Signal detection method and system for IABP device and IABP device

Abstract

The application provides a signal detection method and system for an IABP device and the IABP device, relates to the field of IABP device control, and adopts a differential threshold method to obtain a rising edge in a target detection signal, and on this basis, uses an integral window to perform integral verification on the rising edge to obtain an effective rising edge in the target detection signal, accurately obtains a heart beat rising edge in the target detection signal through a two-level fusion strategy, and can freely select an electrocardiosignal or a blood pressure signal as the target detection signal through a signal source selection switch, so that flexible switching of the two is realized. The signal detection method provided by the application can detect a heart beat signal in real time and accurately, so that the inflation and deflation of the IABP is accurately synchronized with a heart cycle.

Description

Technical Field

[0001] This invention relates to the field of IABP device control, and in particular to a signal detection method, system, and IABP device for use in IABP devices. Background Technology

[0002] IABP (Intra-aortic Balloon Pump) is a mechanical circulatory assist device that uses electrocardiogram (ECG) or arterial pressure (AP) signals to identify the duration of the cardiac cycle, the location of diastole and systole, and thus determine the timing of inflation and deflation. After the onset of diastole (when the aortic valve closes), the balloon inflates to increase coronary perfusion; before the onset of systole, the balloon deflates rapidly to reduce cardiac load, decrease cardiac output, and reduce myocardial oxygen consumption.

[0003] According to the real-time requirements of IABP, the rising edge of the QRS wave in the ECG signal or the rising edge of the systolic pressure in the blood pressure signal is needed to determine the actual heartbeat, thereby enabling rapid and accurate control of the IABP's inflation and deflation timing. Therefore, accurately acquiring the signal rising edge is crucial for improving the control effect of IABP equipment. However, existing rising edge detection algorithms have poor accuracy and only target a single signal, failing to achieve flexible switching between ECG and blood pressure signals. The single differential threshold method is sensitive to the rising edge but has poor accuracy; the traditional Pan-Tompkins method has high accuracy but requires point-by-point serial differentiation (differentiation)-squaring-continuous sliding integration, determining the heartbeat only once at the maximum of the integral envelope corresponding to the R-wave peak. Even at its fastest, it can only determine whether it is a true QRS wave at the end of the QRS wave, resulting in a significant delay, making it unsuitable for direct use in IABP equipment. Summary of the Invention In view of this, the purpose of the present invention is to provide a signal detection method, system and IABP device for IABP device. The method uses differential thresholding to obtain the rising edge in the target detection signal, and on this basis, uses an integral window to perform integral verification on the rising edge to obtain the effective rising edge in the target detection signal. The method accurately obtains the heartbeat rising edge in the target detection signal through a two-level fusion strategy, and can freely select the ECG signal or blood pressure signal as the target detection signal through a signal source selection switch, realizing flexible switching between the two.

[0004] In a first aspect, embodiments of the present invention provide a signal detection method for an IABP device, the method comprising: The target detection signal is obtained from the ECG and blood pressure signals collected in real time by the target IABP device. The heart beat cycle is determined according to the time interval between adjacent heart beats in the target detection signal, and the sliding integral window corresponding to the target detection signal is obtained. The differential signal between adjacent sampling points in the target detection signal is calculated using a preset sampling interval. After calculating the differential threshold based on the peak value of the differential signal, the number of sampling points that meet the differential threshold is counted, and the rising edge in the target detection signal is determined using the sampling point count. After acquiring the differential square signal at the sampling point where the rising edge is located, the integral signal of the differential square signal within the sliding integral window is calculated using the sampling interval, and the effective rising edge in the target detection signal is determined based on the integration threshold of the integral signal. The signal detection result of the target IABP device is determined by the effective rising edge.

[0005] Optionally, the target detection signal can be obtained from the ECG and blood pressure signals collected in real time by the target IABP device, including: Based on the selection result of the signal source selection switch in the target IABP device, a target signal selection instruction is generated, and the electrocardiogram signal or blood pressure signal corresponding to the target signal selection instruction is used as the signal to be processed. When the signal to be processed is an electrocardiogram (ECG) signal, the first cutoff frequency of the first bandpass filter is set by the baseline offset value and electromyographic noise value of the ECG signal. The first cutoff frequency is used to control the first bandpass filter to filter the ECG signal collected in real time by the target IABP device. The resulting processed ECG signal is then used as the target detection signal. When the signal to be processed is a blood pressure signal, the second cutoff frequency of the second bandpass filter is set by the hemodynamic fluctuation value of the blood pressure signal, and the second cutoff frequency is used to control the second bandpass filter to filter the blood pressure signal collected in real time by the target IABP device, and the resulting blood pressure processed signal is used as the target detection signal.

[0006] Optionally, a target signal selection command is generated based on the selection result of the signal source selection switch in the target IABP device, and the ECG signal or blood pressure signal corresponding to the target signal selection command is used as the signal to be processed, including: When the selection result of the signal source selection switch corresponds to the automatic mode selection result, a signal quality calculation strategy corresponding to the automatic mode selection result is generated. Based on the signal quality calculation strategy, the effective heartbeat detection rate, noise energy ratio, spurious wave false detection ratio, and waveform amplitude characteristics of the original ECG signal and blood pressure signal are used to calculate the ECG signal quality value and blood pressure signal quality value, respectively. The maximum signal quality value between the ECG signal quality value and the blood pressure signal quality value is taken as the target signal, and the corresponding target signal selection instruction is obtained based on the target signal; The ECG or blood pressure signal corresponding to the target signal selection command is used as the signal to be processed.

[0007] Optionally, the differential signal between adjacent sampling points in the target detection signal is calculated using a preset sampling interval, and a differential threshold is calculated based on the peak value of the differential signal, including: The first and second signals between adjacent sampling points in the target detection signal are acquired according to a preset sampling interval. After performing a first-order difference operation on the first and second signals by sampling interval, the difference signal between adjacent sampling points is obtained. The differential threshold for each heartbeat cycle is calculated using the current differential peak values ​​of all differential signals in each heartbeat cycle; the differential threshold is calculated using the following formula: ; This is the first differential threshold corresponding to the current heartbeat cycle; This is the second differential threshold corresponding to the previous heartbeat cycle; This represents the current differential peak value in the differential signal during the current heartbeat cycle. and These are the first weight coefficient and the second weight coefficient, respectively, satisfying... .

