IABP-based signal detection system and device

Abstract

The embodiment of the invention provides a signal detection system and device based on IABP, and relates to the technical field of medical instruments.The signal detection device executes the following signal detection method when detecting signal feature points: obtaining an electrocardiosignal to be detected, and converting the electrocardiosignal into an electrocardiogram image; extracting a target waveform feature representing signal form information in the electrocardiogram image, and determining a target electrocardiogram tag to which the electrocardiogram signal belongs based on the target waveform feature; determining a target parameter value matched with the target electrocardiogram tag, and updating a value of a detection parameter item used for detecting a signal feature point in a preset detection strategy to the target parameter value; and detecting signal feature points of the electrocardiosignal according to a preset detection strategy of the updated parameters. By applying the scheme provided by the embodiment of the invention, the signal feature points of the electrocardiosignals can be accurately detected.

Description

Technical Field

[0001] The present application relates to the field of medical device technology, and in particular to a signal detection system and device based on IABP. Background Art

[0002] IABP (Intra-Aortic Balloon Pump) is a medical device commonly used to treat acute heart failure, cardiogenic shock and cardiac pump failure caused by coronary artery disease (such as myocardial infarction). The main function of IABP is to improve cardiac perfusion and reduce cardiac afterload by periodically inflating and deflation of the aorta.

[0003] To ensure that the IABP can effectively support the heart, the timing of its inflation and deflation must be synchronized with the contraction and relaxation of the heart. Specifically, inflation of the IABP usually occurs during diastole, while deflation occurs during systole.

[0004] The detection of signal feature points in the ECG signal is a key factor in the synchronous control of balloon inflation and deflation. By accurately detecting the signal feature points in the ECG signal, the IABP device can inflate and deflate at the right time to ensure the heart receives optimal support. Summary of the invention

[0005] The purpose of the embodiment of the present application is to provide a signal detection system and device based on IABP to accurately detect the signal feature points of the electrocardiogram signal. The specific technical solution is as follows: In a first aspect, an embodiment of the present application provides a signal detection system based on IABP, the system comprising an IABP balloon catheter and a signal detection device, the IABP balloon catheter uses balloon inflation and deflation to unload the heart load, the signal detection device is used to detect signal feature points of an electrocardiogram signal when the IABP balloon catheter is running, wherein the signal detection device performs the following signal detection method when detecting the signal feature points: Acquire an electrocardiogram signal to be detected, and convert the electrocardiogram signal into an electrocardiogram image; Extracting target waveform features representing signal morphology information in the electrocardiogram image, and determining a target electrocardiogram label to which the electrocardiogram signal belongs based on the target waveform features; Determine a target parameter value that matches the target ECG tag, and update the value of the detection parameter item for detecting signal feature points in the preset detection strategy to the target parameter value; According to the preset detection strategy of the updated parameters, the signal feature points of the electrocardiogram signal are detected.

[0006] In one embodiment of the present application, the signal detection device integrates a pre-trained lightweight label classification model; The step of extracting target waveform features representing signal morphology information from the electrocardiogram image and determining a target electrocardiogram label to which the electrocardiogram signal belongs based on the target waveform features includes: Inputting the electrocardiogram image into the label classification model; The label classification model extracts target waveform features representing signal morphology information in the electrocardiogram image, performs electrocardiogram label type matching based on the target waveform features, and outputs an electrocardiogram label; The ECG label output by the label classification model is used as the target ECG label to which the ECG signal belongs.

[0007] In one embodiment of the present application, the above-mentioned determination of the target parameter value matched by the target ECG tag includes: Determining a reference parameter value corresponding to the target ECG tag, wherein the reference parameter value is a detection parameter value for detecting a signal feature point of a standard signal corresponding to the target ECG tag; Based on the reference parameter value, a target parameter value is determined.

[0008] In one embodiment of the present application, determining the target parameter value based on the reference parameter value includes: Calculating a characteristic difference value between the target waveform characteristic and the waveform characteristic of the standard signal; A reference parameter adjustment value corresponding to the characteristic difference value is determined, the reference parameter value is adjusted according to the reference parameter adjustment value, and the adjusted reference parameter value is determined as the target parameter value.

