optionally, the acquisition module 22 described above is specifically used for:

[0099] Based on the pre-calibrated mapping relationship between real-time sampling data and actual spring height, a first actual spring height is obtained, wherein the first actual spring height represents the maximum depth of the spring when it is at rest.

[0100] Optionally, the real-time sampling data includes first sampling data and second sampling data, where the second sampling data is the preceding sampling data located before the first sampling data, and the first sampling data and the second sampling data are any two adjacent sampling data of the N sampling data. The aforementioned judgment module 23 is specifically used for:

[0101] Calculate the amount of data change between the first sampled data and the second sampled data;

[0102] Determine whether the amount of data change exceeds a preset range;

[0103] If the amount of data change exceeds a preset range, then it is determined whether the amount of data change is continuous.

[0104] If the data change is continuous, then the real-time sampled data is determined to meet the preset conditions;

[0105] If the data change is not continuous, it is determined that the real-time sampling data does not meet the preset conditions, and then the real-time sampling data during the cardiopulmonary resuscitation operation on the target object is reacquired.

[0106] Optionally, the above-mentioned calculation module 24 is specifically used for:

[0107] Obtain N sampled data points, and select a first maximum sampled data point from the N sampled data points, wherein the first maximum sampled data point is the maximum value among the N sampled data points;

[0108] Repeat the above steps until a predetermined number of M first maximum sampled data are obtained. Then, select a second maximum sampled data from the M first maximum sampled data, where M is less than N, and the second maximum sampled data is the maximum value among the M first maximum sampled data.

[0109] The second actual spring height is obtained based on the pre-calibrated mapping relationship between real-time sampling data and actual spring height, and the second maximum sampling data;

[0110] The pressing depth is calculated based on the second actual spring height and the first actual spring height.

[0111] Optionally, the above-mentioned calculation module 24 is also used for:

[0112] Select the third maximum sample data and the minimum sample data from the N sample data, wherein the third maximum sample data is the maximum value among the N sample data, and the minimum sample data is the minimum value among the N sample data;

[0113] Determine whether the absolute difference between the third maximum sampled data and the minimum sampled data is greater than a preset threshold;

[0114] If the absolute difference between the third maximum sampled data and the minimum sampled data is greater than a preset threshold, then the median is calculated based on the third maximum sampled data and the minimum sampled data.

[0115] If the real-time sampled data is greater than the median value, it is determined that the spring is in a descending state when pressed.

[0116] If the real-time sampled data is less than the median value, it is determined that the spring is in an upward state when pressed.

[0117] The pressing time corresponding to the rising state and the pressing time corresponding to the falling state are obtained through a preset timing program;

[0118] Calculate the pressing frequency and pressing-release ratio based on the pressing time corresponding to the rising state and the pressing time corresponding to the falling state.

[0119] Optionally, the aforementioned generation module 25 is specifically used for:

[0120] When the CPR device is in the compression state, the compression time when performing cardiopulmonary resuscitation on the target object is acquired.

[0121] Determine whether the duration of the pressing time meets a preset duration threshold;

[0122] If the duration of the compression time meets a preset duration threshold, then the step of generating cardiopulmonary resuscitation parameter detection results corresponding to the target object based on the compression depth, the compression frequency, and the compression-release ratio is executed.

[0123] It should be noted that the information interaction and execution process between the above-mentioned units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0124] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Figure 3 As shown, the computer device of this embodiment includes: at least one processor ( Figure 3Only one is shown in the diagram), a memory, and a computer program stored in the memory and capable of running on at least one processor, wherein the processor executes the computer program to implement the steps in any of the above embodiments of the cardiopulmonary resuscitation parameter detection method.

[0125] This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 3 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. Computer devices may include more or fewer components than shown in the illustration, or combinations of certain components, or different components, such as network interfaces, displays, and input systems.

[0126] In one embodiment, a computer-readable storage medium is provided that, when the instructions in the computer-readable storage medium are executed by a processor in a computer device, enables the computer device to perform the steps of any embodiment of the cardiopulmonary resuscitation parameter detection method disclosed in this invention, which will not be repeated here. The computer-readable storage medium may be non-volatile or volatile.