[0008] Optionally, the number of sampling points that satisfy the difference threshold is counted, and the rising edge in the target detection signal is determined using the sampling point count, including: Obtain all target sampling points contained in the target detection signal within each heartbeat cycle, and determine the target differential signal of each target sampling point; When the number of sampling points whose target differential signal under the current target sampling point is not less than the differential threshold meets the preset counting threshold condition, and the distance between the current target sampling point and the previous heartbeat cycle is greater than the refractory period of the target IABP device, the target time corresponding to the current target sampling point is obtained. The rising edge in the target detection signal is determined based on the target time.

[0009] Optionally, after acquiring the differential squared signal at the sampling point where the rising edge occurs, the integral signal of the differential squared signal within the sliding integration window is calculated using the sampling interval, including: Obtain the current target sampling point corresponding to the rising edge, and obtain the differential squared signal at the sampling point based on the squared result of the target differential signal at the current target sampling point; Starting from the current target sampling point, obtain the time period corresponding to the sliding integral window before the starting point in the target detection signal, and obtain each sampling point within the time period; The integral signal of the differential square signal within the sliding integral window is calculated using the sampling interval and each sampling point.

[0010] Optionally, the process of obtaining the integration threshold of the integral signal includes: The integration threshold for each heartbeat cycle is calculated using the current integration peak value of all integral signals in each heartbeat cycle; the integration threshold is calculated using the following formula: ; This is the first integral threshold corresponding to the current heartbeat cycle; The second integral threshold corresponding to the previous heartbeat cycle; This represents the current integrated peak value in the integrated signal during the current heartbeat cycle. and These are the first weight coefficient and the second weight coefficient, respectively, satisfying... .

[0011] Optionally, the effective rising edge in the target detection signal is determined based on the integration threshold of the integrated signal, including: Obtain all target sampling points contained in the target detection signal within each heartbeat cycle, and determine the target integral signal of each target sampling point; When the target integral signal at the current target sampling point is detected to be no less than the integration threshold, and the rising slope of the target integral signal is no less than 0.5 times the differential signal corresponding to the current target sampling point, the candidate time corresponding to the current target sampling point is obtained. The effective rising edge in the target detection signal is determined based on the candidate time.

[0012] In a second aspect, the present invention provides a signal detection system for an IABP device, the system comprising: Initialization module: used to obtain the target detection signal from the ECG and blood pressure signals collected in real time by the target IABP device, determine the heart beat cycle according to the time interval between adjacent heart beats in the target detection signal, and obtain the sliding integral window corresponding to the target detection signal; Rising edge determination module: It is used to calculate the differential signal between adjacent sampling points in the target detection signal using a preset sampling interval, calculate the differential threshold based on the peak value of the differential signal, count the sampling points that meet the differential threshold, and use the sampling point count to determine the rising edge in the target detection signal; Effective rising edge determination module: After acquiring the differential square signal at the sampling point where the rising edge is located, it calculates the integral signal of the differential square signal within the sliding integral window using the sampling interval, and determines the effective rising edge in the target detection signal based on the integration threshold of the integral signal. Signal detection result determination module: used to determine the signal detection result of the target IABP device through the effective rising edge.

[0013] Thirdly, the present invention also provides an IABP device, which includes a signal acquisition module, a signal processing and detection module, an inflation / deflation timing calculation module, an balloon counterpulsation module, a user interface module, and a controller; wherein the controller is connected to the signal acquisition module, the signal processing and detection module, the inflation / deflation timing calculation module, the balloon counterpulsation module, and the user interface module respectively. The controller includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, and the processor executing the computer-executable instructions to implement the steps of the signal detection method for an IABP device provided in the first aspect.

[0014] This invention provides a signal detection method, system, and IABP device for use in IABP devices. In detecting rising edges in the ECG and blood pressure signals of an IABP device, the method first obtains a target detection signal based on the real-time ECG and blood pressure signals acquired by the target IABP device. The heartbeat period is determined based on the time interval between adjacent heartbeats in the target detection signal, and a sliding integral window corresponding to the target detection signal is obtained. Then, the target detection signal is obtained again based on the real-time ECG and blood pressure signals acquired by the target IABP device. The heartbeat period is determined based on the time interval between adjacent heartbeats in the target detection signal, and a sliding integral window corresponding to the target detection signal is obtained. Subsequently, after obtaining the differential square signal at the sampling point where the rising edge occurs, the integral signal of the differential square signal within the sliding integral window is calculated using the sampling interval. The effective rising edge in the target detection signal is determined based on the integration threshold of the integral signal. Finally, the signal detection result of the target IABP device is determined based on the effective rising edge. This method employs a differential thresholding method to obtain the rising edge of the target detection signal. Based on this, an integral window is used to verify the rising edge through integration, yielding the valid rising edge in the target detection signal. A two-stage fusion strategy accurately obtains the heartbeat rising edge from the target detection signal. Furthermore, an ECG or blood pressure signal can be freely selected as the target detection signal via a signal source selection switch, enabling flexible switching between the two. The signal detection method provided by this invention can detect heartbeat signals in real time and accurately, thereby ensuring precise synchronization between the inflation and deflation of the IABP and the cardiac cycle, improving cardiac perfusion and reducing cardiac afterload.

[0015] Other features and advantages of the invention will be set forth in the following description, and some advantages will become apparent from the description or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A flowchart of a signal detection method for an IABP device provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the relationship between central circulatory cycle and arterial blood pressure in a signal detection method for an IABP device provided by an embodiment of the present invention. Figure 3 A flowchart of another signal detection method for an IABP device provided in an embodiment of the present invention; Figure 4 This invention provides a signal detection method for an IABP device, where the QRS wave has a positive main wave direction, and is a graph of IABP electrocardiogram signal data. Figure 5 This is a graph of IABP electrocardiogram data with a negative main wave direction of the QRS wave processed in a signal detection method for an IABP device provided in an embodiment of the present invention. Figure 6 This invention provides a method for signal detection in an IABP device, which uses an atrial fibrillation signal to process an IABP electrocardiogram data curve. Figure 7 This is a schematic diagram of a signal detection system for an IABP device provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of an IABP device provided in an embodiment of the present invention; Figure 9 This is a display effect diagram of the user interface module in an IABP device when the signal source is ECG, provided as an embodiment of the present invention; Figure 10This is a display effect diagram of the user interface module in an IABP device where the signal source is an AP, provided by an embodiment of the present invention; Figure 11 This is a schematic diagram of the controller in an IABP device provided in an embodiment of the present invention.