[0009] In a second aspect, an embodiment of the present application provides a signal detection device based on IABP, the device comprising: An image conversion module, used for acquiring an electrocardiogram signal to be detected and converting the electrocardiogram signal into an electrocardiogram image; A label determination module, used for extracting target waveform features representing signal morphology information in the electrocardiogram image, and determining a target electrocardiogram label to which the electrocardiogram signal belongs based on the target waveform features; A parameter updating module, used to determine a target parameter value that matches the target ECG tag, and update the value of the detection parameter item used for detecting signal feature points in the preset detection strategy to the target parameter value; The signal detection module is used to detect the signal feature points of the electrocardiogram signal according to a preset detection strategy of the update parameters.

[0010] In one embodiment of the present application, the above-mentioned device integrates a pre-trained lightweight label classification model; the label determination module is specifically used to input the ECG image into the label classification model; the label classification model extracts the target waveform features representing the signal morphology information in the ECG image, performs ECG label type matching based on the target waveform features, and outputs the ECG label; the ECG label output by the label classification model is used as the target ECG label to which the ECG signal belongs.

[0011] In one embodiment of the present application, the parameter updating module includes: A parameter determination submodule, used to determine a reference parameter value corresponding to the target ECG tag, wherein the reference parameter value is a detection parameter value for detecting a signal feature point of a standard signal corresponding to the target ECG tag; The parameter updating submodule is used to determine the target parameter value based on the reference parameter value, and update the value of the detection parameter item used to detect the signal feature point in the preset detection strategy to the target parameter value.

[0012] In one embodiment of the present application, the above-mentioned parameter updating submodule is specifically used to calculate the characteristic difference value between the target waveform characteristic and the waveform characteristic of the standard signal; determine the benchmark parameter adjustment value corresponding to the characteristic difference value, adjust the benchmark parameter value according to the benchmark parameter adjustment value, and determine the adjusted benchmark parameter value as the target parameter value.

[0013] In a third aspect, an embodiment of the present application provides an electronic medical device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; Memory, used to store computer programs; The processor is used to implement the method steps described in the first aspect when executing the program stored in the memory.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in the first aspect are implemented.

[0015] From the above, it can be seen that by applying the solution provided in the embodiment of the present application, the signal characteristic points of the ECG signal are detected according to the preset detection strategy for updating the parameters, and the updated parameter values ​​are determined dynamically in real time based on the target ECG tag to which the current ECG signal belongs. The target ECG tag represents the signal state of the current ECG signal. Accordingly, the updated parameter values ​​are adaptive to the signal state of the current ECG signal. Therefore, the preset detection strategy for updating the parameters can adapt to the signal state of the current ECG signal and accurately detect the signal characteristic points of the current ECG signal, thereby improving the detection accuracy of the signal characteristic points.

[0016] Of course, implementing any product or method of the present application does not necessarily require achieving all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other embodiments can also be obtained based on these drawings.

[0018] Figure 1 A schematic diagram of the structure of a signal detection system provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of a first signal detection method provided in an embodiment of the present application; Figure 3 A schematic diagram of a flow chart of a second signal detection method provided in an embodiment of the present application; Figure 4 A schematic diagram of a flow chart of a third signal detection method provided in an embodiment of the present application; Figure 5 A schematic diagram of the structure of a signal detection device provided in an embodiment of the present application; Figure 6 A schematic diagram of the structure of an electronic medical device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field based on the present application belong to the scope of protection of the present application.

[0020] Before introducing the embodiments of the present application, first, the signal detection system provided by the present application is described.

[0021] See also Figure 1, Figure 1 This is a schematic diagram of the structure of a signal detection system provided in an embodiment of the present application. The system includes an IABP balloon catheter 11 and a signal detection device 12.

[0022] The IABP balloon catheter 11 is placed in the patient's heart to provide cardiac circulation assistance.

[0023] The signal detection device 12 is used to detect signal feature points of the electrocardiogram signal during the operation of the IABP. The signal feature points generally include a QRS complex, an R wave peak, a P wave, a T wave, and the like.