[0127] The processor referred to can be a CPU, but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0128] Memory includes readable storage media, internal memory, etc., wherein internal memory can be the RAM of a computer device, providing an environment for the operation of the operating system and computer-readable instructions stored in the readable storage media. The readable storage media can be the hard drive of a computer device, or in other embodiments, it can be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, memory can include both internal storage units and external storage devices of the computer device. Memory is used to store the operating system, applications, bootloader, data, and other programs, such as program code for computer programs. Memory can also be used to temporarily store data that has been output or will be output.

[0129] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0130] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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 as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0131] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for detecting cardiopulmonary resuscitation parameters, characterized in that, include: When turning on the CPR equipment, enable system self-test; If the system self-test passes, it acquires real-time sampling data when performing cardiopulmonary resuscitation on the target object, wherein the real-time sampling data includes N sampling data. Determine whether the real-time sampled data meets preset conditions; If the real-time sampling data meets the preset conditions, then the pressing depth, pressing frequency, and pressing release ratio are calculated based on the real-time sampling data. Based on the compression depth, the compression frequency, and the compression-release ratio, cardiopulmonary resuscitation parameter detection results corresponding to the target object are generated; The step of acquiring real-time sampling data during cardiopulmonary resuscitation of the target object includes: Based on the pre-calibrated mapping relationship between real-time sampling data and actual spring height, a first actual spring height is obtained, wherein the first actual spring height represents the maximum depth of the spring when it is at rest. The calculation of compression depth, compression frequency, and compression-release ratio based on the real-time sampling data includes: Obtain N sampled data points, and select a first maximum sampled data point from the N sampled data points, wherein the first maximum sampled data point is the maximum value among the N sampled data points; Repeat the above steps until a predetermined number of M first maximum sampled data are obtained. Then, select a second maximum sampled data from the M first maximum sampled data, where M is less than N, and the second maximum sampled data is the maximum value among the M first maximum sampled data. The second actual spring height is obtained based on the pre-calibrated mapping relationship between real-time sampling data and actual spring height, and the second maximum sampling data; Calculate the pressing depth based on the second actual spring height and the first actual spring height; Select the third maximum sample data and the minimum sample data from the N sample data, wherein the third maximum sample data is the maximum value among the N sample data, and the minimum sample data is the minimum value among the N sample data; Determine whether the absolute difference between the third maximum sampled data and the minimum sampled data is greater than a preset threshold; If the absolute difference between the third maximum sampled data and the minimum sampled data is greater than a preset threshold, then the median is calculated based on the third maximum sampled data and the minimum sampled data. If the real-time sampled data is greater than the median value, it is determined that the spring is in a descending state when pressed. If the real-time sampled data is less than the median value, it is determined that the spring is in an upward state when pressed. The pressing time corresponding to the rising state and the pressing time corresponding to the falling state are obtained through a preset timing program; Calculate the pressing frequency and pressing-release ratio based on the pressing time corresponding to the rising state and the pressing time corresponding to the falling state.

2. The method for detecting cardiopulmonary resuscitation parameters as described in claim 1, characterized in that, The real-time sampling data includes first sampling data and second sampling data, where the second sampling data is the preceding sampling data located before the first sampling data. The first sampling data and the second sampling data are any two adjacent sampling data from the N sampling data. Determining whether the real-time sampling data meets preset conditions includes: Calculate the amount of data change between the first sampled data and the second sampled data; Determine whether the amount of data change exceeds a preset range; If the amount of data change exceeds a preset range, then it is determined whether the amount of data change is continuous. If the data change is continuous, then the real-time sampled data is determined to meet the preset conditions; If the data change is not continuous, it is determined that the real-time sampling data does not meet the preset conditions, and then the real-time sampling data during the cardiopulmonary resuscitation operation on the target object is reacquired.