[0019] icon: 100 - Initialization module; 200 - Rising edge determination module; 300 - Valid rising edge determination module; 400 - Signal detection result determination module; 10 - Signal acquisition module; 20 - Signal processing and detection module; 30 - Inflation / deflation timing calculation module; 40 - Balloon counterpulsation module; 50 - User interface module; 60 - Controller; 101 - Processor; 102 - Memory; 103 - Bus; 104 - Communication interface. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. 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.

[0021] To facilitate understanding of this embodiment, a signal detection method for an IABP device disclosed in this embodiment will first be described in detail, such as... Figure 1 As shown, the method includes: Step S101: Based on the ECG and blood pressure signals collected in real time by the target IABP device, obtain the target detection signal, determine the heartbeat cycle according to the time interval between adjacent heartbeats in the target detection signal, and obtain the sliding integral window corresponding to the target detection signal.

[0022] Based on the ECG and arterial blood pressure signals collected in real time by the target IABP device, the target detection signal is determined through a configurable signal source selection mechanism, targeted signal preprocessing is completed, and the corresponding heart rate calculation rules and sliding integral window parameters are matched according to the type of the target detection signal.

[0023] The specific implementation process is as follows: The IABP device synchronously and continuously acquires two raw physiological signals: one is the surface electrocardiogram (ECG) signal, and the other is the invasive arterial pressure (AP) signal. The device can manually specify a single channel as the target detection signal through the signal source selection switch, or it can turn on the automatic discrimination mode. The algorithm automatically compares the quality of the two signals and selects the one with a higher signal-to-noise ratio and less waveform interference as the target detection signal used in this operation.

[0024] For the selected target detection signal, adjacent heartbeat identification is performed: ECG signal identification identifies the peak value of the QRS wave main peak, and AP blood pressure signal identification identifies the peak value of systolic pressure. The time interval between two effective peak values ​​of adjacent heartbeats is calculated and defined as the current real-time heartbeat cycle. This cycle represents the duration of a single complete heartbeat and is used for subsequent threshold adaptation, integral window parameter matching, and inflation / deflation timing calculation.

[0025] Based on the type of target detection signal (ECG / blood pressure), configure Tompkins sliding square integral windows of different lengths: ECG signals are adapted to short integral windows to match the duration of the QRS wave; blood pressure signals are adapted to longer integral windows to match the duration of the rising edge of arterial systolic pressure. This completes the initialization of the basic parameters of the integral window and provides a computing platform for the subsequent integral verification process.

[0026] Step S102: Calculate the differential signal between adjacent sampling points in the target detection signal using a preset sampling interval. Calculate the differential threshold based on the peak value of the differential signal, count the number of sampling points that meet the differential threshold, and use the sampling point count to determine the rising edge in the target detection signal.

[0027] Calculate the first-order difference signal point by point and extract the waveform slope feature: Based on a fixed sampling interval preset in the hardware, the difference between adjacent sampling points is calculated on the original target detection signal after filtering and preprocessing to generate a differential signal. The positive peak of the differential signal corresponds to the rising edge position of the waveform that rises rapidly. The steepness of the waveform change at each point is quantified by the differential value to achieve preliminary extraction of rising edge features.

[0028] Adaptive dynamic calculation of differential threshold: continuously count all positive peak values ​​of differential signals within the current heartbeat cycle, and adaptively update the differential judgment threshold based on the peak amplitude; at the same time, set a fixed differential threshold in combination with the number of signal sampling points to constrain the continuous rise length of the rising edge and filter out single-point instantaneous noise spikes.

[0029] Threshold screening and marking of rising edge candidate points: Traverse all differential sampling points. When the differential amplitude of consecutive sampling points continuously exceeds the differential judgment threshold, and the number of consecutive qualified sampling points meets the relevant counting threshold requirements, mark that time point as a rising edge. Simultaneously, a physiological refractory period constraint is superimposed. If the interval between the current candidate point and the previously confirmed heartbeat is less than the fixed refractory period, the candidate point is directly eliminated to avoid false rising edge detections caused by T waves, F waves, and tubing noise. This step only completes the coarse screening; the rising edges include some false detection points caused by noise and spurious waves, requiring further integration verification in the next stage.

[0030] Step S103: After acquiring the differential square signal at the sampling point where the rising edge is located, calculate the integral signal of the differential square signal within the sliding integral window using the sampling interval, and determine the effective rising edge in the target detection signal based on the integration threshold of the integral signal.

[0031] The differential signal corresponding to the rising edge is squared to enhance it: the first-order differential signal values ​​corresponding to all rising edges are extracted and squared to generate a differential squared signal. The squared operation can unify the positive and negative fluctuation differential signal into a positive amplitude, nonlinearly amplify the energy of the steep real rising edge, and at the same time reduce the amplitude of the smooth P wave, T wave, low-frequency baseline drift and small random noise, thus widening the characteristic difference between the real heartbeat waveform and the interference waveform.

[0032] Sliding window integration operation generates an integral signal: taking the rising edge sampling point as the end point of the window, a sliding integration window of a preset length is truncated forward. The time corresponding to each sampling point in the window is converted by combining the hardware sampling interval. All differential square signals in the window are accumulated, summed, and normalized and averaged to obtain the Tompkins moving square integral signal. The integration operation can smooth scattered noise, accumulate the energy characteristics of continuous rising edges, and distinguish isolated noise spikes from the real heartbeat waveform.

[0033] Adaptive generation of integration thresholds and verification of valid rising edges: The peak value of the integral signal within the current heartbeat cycle is statistically analyzed, and the integration judgment threshold is dynamically updated. At the same time, the integration slope auxiliary judgment condition is set. The amplitude of the integral signal at the position corresponding to the rising edge is compared with the integration threshold. If the integral peak value meets the standard and the rising slope of the integral waveform meets the preset ratio requirement, the rising edge is determined to be a valid rising edge corresponding to the real heartbeat. Candidate rising edges that do not meet the threshold conditions are directly eliminated to eliminate false detection results caused by electromyographic interference, atrial fibrillation sawtooth waves, and tubing bubble noise.

[0034] Step S104: Determine the signal detection result of the target IABP device through the effective rising edge.

[0035] Locking in a precise heartbeat synchronization reference point: using the effective rising edge as the standard timing start point for a single cardiac cycle: in ECG mode, the effective rising edge of the QRS wave is used as the synchronization reference, and in AP mode, the effective rising edge of the systolic pressure is used as the synchronization reference.