[0024] The signal detection device 12 is integrated into the IABP host and placed outside the patient's body. The operation of the IABP balloon catheter 11 is controlled by the IABP host, and the processor of the IABP host determines the IABP triggering time according to the signal feature points detected by the signal detection device 12 .

[0025] Since the IABP triggering timing directly determines the operating quality of the IABP balloon catheter 11, if the IABP is triggered at an incorrect triggering time, it will cause serious damage to the patient's life and health. Therefore, accurate detection of signal feature points is very critical.

[0026] When the signal detection device detects the signal characteristic point, it performs the following Figure 2 The method steps of the corresponding embodiment.

[0027] See also Figure 2 , Figure 2 This is a flow chart of a first signal detection method provided in an embodiment of the present application. The method includes the following steps S201-S204.

[0028] Step S201: Acquire the electrocardiogram signal to be detected, and convert the electrocardiogram signal into an electrocardiogram image.

[0029] The above ECG signals are collected in real time. If the ECG lead wire is pre-attached to the patient's skin surface, the signal detection device is connected to the above ECG lead wire, and the currently collected ECG signal can be obtained in real time.

[0030] The acquired ECG signal may be a signal obtained by preprocessing the collected initial ECG signal, such as filtering, standardization, and other preprocessing methods.

[0031] Compared with ECG signals, ECG images are two-dimensional data, which directly and accurately characterize the signal waveform characteristics of ECG signals from the perspective of timing information.

[0032] One implementation method of converting an ECG signal into an ECG image is as follows: using a preset coarse screening threshold to preliminarily screen candidate signal feature points in the ECG signal, the candidate signal feature points representing feature points in the ECG signal that may be signal feature points; setting a preset number of sampling points before and after the candidate signal feature points, and using the patient's cardiac cycle to segment and intercept the set signal points, performing a two-dimensional image conversion on the intercepted ECG signal to obtain an ECG image.

[0033] Step S202: extracting target waveform features representing signal morphology information in the ECG image, and determining a target ECG label to which the ECG signal belongs based on the target waveform features.

[0034] The target waveform features may include signal amplitude, kurtosis, skewness, frequency, etc. The target waveform features may be extracted using a preset feature extraction algorithm.

[0035] The above target ECG labels can be understood as representing the patient's current heart state from the perspective of the patient's ECG signal. The ECG signals of patients with different heart states are different, and the target ECG labels of the ECG signals are also different. Taking the classification standard of the American Association for the Advancement of Medical Instrumentation as an example, there are a total of 15 types of ECG labels for ECG signals, including normal heartbeats, ventricular premature beats, atrial premature beats and other ECG labels.

[0036] One implementation method for determining the target ECG tag is to calculate the similarity between the target waveform feature and the waveform feature corresponding to each preset ECG tag, and use the preset ECG tag corresponding to the highest similarity as the target ECG tag to which the ECG signal belongs.

[0037] Other implementations of determining target waveform characteristics can be found in the following Figure 3 The corresponding embodiments are not described in detail here.

[0038] Step S203: determining a target parameter value that matches the target ECG tag, and updating the value of the detection parameter item used to detect the signal feature point in the preset detection strategy to the target parameter value.

[0039] Target parameter values ​​may include detection thresholds, detection ranges, and the like.

[0040] The above-mentioned preset detection strategy can be any signal feature point detection strategy in the prior art. For example, for the signal feature point R wave, the preset detection strategy includes enhancing the R wave signal by bandpass filtering, differentiating, squaring, and moving integrating the ECG signal in sequence, and detecting the R wave position using the R wave detection threshold. The detection parameter items include parameter items such as the filter cutoff frequency, the moving integration window width, and the differential threshold iteration parameter.

[0041] Since the target parameter value is used as the value of the detection parameter item in the preset detection strategy, that is, the detection value used to detect the signal feature point in the preset detection strategy is updated in real time instead of being fixed.

[0042] One implementation method of determining the target detection parameter value is: presetting the correspondence between the ECG tag and the detection parameter value, and determining the detection parameter value corresponding to the target ECG tag according to the correspondence as the target detection parameter value.

[0043] For other implementations of determining target detection parameter values, see the following Figure 4 The corresponding embodiments are not described in detail here.