3. The method for detecting cardiopulmonary resuscitation parameters as described in claim 1, characterized in that, Before generating the cardiopulmonary resuscitation parameter detection results corresponding to the target object based on the compression depth, the compression frequency, and the compression-release ratio, the process includes: When the CPR device is in the compression state, the compression time when performing cardiopulmonary resuscitation on the target object is acquired. Determine whether the duration of the pressing time meets a preset duration threshold; If the duration of the compression time meets a preset duration threshold, then the step of generating cardiopulmonary resuscitation parameter detection results corresponding to the target object based on the compression depth, the compression frequency, and the compression-release ratio is executed.

4. A cardiopulmonary resuscitation parameter detection device, characterized in that, include: The self-test module is used to enable system self-test when the CPR device is turned on; The acquisition module is used to acquire real-time sampling data when performing cardiopulmonary resuscitation on the target object if the system self-test passes, wherein the real-time sampling data includes N sampling data. The judgment module is used to determine whether the real-time sampled data meets preset conditions; The calculation module is used to calculate the pressing depth, pressing frequency, and pressing release ratio based on the real-time sampling data if the real-time sampling data meets the preset conditions. A generation module is used to generate cardiopulmonary resuscitation parameter detection results corresponding to the target object based on the compression depth, the compression frequency, and the compression-release ratio; The step of acquiring real-time sampling data during cardiopulmonary resuscitation of the target object includes: Based on the pre-calibrated mapping relationship between real-time sampling data and actual spring height, a first actual spring height is obtained, wherein the first actual spring height represents the maximum depth of the spring when it is at rest. The calculation of compression depth, compression frequency, and compression-release ratio based on the real-time sampling data includes: Obtain N sampled data points, and select a first maximum sampled data point from the N sampled data points, wherein the first maximum sampled data point is the maximum value among the N sampled data points; Repeat the above steps until a predetermined number of M first maximum sampled data are obtained. Then, select a second maximum sampled data from the M first maximum sampled data, where M is less than N, and the second maximum sampled data is the maximum value among the M first maximum sampled data. The second actual spring height is obtained based on the pre-calibrated mapping relationship between real-time sampling data and actual spring height, and the second maximum sampling data; Calculate the pressing depth based on the second actual spring height and the first actual spring height; Select the third maximum sample data and the minimum sample data from the N sample data, wherein the third maximum sample data is the maximum value among the N sample data, and the minimum sample data is the minimum value among the N sample data; Determine whether the absolute difference between the third maximum sampled data and the minimum sampled data is greater than a preset threshold; If the absolute difference between the third maximum sampled data and the minimum sampled data is greater than a preset threshold, then the median is calculated based on the third maximum sampled data and the minimum sampled data. If the real-time sampled data is greater than the median value, it is determined that the spring is in a descending state when pressed. If the real-time sampled data is less than the median value, it is determined that the spring is in an upward state when pressed. The pressing time corresponding to the rising state and the pressing time corresponding to the falling state are obtained through a preset timing program; Calculate the pressing frequency and pressing-release ratio based on the pressing time corresponding to the rising state and the pressing time corresponding to the falling state.

5. The cardiopulmonary resuscitation parameter detection device as described in claim 4, characterized in that, The real-time sampling data includes first sampling data and second sampling data, wherein the second sampling data is the preceding sampling data located before the first sampling data, and the first sampling data and the second sampling data are any two adjacent sampling data of the N sampling data. The judgment module is further configured to: Calculate the amount of data change between the first sampled data and the second sampled data; Determine whether the amount of data change exceeds a preset range; If the amount of data change exceeds a preset range, determine whether the amount of data change is a continuous change. If the data change is continuous, then the real-time sampled data is determined to meet the preset conditions; If the data change is not continuous, it is determined that the real-time sampling data does not meet the preset conditions, and then the real-time sampling data during the cardiopulmonary resuscitation operation on the target object is reacquired.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the cardiopulmonary resuscitation parameter detection method as described in any one of claims 1 to 3.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the cardiopulmonary resuscitation parameter detection method as described in any one of claims 1 to 3.

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