[0036] Output complete signal detection results: Based on the time coordinate of the effective rising edge and the real-time heartbeat cycle, complete detection data is output synchronously, including: the precise start time of each real heartbeat, the total duration of the cardiac cycle, the systolic interval, and the diastolic interval. This set of detection data is directly sent to the IABP inflation and deflation timing calculation module to accurately calculate the triggering timing of balloon inflation and deflation, drive the balloon to move synchronously with the human cardiac cycle, and achieve the clinical auxiliary effect of diastolic inflation to increase coronary perfusion and systolic early deflation to reduce cardiac afterload.

[0037] The entire detection process employs differentiated filtering and differentiated integration window parameters for ECG and AP signals. The signal source can be manually switched or automatically selected for optimal switching at any time. The two signals share the same set of differential + integral verification two-level detection logic, which solves the shortcomings of traditional algorithms that only support a single signal, have insufficient rising edge detection accuracy, and are prone to missed detection and false detection under arrhythmia / strong interference.

[0038] Specifically, the relationship between cardiac cycle and arterial blood pressure is as follows: Figure 2 As shown, Figure 2 This study demonstrates the dynamic changes in aortic pressure during a single complete cardiac cycle under normal sinus rhythm, as well as the temporal correspondence between different cardiac phases, valve opening and closing actions, and pressure waveforms. This provides a physiological basis for detecting the rising edge of the heartbeat and calibrating the inflation and deflation timing of the intraaortic balloon counterpulsation (IABP) device of this invention.

[0039] Specifically, the heart rate in the graph is 75 beats per minute (75 bpm), which corresponds to a single complete cardiac cycle duration of 0.8 seconds (60s / 75=0.8s).

[0040] The total duration of the cardiac cycle is 0.8s, which is divided into two major phases: ventricular systole (0.30s) and ventricular diastole (0.50s), and further subdivided into 7 phases.

[0041] Whole-heart diastole: 0.40s, refers to the time period during which both the atria and ventricles are in diastole.

[0042] The horizontal axis corresponds to the seven sub-phases of the cardiac cycle, as follows: 1. Ventricular systole (total duration 0.30s) – Ventricular contraction and ejection of blood into the aorta.

[0043] ①Isovolumetric contraction phase (0.05s): The ventricle begins to contract, and the intraventricular pressure rises rapidly. At this time, the mitral valve is closed and the aortic valve has not yet opened. The ventricular volume remains unchanged, but the pressure rises sharply.

[0044] ② Rapid ejection phase (0.10s): When the intraventricular pressure exceeds the aortic pressure, the aortic valve opens, and blood is ejected into the aorta at high speed, causing the aortic pressure to rise rapidly to its peak value.

[0045] ③ Slowed ejection phase (0.15s): Ventricular contractility weakens, ejection velocity slows down, and aortic pressure gradually decreases after reaching its peak.

[0046] 2. Ventricular diastole (total duration 0.50s) – the ventricles relax and receive blood from the atria.

[0047] ④Isovolumetric relaxation (0.08s): The ventricles begin to relax, the aortic valve closes, the mitral valve has not yet opened, the ventricular volume remains unchanged, and the pressure drops rapidly.

[0048] ⑤ Rapid filling phase (0.12s): The intraventricular pressure is lower than the atrial pressure, the mitral valve opens, and blood rapidly flows from the atrium into the ventricle.

[0049] ⑥ Slowed filling period (0.20s): The amount of blood filling the ventricle increases, the pressure difference between the atria and ventricles decreases, and the rate of blood filling slows down.

[0050] ⑦ Atrial systole (0.10s): The atria actively contract, squeezing the remaining blood into the ventricles, completing the final filling of the ventricles.

[0051] The four green vertical lines in the diagram correspond to four key valve action points, which are also the core physiological benchmarks for IABP inflation and deflation control: Mitral valve closure: At the onset of isovolumetric contraction, the ventricles begin to contract, and the atrioventricular valves close to prevent blood from flowing back into the atria.

[0052] Aortic valve opening: At the end of the isovolumetric contraction phase → at the beginning of the rapid ejection phase, the intraventricular pressure exceeds the aortic pressure, the semilunar valve opens, and blood enters the aorta.

[0053] Aortic valve closure: The process begins with a slowed ejection phase at the end of the ejection phase, followed by the onset of isovolumetric relaxation. As ejection ends and the ventricles begin diastole, the aortic valve closes. This is the optimal time to inflate the IABP balloon – inflating at the beginning of diastole maximizes coronary perfusion pressure.

[0054] Mitral valve opening: at the end of isovolumetric relaxation → at the beginning of rapid filling phase, the intraventricular pressure is lower than the atrial pressure, the atrioventricular valves open, and the ventricle begins to fill.

[0055] The red curve reflects the dynamic changes in intra-aortic pressure during the cardiac cycle: Systolic phase: As blood is ejected from the ventricle, the pressure rises rapidly to its peak, then slowly declines; Diastole: After the aortic valve closes, a small notch appears in the pressure (diploma notch), and then as blood continues to flow to the periphery, the aortic pressure gradually decreases, reaching its lowest value before the next systole.

[0056] IABP pinpoints the starting point of the cardiac cycle by accurately identifying the rising edge of systolic pressure (corresponding to aortic valve opening and the start of ejection); then, by combining this with the real-time duration of the cardiac cycle, it infers the aortic valve closing point (the start of diastole) as the timing for balloon inflation; and deflates the balloon in advance before the next rising edge of systolic pressure arrives, reducing left ventricular ejection resistance.

[0057] The core of the signal detection method for the IABP device in this embodiment is to accurately capture the starting point of the pressure curve, thereby ensuring precise synchronization between balloon inflation / deflation and the cardiac cycle.

[0058] Optionally, the target detection signal is obtained from the ECG and blood pressure signals collected in real time by the target IABP device, including the following steps: Step S201: Generate a target signal selection command based on the selection result of the signal source selection switch in the target IABP device, and use the ECG signal or blood pressure signal corresponding to the target signal selection command as the signal to be processed.

[0059] Based on the gear selection result output by the signal source selection switch configured in the target IABP device, a matching target signal selection command is generated. The aforementioned signal source selection switch includes three selectable gears: ECG signal gear, blood pressure signal gear, and automatic recognition gear, each corresponding to different command logic. If the switch is switched to the manual ECG signal mode, a dedicated ECG selection command is generated, which identifies the ECG signal collected in real time by the device as the signal to be processed. If the switch is switched to the manual blood pressure signal setting, a blood pressure-specific selection command is generated, and the arterial blood pressure signal collected in real time by the device is identified as the signal to be processed. If the switch is switched to the automatic recognition mode, the algorithm simultaneously performs a quantitative evaluation of the signal quality of the real-time collected ECG and blood pressure signals, selects the signal with the best signal-to-noise ratio and waveform integrity, generates a selection instruction for the corresponding signal type, and uses the best signal as the signal to be processed.