[0044] Step S204: Detect signal feature points of the electrocardiogram signal according to the preset detection strategy of the updated parameters.

[0045] From the above, it can be seen that when applying the solution provided in this embodiment, the signal characteristic points of the ECG signal are detected according to the preset detection strategy for updating the parameters, and the updated parameter values ​​are determined dynamically in real time based on the target ECG tag to which the current ECG signal belongs. The target ECG tag represents the signal state of the current ECG signal. Accordingly, the updated parameter values ​​are adaptive to the signal state of the current ECG signal. Then, the preset detection strategy for updating the parameters can adapt to the signal state of the current ECG signal and accurately detect the signal characteristic points of the current ECG signal, thereby improving the detection accuracy of the signal characteristic points.

[0046] The foregoing Figure 2 In a corresponding embodiment, the signal detection device can integrate a pre-trained lightweight label classification model. Since the label classification model is a lightweight architecture model, it can be used on mobile devices and embedded devices, ensuring accuracy while reducing computing resources and memory consumption.

[0047] The above-mentioned label classification model is obtained through pre-training. Specifically, the ECG signals in the existing ECG database are divided into three groups of data: testing, training and verification. The above-mentioned ECG signals are converted into two-dimensional images, and are trained and tested using a lightweight neural network, such as modifying the input shape of the model, performing weighted averaging on the initial convolutional layer, etc. The model after the training is used as the label classification model.

[0048] The above-mentioned lightweight neural network can be MobileNetV2, which is based on key technologies such as deep separable convolution and inverted residual structure, significantly improving computational efficiency while maintaining high accuracy.

[0049] Based on this, the aforementioned step S202 can be implemented according to the following steps S302-S304. Based on this, see Figure 3 , Figure 3The flowchart of the second signal detection method provided in the embodiment of the present application is as follows. The above method includes the following steps S301-S306.

[0050] Step S301: Acquire the ECG signal to be detected, and convert the ECG signal into an ECG image.

[0051] The above step S301 is the same as the above step S201 and will not be described again.

[0052] Step S302: inputting the electrocardiogram image into the label classification model.

[0053] Step S303: The label classification model extracts the target waveform features representing the signal morphology information in the ECG image, performs ECG label type matching based on the target waveform features, and outputs the ECG label.

[0054] The label classification model is pre-trained based on a large amount of training sample data. The label classification model has learned the law of the characteristic relationship between the ECG image and the ECG label. Therefore, when the ECG image is input into the label classification model, the output ECG label can accurately represent the label to which the ECG signal belongs.

[0055] The label classification model can use the deep separable convolution technology, which decomposes the standard convolution into two simpler operations, namely channel-by-channel convolution and main hall convolution, and outputs the ECG label through the above deep separable convolution technology. Compared with the standard convolution, the use of the above two types of convolution significantly reduces the amount of calculation and the number of parameters, realizing the lightweight architecture of the model.

[0056] Step S304: using the ECG label output by the label classification model as the target ECG label to which the ECG signal belongs.

[0057] Step S305: determining a target parameter value that matches the target ECG tag, and updating the value of the detection parameter item used to detect the signal feature point in the preset detection strategy to the target parameter value.

[0058] Step S306: Detect signal feature points of the ECG signal according to the preset detection strategy of the updated parameters.

[0059] The above steps S305-S306 are the same as the above Figure 2 Steps S203 - S204 of the corresponding embodiment are the same and will not be described again.

[0060] The foregoing Figure 2 In the corresponding embodiment, in addition to determining the target parameter value by the aforementioned implementation method, step S203 can also be implemented according to the following steps S403-S404. Figure 4 , Figure 4This is a flow chart of a third signal detection method provided in an embodiment of the present application. The method includes the following steps S401-S405.

[0061] Step S401: Acquire the ECG signal to be detected, and convert the ECG signal into an ECG image.

[0062] Step S402: extracting target waveform features representing signal morphology information in the ECG image, and determining a target ECG label to which the ECG signal belongs based on the target waveform features.

[0063] The above steps S401-S402 are similar to the above steps Figure 2 Steps S201 - S202 of the corresponding embodiment are the same and will not be described again.