[0060] Among them, the core detection target for ECG signals to be processed is the rising edge of the QRS complex, and the core detection target for blood pressure signals to be processed is the rising edge of systolic pressure. The rising edges of both types of waveforms are the reference timing characteristics for the synchronous control of balloon inflation and deflation by the IABP device.

[0061] Step S202: When the signal to be processed is an electrocardiogram (ECG) signal, the first cutoff frequency of the first bandpass filter is set by the baseline offset value and electromyographic noise value of the ECG signal. The first cutoff frequency is used to control the first bandpass filter to filter the ECG signal collected in real time by the target IABP device, and the resulting ECG processed signal is used as the target detection signal.

[0062] When the signal to be processed output in step S201 is an electrocardiogram (ECG) signal, the ECG-specific filtering process is executed: The baseline offset amplitude and electromyographic noise intensity of the current ECG signal are extracted in real time, and the first cutoff frequency of the first bandpass filter is adaptively configured according to the magnitude of the two interference parameters. The first passband frequency of the first bandpass filter can be set to 0.5Hz~15Hz. This frequency band can specifically filter out low-frequency baseline drift interference, 50Hz power frequency interference, and high-frequency electromyographic noise in the electrocardiogram signal, while fully preserving the slope and amplitude characteristics of the QRS complex. The configured first bandpass filter is used to perform filtering operations on the real-time acquired ECG signal to remove various irrelevant interference waveforms and obtain a clean ECG signal with waveform characteristics. The filtered ECG signal is marked as an ECG-type target detection signal and sent to the subsequent differential thresholding process.

[0063] Step S203: When the signal to be processed is a blood pressure signal, the second cutoff frequency of the second bandpass filter is set by the hemodynamic fluctuation value of the blood pressure signal, and the second cutoff frequency is used to control the second bandpass filter to filter the blood pressure signal collected in real time by the target IABP device, and the resulting blood pressure processed signal is used as the target detection signal.

[0064] When the signal to be processed output in step S201 is an arterial blood pressure signal, a blood pressure signal-specific filtering process is executed: The system extracts two types of interference characteristic parameters from the current blood pressure signal in real time: the amplitude of low-frequency hemodynamic fluctuations and the intensity of high-frequency impulse noise. Based on the fluctuation and noise parameters, the system adaptively configures the second cutoff frequency of the second bandpass filter. The second passband frequency of the second bandpass filter can be set to 3Hz~10Hz. This frequency band can suppress low-frequency drift and high-frequency noise interference caused by changes in blood flow in the blood pressure waveform, and preserve the slope change characteristics of the systolic pressure rise edge to the greatest extent. The configured second bandpass filter is used to perform filtering operations on the real-time acquired blood pressure signal to remove distorted and interference waveforms and obtain a blood pressure signal with clear characteristics. The filtered blood pressure processing signal is marked as a target detection signal of the blood pressure type and sent to the subsequent differential thresholding process.

[0065] The above steps establish two independent bandpass filtering systems by differentiating between ECG and blood pressure signals. The filtering frequency bands are customized for the respective interference sources of the two types of physiological signals, which solves the shortcomings of existing algorithms that only adapt to a single signal and do not perform differentiated preprocessing. This provides a high signal-to-noise ratio input signal for the subsequent two-level fusion rising edge detection algorithm, thereby reducing the probability of false detection and false negative detection from the source.

[0066] Optionally, a target signal selection command is generated based on the selection result of the signal source selection switch in the target IABP device, and the ECG signal or blood pressure signal corresponding to the target signal selection command is used as the signal to be processed, including the following steps: Step S301: When the selection result of the signal source selection switch corresponds to the automatic mode selection result, generate the signal quality calculation strategy corresponding to the automatic mode selection result.

[0067] The signal source selection switch configured in the IABP device includes three selectable modes: manual ECG mode, manual blood pressure mode, and automatic recognition mode. When the signal source selection switch outputs an automatic mode selection result, the device's main control module responds to the mode command and generates a dedicated signal quality calculation strategy that adapts to the parallel evaluation of ECG and arterial blood pressure signals in both channels.

[0068] This calculation strategy differs from the single-channel independent processing logic in manual mode. It can simultaneously retrieve two channels of raw physiological waveform data, uniformly define the calculation rules and weighting coefficients of four types of signal quality quantification evaluation indicators, and provide a standardized calculation framework for simultaneous scoring and horizontal comparison of two signals.

[0069] Step S302: Based on the signal quality calculation strategy, calculate the signal quality value of the ECG signal and the signal quality value of the blood pressure signal using the effective heartbeat detection rate, noise energy ratio, spurious wave false detection ratio and waveform amplitude characteristics corresponding to the ECG signal and blood pressure signal, respectively.

[0070] Based on the signal quality calculation strategy output in step S301, a four-dimensional quantitative evaluation is simultaneously performed on the ECG and arterial blood pressure signals acquired in real time by the device, and independent comprehensive quality scores for the two signals are calculated respectively: Effective heartbeat detection rate index: The two-level fusion detection logic with differential threshold + Tompkins integral window verification is used to count the ratio of the number of effective heartbeat rising edges that have passed the integral window verification within a fixed time to the total number of all candidate points; among them, the ECG signal is counted based on the effective rising edge of the QRS complex, and the blood pressure signal is counted based on the effective rising edge of the systolic pressure. The higher the detection rate, the stronger the waveform recognition stability. Noise energy ratio index: The proportion of interference energy of two types of signals to the total signal energy is calculated separately: For electrocardiogram signals, the proportion of baseline drift, 50Hz power frequency interference, and electromyographic noise is calculated; for blood pressure signals, the proportion of low-frequency fluctuations in hemodynamics and high-frequency pulse noise is calculated. The higher the noise ratio, the lower the corresponding signal quality score. The false detection rate index is: the proportion of times that pseudo-waves with no physiological significance, such as P waves, T waves, atrial fibrillation F waves, and blood flow distortion noise, are misjudged as the rising edge of a heartbeat by the differential threshold method out of all candidate points. The higher the false detection rate, the worse the waveform reliability. Waveform amplitude characteristic index: Calculate the difference between the peak amplitude of the target rising edge and the baseline amplitude, reflecting the amplitude characteristic identification of the rising edge of the QRS wave / systolic pressure. The higher the amplitude, the more conducive it is to the accurate identification of the rising edge in the future.

[0071] The above four indicators are combined and calculated according to preset weights, and the signal quality scores corresponding to the electrocardiogram signal and the blood pressure signal are output respectively.

[0072] Step S303: Take the maximum signal quality value between the ECG signal quality value and the blood pressure signal quality value as the target signal, and obtain the corresponding target signal selection instruction based on the target signal.

[0073] The quality scores of the electrocardiogram signal and the blood pressure signal calculated in step S302 are compared numerically, and the physiological signal with the higher score and better overall waveform performance is selected as the target signal. Based on the waveform type of the target signal (ECG type / arterial blood pressure type), a unique target signal selection instruction is generated. The instruction simultaneously carries the signal type identifier, the specific filter frequency band parameters for the signal, the matching Tompkins integration window length, and other supporting parameters, which can be directly called by subsequent preprocessing and rising edge detection processes.

[0074] Step S304: Use the ECG signal or blood pressure signal corresponding to the target signal selection command as the signal to be processed.

[0075] Parse the target signal selection instruction generated in step S303, read the signal type identifier recorded in the instruction, and match it with the corresponding original waveform: If it is identified as an ECG signal type, the ECG signal collected in real time by the target IABP device is retrieved and identified as the signal to be processed. If it is identified as an arterial blood pressure signal, the blood pressure signal collected in real time by the target IABP device is retrieved and identified as the signal to be processed. The final output signal to be processed will be sent to the differentiated bandpass filtering preprocessing flow corresponding to S202 and S203 mentioned above.

[0076] Optionally, the differential signal between adjacent sampling points in the target detection signal is calculated using a preset sampling interval, and the differential threshold is calculated based on the peak value of the differential signal, including the following steps: Step S401: Acquire the first signal and the second signal between adjacent sampling points in the target detection signal according to the preset sampling interval.

[0077] Pre-configure uniform sampling parameters: the optimal sampling rate is 250Hz~360Hz, corresponding to the sampling interval. The value ranges from 2.78ms to 4ms; the target detection signal (ECG / blood pressure processed signal) after bandpass filtering is read frame by frame along the time axis. For each current sampling time t, two sets of temporally adjacent sampling data are extracted simultaneously: the sampling time... The corresponding waveform sample value is defined as the first signal. The waveform sample value corresponding to the current sampling time t is defined as the second signal. ; A continuous sliding window extracts the first and second signal samples from all adjacent sampling points in a continuous time sequence, completely preserving the original amplitude data of the waveform as it changes over time, providing the basic input for first-order difference operations.

[0078] Step S402: After performing a first-order difference operation on the first signal and the second signal through the sampling interval, the difference signal between adjacent sampling points is obtained.

[0079] Using the first and second signals of adjacent sampling points extracted in step S401 as the computational objects, standard first-order difference calculation is performed to obtain the difference signal. The formula used is: ; Differential signaling The value directly reflects the instantaneous slope of the target detection signal within that sampling interval: The positive peak corresponds to the region of maximum slope of the rising edge of the QRS wave in electrocardiogram and the systolic waveform of blood pressure. The negative peak corresponds to the falling edge of the waveform; In the above calculation process, the ECG and blood pressure target detection signals share the same set of differential operation logic, without the need to design separate calculation processes for different signal types, thus achieving unified slope feature extraction for both signals.

[0080] Step S403: Calculate the differential threshold corresponding to each heartbeat cycle using the current differential peak values ​​of all differential signals under each heartbeat cycle; wherein, the differential threshold is calculated using the following formula: ; This is the first differential threshold corresponding to the current heartbeat cycle; This is the second differential threshold corresponding to the previous heartbeat cycle; This represents the current differential peak value in the differential signal during the current heartbeat cycle. and These are the first weight coefficient and the second weight coefficient, respectively, satisfying... Furthermore, the initial differential threshold in the differential threshold is 1 / 3 of the differential peak value in the first 2 seconds of the first heartbeat cycle.

[0081] Using a single complete heartbeat cycle as the update unit, the adaptive differential threshold Thd is iteratively updated based on the peak characteristics of the differential signal within the current cycle, and is divided into two stages: initialization assignment and dynamic update per heartbeat. Initial threshold assignment: In the first 2 seconds of the acquisition window after the device starts detection, all differential signals D(t) are traversed and all positive differential peaks are filtered. 1 / 3 of the maximum value of the positive peak during this period is taken as the initial differential threshold Thd, which is used as the basis for the first rising edge judgment. Adaptive update per beat: After each valid beat is identified through subsequent Tompkins integral window verification, the maximum differential peak value of the waveform corresponding to that beat is extracted. The difference threshold is updated using a weighted smoothing formula: ; By using a weighted fusion of historical threshold weight of 0.875 and current peak weight of 0.125, the threshold abrupt changes caused by instantaneous fluctuations in signal amplitude are smoothed out, and waveform amplitude changes under complex scenarios such as electromyographic interference, hemodynamic fluctuations, and arrhythmia are continuously adapted. Matching differential threshold: Synchronously configure the continuous sampling point counting threshold (configurable range 3~8). This counting threshold, together with the dynamic differential threshold output in this step, constitutes a dual judgment condition. Only when the differential signals of multiple consecutive sampling points exceed the current period differential threshold is it marked as a candidate rising edge, further reducing the false detection probability caused by noise single-point pulses.

[0082] Optionally, the number of sampling points that meet the difference threshold is counted, and the rising edge in the target detection signal is determined using the sampling point count, including the following steps: Step S501: Obtain all target sampling points contained in the target detection signal within each heartbeat cycle, and determine the target differential signal of each target sampling point.

[0083] Using a single complete heartbeat cycle as the traversal window, all sampling points of the target detection signal (ECG filtered signal / blood pressure filtered signal) within the window are read sequentially, and these sampling points are uniformly recorded as target sampling points; the differential signal D(t) corresponding to each target sampling point, which is obtained by the previous step based on a uniform sampling interval Δt (sampling rate 250~360Hz, Δt=2.78~4ms), is retrieved, which is the target differential signal required in this step; The differential threshold Thd, which is adaptively updated for the current heartbeat cycle, and the configurable continuous sampling count threshold (the count threshold can be selected in the range of 3 to 8 sampling points, with 3 being the preferred setting for clinical scenarios) are retrieved simultaneously to provide quantitative benchmark parameters for subsequent rising edge determination.

[0084] Step S502: When the number of sampling points whose target differential signal under the current target sampling point is not less than the differential threshold satisfies the preset counting threshold condition, and the distance between the current target sampling point and the previous heartbeat cycle is greater than the refractory period of the target IABP device, the target time corresponding to the current target sampling point is obtained.