[0064] Step S403: Determine the reference parameter value corresponding to the target ECG tag.

[0065] The above-mentioned reference parameter value is a detection parameter value used to detect the signal feature point of the standard signal corresponding to the target ECG tag.

[0066] One implementation method for determining the reference parameter value is: according to the pre-set correspondence between the ECG tag and the standard signal, determine the standard signal corresponding to the target ECG tag, and obtain the detection parameter value of the signal feature point for detecting the standard signal as the reference parameter value.

[0067] In this implementation manner, detection parameter values ​​of detection signal characteristic points corresponding to various types of standard signals may be preset, and the reference parameter values ​​may be determined according to the preset relationship.

[0068] Step S404: Based on the reference parameter value, a target parameter value is determined, and the value of the detection parameter item used to detect the signal feature point in the preset detection strategy is updated to the target parameter value.

[0069] The first implementation method of determining the target parameter value is to directly determine the reference parameter value as the target parameter value.

[0070] The second implementation method for determining the target parameter value is: calculating the characteristic difference value between the target waveform characteristics and the waveform characteristics of the standard signal; determining the benchmark parameter adjustment value corresponding to the characteristic difference value, adjusting the benchmark parameter value according to the benchmark parameter adjustment value, and determining the adjusted benchmark parameter value as the target parameter value.

[0071] The standard signal is the standard signal of the target ECG tag to which the current ECG signal belongs. Even if two signals belong to the same ECG tag, there are still signal differences. Therefore, the above characteristic difference value represents the specific difference between the current ECG signal and the standard signal. Then, the target parameter value adjusted based on the characteristic difference value is more suitable for the signal specific characteristics of the current ECG signal, thereby more accurately detecting the signal feature points of the ECG signal.

[0072] One implementation method of determining the reference parameter adjustment value is: presetting a corresponding relationship between the characteristic difference and the adjustment value, and determining the reference parameter adjustment value corresponding to the characteristic difference value according to the corresponding relationship.

[0073] One implementation of adjusting the reference parameter value is: calculating the sum of the reference parameter adjustment value and the reference parameter value, and determining the calculated sum as the adjusted reference parameter value.

[0074] Step S405: Detect signal feature points of the electrocardiogram signal according to the preset detection strategy of the updated parameters.

[0075] The above step S405 is the same as the above step S204 and will not be described again.

[0076] It can be seen that in this embodiment, since the determined reference parameter value is the detection parameter value corresponding to the standard signal in the target ECG tag, the detection accuracy of the above detection parameter value is relatively high. Therefore, the detection accuracy of the determined reference parameter value is relatively high, thereby achieving accurate detection of the signal feature points of the ECG signal.

[0077] Corresponding to the above-mentioned signal detection system based on IABP, the embodiment of the present application also provides a signal detection device based on IABP.

[0078] See also Figure 5 , Figure 5 A schematic diagram of the structure of a signal detection device provided in an embodiment of the present application, the device comprising: An image conversion module 501 is used to obtain an electrocardiogram signal to be detected and convert the electrocardiogram signal into an electrocardiogram image; The label determination module 502 is used to extract the target waveform features representing the signal morphology information in the electrocardiogram image, and determine the target electrocardiogram label to which the electrocardiogram signal belongs based on the target waveform features; A parameter updating module 503 is used to determine a target parameter value that matches the target ECG tag, and to update the value of a detection parameter item used for detecting signal feature points in a preset detection strategy to the target parameter value; The signal detection module 504 is used to detect the signal feature points of the electrocardiogram signal according to a preset detection strategy of the update parameters.

[0079] From the above, it can be seen that when applying the solution provided in this embodiment, the signal characteristic points of the ECG signal are detected according to the preset detection strategy for updating the parameters, and the updated parameter values ​​are determined dynamically in real time based on the target ECG tag to which the current ECG signal belongs. The target ECG tag represents the signal state of the current ECG signal. Accordingly, the updated parameter values ​​are adaptive to the signal state of the current ECG signal. Then, the preset detection strategy for updating the parameters can adapt to the signal state of the current ECG signal and accurately detect the signal characteristic points of the current ECG signal, thereby improving the detection accuracy of the signal characteristic points.

[0080] In one embodiment of the present application, the above-mentioned device integrates a pre-trained lightweight label classification model; the label determination module 502 is specifically used to input the ECG image into the label classification model; the label classification model extracts the target waveform features representing the signal morphology information in the ECG image, performs ECG label type matching based on the target waveform features, and outputs the ECG label; the ECG label output by the label classification model is used as the target ECG label to which the ECG signal belongs.

[0081] The label classification model is a lightweight model that can be used on mobile devices and embedded devices, ensuring accuracy while reducing computing resources and memory consumption. In addition, the label classification model is pre-trained based on a large amount of training sample data. The label classification model has learned the law of the characteristic relationship between the ECG image and the ECG label. Therefore, when the ECG image is input into the label classification model, the output ECG label can accurately represent the label to which the ECG signal belongs.

[0082] In one embodiment of the present application, the parameter updating module 503 includes: A parameter determination submodule, used to determine a reference parameter value corresponding to the target ECG tag, wherein the reference parameter value is a detection parameter value for detecting a signal feature point of a standard signal corresponding to the target ECG tag; The parameter updating submodule is used to determine the target parameter value based on the reference parameter value, and update the value of the detection parameter item used to detect the signal feature point in the preset detection strategy to the target parameter value.

[0083] It can be seen that in this embodiment, since the determined reference parameter value is the detection parameter value corresponding to the standard signal in the target ECG tag, the detection accuracy of the above detection parameter value is relatively high. Therefore, the detection accuracy of the determined reference parameter value is relatively high, thereby achieving accurate detection of the signal feature points of the ECG signal.

[0084] In one embodiment of the present application, the above-mentioned parameter updating submodule is specifically used to calculate the characteristic difference value between the target waveform characteristic and the waveform characteristic of the standard signal; determine the benchmark parameter adjustment value corresponding to the characteristic difference value, adjust the benchmark parameter value according to the benchmark parameter adjustment value, and determine the adjusted benchmark parameter value as the target parameter value.

[0085] It can be seen that the above characteristic difference value represents the specific difference between the current ECG signal and the standard signal. Then, the target parameter value adjusted based on the characteristic difference value is more suitable for the signal specificity of the current ECG signal, thereby more accurately detecting the signal characteristic points of the ECG signal.

[0086] Corresponding to the above-mentioned IABP-based signal detection system, the present application embodiment provides an electronic medical device, see Figure 6 , Figure 6 A schematic diagram of the structure of an electronic medical device provided in an embodiment of the present application, wherein the electronic medical device comprises a processor 601, a communication interface 602, a memory 603 and a communication bus 604, wherein the processor 601, the communication interface 602 and the memory 603 communicate with each other via the communication bus 604; Memory 603, used for storing computer programs; The processor 601 is used to implement the above signal detection method steps when executing the program stored in the memory 603.

[0087] The communication bus mentioned in the above controller can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0088] The communication interface is used for communication between the above controller and other devices.

[0089] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0090] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0091] In another embodiment provided in the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the signal detection method provided in the embodiment of the present application is implemented.

[0092] In another embodiment provided in the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to implement the above-mentioned signal detection method provided in the embodiment of the present application.

[0093] From the above, it can be seen that when applying the solution provided in this embodiment, the signal characteristic points of the ECG signal are detected according to the preset detection strategy for updating the parameters, and the updated parameter values ​​are determined dynamically in real time based on the target ECG tag to which the current ECG signal belongs. The target ECG tag represents the signal state of the current ECG signal. Accordingly, the updated parameter values ​​are adaptive to the signal state of the current ECG signal. Then, the preset detection strategy for updating the parameters can adapt to the signal state of the current ECG signal and accurately detect the signal characteristic points of the current ECG signal, thereby improving the detection accuracy of the signal characteristic points.

[0094] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0095] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0096] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, electronic medical equipment, and computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0097] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application are included in the protection scope of the present application.