[0085] For each time-series progressive target sampling point, both the amplitude counting condition and the inappropriate interval condition are checked simultaneously. Only when both conditions are met can the time-series target time t1 corresponding to the current sampling point be extracted. Differential amplitude continuous counting condition: Continuously count the number of target sampling points that appear consecutively along the time axis and whose target differential signal D(t) ≥ the current period differential threshold Thd; when the cumulative number of consecutively exceeding the threshold sampling points reaches the preset counting threshold, it is determined that the first-level judgment condition is met. This rule can filter out noise and clutter from only a single sampling point that instantaneously exceeds the threshold. When the counting threshold is configured to 3, the optimal candidate rising edge is located at the 4th sampling point in the exceeding threshold sequence, maximizing proximity to the starting position of the rising edge and controlling the detection delay.

[0086] Physiological refractory period interval condition: A standard refractory period of 200ms is preset. The time difference between the current target sampling point and the previous marked candidate rising edge is calculated. If this time difference is greater than the 200ms refractory period, the second-level judgment condition is met. This rule masks multiple spurious peaks generated in a short period within the same cardiac cycle, avoiding repeated triggering of candidate marking by high-amplitude spurious waveforms such as P waves and T waves.

[0087] When the above two constraints are met simultaneously, the time coordinates corresponding to the current target sampling point are recorded as the target time t1.

[0088] Step S503: Determine the rising edge in the target detection signal based on the target time.

[0089] Mark the target time t1 selected in step S502 as the rising edge obtained by the differential threshold method of the target detection signal, and complete the first-level process; It should be noted that relying solely on differential thresholding to identify rising edges has inherent limitations: in scenarios involving ECG baseline drift, electromyographic interference, or atrial fibrillation waveform distortion, high-amplitude P waves, T waves, and F waves can still be misidentified as candidate rising edges of the heartbeat. Directly using this result would cause IABP inflation and deflation timing errors. Therefore, marking rising edges automatically triggers the subsequent Tompkins integral window verification process, which performs secondary verification of authenticity through squared enhancement, sliding integration, and integral threshold checking, eliminating invalid candidate points generated by spurious waveforms.

[0090] The traditional Pan-Tompkins method requires point-by-point serial differentiation (difference) → squaring → continuous sliding integration. A single beat is determined at the maximum of the integral envelope corresponding to the R-wave peak. At best, it can only determine whether it is a true QRS wave at the end of the QRS wave, resulting in significant latency. Furthermore, continuous integration is required for all data, leading to high memory and algorithmic overhead. If a suitable integration window length can be set and a suitable integration window threshold can be found at the rising edge of the integration window, the true beat can be determined, and integration window verification can be performed only at the position determined by the difference threshold, effectively solving the problems of high latency and computational cost in the Tompkins method. To address these issues, alternatively, after obtaining the squared difference signal at the sampling point where the rising edge occurs, the integral signal of the squared difference signal within the sliding integration window can be calculated using the sampling interval, including the following steps: Step S601: Obtain the current target sampling point corresponding to the rising edge, and obtain the differential square signal at the sampling point based on the square result of the target differential signal at the current target sampling point.

[0091] Retrieve the rising edge of the preceding differential threshold process completion marker, lock the current target sampling point corresponding to the rising edge, record the time coordinate t1 corresponding to the sampling point, and synchronously extract the target differential signal D(t) matched by the sampling point. Perform squared enhancement operation on the target differential signal, the operation formula is as follows: The result of the operation is defined as a differential squared signal; This squaring operation has a dual optimization effect: First, it unifies the differential signal with bidirectional fluctuations into a positive amplitude, eliminating the interference of negative values ​​on feature judgment at the falling edge; second, through nonlinear amplification, it significantly increases the amplitude of high-slope waveforms such as the ECG QRS wave and the rising edge of the systolic blood pressure, while simultaneously reducing the amplitude of interfering waveforms such as electromyographic noise, baseline drift, P wave, T wave, and atrial fibrillation F wave, thus widening the feature difference between effective heartbeats and pseudo-waves and providing clear differentiation for subsequent integration and screening; ECG and blood pressure target detection signals share the same set of squaring operation logic, eliminating the need to design separate differentiated calculation rules.

[0092] Step S602: Starting from the current target sampling point, obtain the time period corresponding to the sliding integral window before the starting point in the target detection signal, and obtain each sampling point within the time period.

[0093] The current target sampling point t1 corresponding to the rising edge is used as the end reference point of the sliding integration window. The sample length M of the dedicated integration window is matched according to the type of the current target detection signal: if the target detection signal is an electrocardiogram signal, the integration window length M is set to 30~40 sample points (corresponding to an actual duration of 0.12~0.16s); if the target detection signal is a blood pressure signal, the integration window length M is set to 40~50 sample points (corresponding to an actual duration of 0.16~0.2s), to adapt to the difference in the duration of the rising edge of the two types of physiological signals. Combined with the equipment, a unified preset sampling interval (Sampling rate 250~360Hz, (Values ​​range from 2.78 to 4 ms), defining the complete time interval corresponding to the sliding integral window in reverse and forward directions: ; Traverse all time-series sampling points within the interval along the time axis, completely extract the differential squared signal E(t) corresponding to each sampling point within the window, and uniformly classify it as the original input data for integration operation; Compared to the traditional Pan-Tompkins algorithm, which can only identify the hysteresis defect of the heartbeat at the end of the QRS wave, this scheme aligns the end point of the integration window with the rising edge candidate points obtained by differential integration. Feature verification can be performed directly at the rising edge position, compressing the detection delay from the root cause and ensuring that the total delay of single heartbeat detection is ≤35ms. Integration is performed only on a fixed time domain interval before and after the candidate peak detected by differential integration, rather than continuous sliding integration of the entire signal. This significantly reduces the window buffer length and computational load, making it compatible with MCUs and requiring less hardware. It also reduces memory and computing power costs without sacrificing accuracy.

[0094] Step S603: Calculate the integral signal of the differential square signal within the sliding integral window using the sampling interval and each sampling point.