Claims

1. A signal detection system based on IABP, characterized in that: The system includes an IABP balloon catheter and a signal detection device, wherein the IABP balloon catheter unloads the heart load by inflating and deflation of the balloon, and the signal detection device is used to detect the signal characteristic points of the electrocardiogram signal when the IABP balloon catheter is in operation, wherein the signal detection device performs the following signal detection method when detecting the signal characteristic points: Acquire an electrocardiogram signal to be detected, and convert the electrocardiogram signal into an electrocardiogram image; Extracting target waveform features representing signal morphology information in the electrocardiogram image, and determining a target electrocardiogram label to which the electrocardiogram signal belongs based on the target waveform features; Determine a target parameter value that matches the target ECG tag, and update the value of the detection parameter item for detecting signal feature points in the preset detection strategy to the target parameter value; According to the preset detection strategy of the updated parameters, the signal feature points of the electrocardiogram signal are detected.

2. The system according to claim 1, characterized in that The signal detection device integrates a pre-trained lightweight label classification model; The step of extracting target waveform features representing signal morphology information from the electrocardiogram image and determining a target electrocardiogram label to which the electrocardiogram signal belongs based on the target waveform features includes: Inputting the electrocardiogram image into the label classification model; The label classification model extracts target waveform features representing signal morphology information in the electrocardiogram image, performs electrocardiogram label type matching based on the target waveform features, and outputs an electrocardiogram label; The ECG label output by the label classification model is used as the target ECG label to which the ECG signal belongs.

3. The system according to claim 1 or 2, characterized in that: Determining the target parameter value that matches the target ECG tag includes: Determining a reference parameter value corresponding to the target ECG tag, wherein the reference parameter value is a detection parameter value for detecting a signal feature point of a standard signal corresponding to the target ECG tag; Based on the reference parameter value, a target parameter value is determined.

4. The system according to claim 3, characterized in that The step of determining a target parameter value based on the reference parameter value comprises: Calculating a characteristic difference value between the target waveform characteristic and the waveform characteristic of the standard signal; A reference parameter adjustment value corresponding to the characteristic difference value is determined, the reference parameter value is adjusted according to the reference parameter adjustment value, and the adjusted reference parameter value is determined as the target parameter value.

5. A signal detection device based on IABP, characterized in that: The device comprises: An image conversion module, used for acquiring an electrocardiogram signal to be detected and converting the electrocardiogram signal into an electrocardiogram image; A label determination module, used for extracting target waveform features representing signal morphology information in the electrocardiogram image, and determining a target electrocardiogram label to which the electrocardiogram signal belongs based on the target waveform features; A parameter updating module, used to determine a target parameter value that matches the target ECG tag, and update the value of the detection parameter item used for detecting signal feature points in the preset detection strategy to the target parameter value; The signal detection module is used to detect the signal feature points of the electrocardiogram signal according to a preset detection strategy of the update parameters.

6. The device according to claim 5, characterized in that The device integrates a pre-trained lightweight label classification model; the label determination module is specifically used to input the electrocardiogram image into the label classification model; the label classification model extracts the target waveform features representing the signal morphology information in the electrocardiogram image, performs electrocardiogram label type matching based on the target waveform features, and outputs the electrocardiogram label; The ECG label output by the label classification model is used as the target ECG label to which the ECG signal belongs.

7. The device according to claim 5 or 6, characterized in that The parameter updating module comprises: A parameter determination submodule, used to determine a reference parameter value corresponding to the target ECG tag, wherein the reference parameter value is a detection parameter value for detecting a signal feature point of a standard signal corresponding to the target ECG tag; The parameter updating submodule is used to determine the target parameter value based on the reference parameter value, and update the value of the detection parameter item used to detect the signal feature point in the preset detection strategy to the target parameter value.

8. The device according to claim 7, characterized in that The parameter updating submodule is specifically used to calculate the characteristic difference value between the target waveform characteristic and the waveform characteristic of the standard signal; determine the benchmark parameter adjustment value corresponding to the characteristic difference value, adjust the benchmark parameter value according to the benchmark parameter adjustment value, and determine the adjusted benchmark parameter value as the target parameter value.

9. An electronic medical device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing the method steps described in any one of claims 1 to 4 when executing a program stored in a memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps of any one of claims 1 to 4 are implemented.

Images