[0095] Perform an accumulation and summation operation on the differential squared signals corresponding to the M sampling points within the sliding integral window intercepted in step S602, where the traversal terms satisfy k=0,1,2……M-1, and the summation object is... ; Introducing normalization coefficients The sum is averaged to eliminate the deviation in integral amplitude caused by the inconsistent integral window length M of ECG and blood pressure signals, ensuring that different signal types can share a common set of integral threshold judgment criteria. The normalized Tompkins moving square integral signal IT(t) is output, and the integral signal is calculated using the following formula: ; This represents the length of the sliding integral window. These are the label values ​​for each sampling point within the time period; For the first The differential squared signal at time t; This represents the time value corresponding to the current target sampling point; The sampling interval; To perform a moving integral on the differential squared signal E(t) over the time interval t1-M to t1, .

[0096] The generated integral signal IT(t) has a natural noise smoothing effect: the amplitude of single-point random noise is greatly reduced after sliding integration, while the continuous high slope feature of the rising edge of the real heartbeat is amplified by squaring and integration to form a stable convex waveform; this integral signal will be directly sent to the subsequent dual judgment process of integral threshold and rising slope to identify whether the rising edge is a real heartbeat and to eliminate false candidate points generated by P wave, T wave and blood flow distortion noise.

[0097] Optionally, the integration threshold of the integral signal can be obtained by calculating the integration threshold corresponding to each heartbeat cycle using the current integration peak value of all integral signals under each heartbeat cycle; wherein, the integration threshold is calculated by the following formula: ; This is the first integral threshold corresponding to the current heartbeat cycle; The second integral threshold corresponding to the previous heartbeat cycle; This represents the current integrated peak value in the integrated signal during the current heartbeat cycle. and These are the first weight coefficient and the second weight coefficient, respectively, satisfying... Furthermore, the initial integration threshold in the integration threshold is 2 / 3 of the peak integration value in the first 2 seconds of the first heartbeat cycle.

[0098] The calculation process of the integral threshold adopts the dynamic integral threshold adaptive solution logic that is matched with the Tompkins integral window verification process. Based on the peak characteristics of all integral signals within a single complete heartbeat cycle, the integral threshold is iteratively updated cycle by cycle to adapt to the current waveform state. It is divided into two major execution stages: threshold initialization and heartbeat-by-heart weighted iterative update. It replaces the traditional fixed integral threshold scheme and solves the defects of fixed threshold judgment failure in scenarios such as ECG baseline drift, electromyography interference, blood pressure and blood flow distortion, and arrhythmia. It provides a dynamic quantitative judgment benchmark for the verification of the authenticity of rising edges.

[0099] Specifically, after the device starts the signal detection function, it first captures the sliding integral signal for a continuous period of 2 seconds after the start of detection. The process iterates through all integral signal sampling points within the 2-second window, extracts all integral peak values, and filters out the maximum value. Two-thirds of this maximum integral peak value is used as the initial integral threshold for the first heartbeat cycle. This initialization rule is applicable to both ECG and blood pressure target detection signals. When there is no historical heartbeat data at the initial power-on stage, a baseline judgment threshold is generated based on the waveform characteristics of the first 2 seconds, ensuring that integral verification of the rising edge can be performed immediately after power-on, without a detection gap.

[0100] After each valid rising edge of a heartbeat is verified through the integration window, a new integration threshold adapted to the waveform of the current cycle is generated by weighting the current heartbeat cycle's integration peak value and the historical integration threshold of the previous cycle. The specific calculation rules are as follows: The new first integral threshold corresponding to the current heartbeat cycle; The second integral threshold for the completion of the previous complete heartbeat cycle (i.e., the historical baseline threshold for this iteration). : Sliding integral signal within the current complete heartbeat cycle The corresponding maximum integral peak value; C is the first weight coefficient and D is the second weight coefficient. The two sets of weights satisfy the constraint C+D=1; the preferred weight configuration is C=0.875 and D=0.125.

[0101] Threshold iteration calculation formula: ; A smooth weighting mechanism is adopted, which retains historical thresholds with high weights and introduces the current cycle peak with low weights: relying on the high weight of 0.875 to continue the long-term stable waveform judgment benchmark, and relying on the low weight of 0.125 to adapt to the instantaneous changes in the amplitude and interference intensity of a single heartbeat waveform, so as to avoid drastic changes in the integral threshold caused by electromyographic noise and instantaneous blood flow fluctuations, and ensure that the threshold changes smoothly and continuously.

[0102] The weighted calculation is completed to obtain the new integral threshold for the current period. Afterwards, The value is assigned to the historical threshold required for the next heartbeat cycle iteration. The process is repeated to complete the adaptive update step by step. The updated integration threshold will be used as a candidate rising edge. The core criterion: only when the candidate time Integral signal at the location The integral threshold is reached, and the rising slope of the integral signal is not lower than that of the differential signal. Only when the amplitude is 0.5 times that of the rising edge is it determined to be a true and valid heartbeat, and false candidate points generated by P waves, T waves, atrial fibrillation F waves, and blood flow distortion noises are eliminated.

[0103] Optionally, determining the valid rising edge in the target detection signal based on the integration threshold of the integrated signal includes the following steps: Step S701: Obtain all target sampling points contained in the target detection signal within each heartbeat cycle, and determine the target integral signal of each target sampling point.

[0104] Using a single complete heartbeat cycle as the time-series traversal window, all time-series sampling points of the target detection signal (ECG processing signal / arterial blood pressure processing signal) after differential bandpass filtering within the window are read frame by frame, and these sampling points are uniformly recorded as target sampling points; The normalized Tompkins integral signal, obtained by pre-square enhancement and sliding integral operation, corresponds one-to-one with each target sampling point. This serves as the target integral signal required for determination in this step. Synchronously retrieve the integral threshold of the current heart rate cycle after adaptive weighting update. And a preset slope judgment coefficient of 0.5; lock the target sampling point corresponding to the candidate rising edge t1 of the differential threshold mark separately, and extract the target integral signal IT(t1) and the original differential signal D(t1) of the point as the core data for dual verification.

[0105] Step S702: When the target integral signal at the current target sampling point is detected to be not less than the integration threshold, and the rising slope of the target integral signal is not less than 0.5 times the differential signal corresponding to the current target sampling point, the candidate time corresponding to the current target sampling point is obtained.

[0106] For the current target sampling point corresponding to the rising edge, the integral amplitude condition and the integral rising slope condition are checked simultaneously. Only when both conditions are met is the time coordinate corresponding to the current sampling point extracted as a valid candidate time t1.

[0107] The integral amplitude threshold determination condition is: the target integral signal IT(t1) corresponding to the current target sampling point is greater than or equal to the current period's adaptive integral threshold Thi. The rising edge of a true heartbeat will form a high-amplitude integral waveform after squaring and sliding integration, while noise, P waves, T waves, and atrial fibrillation F waves have low integral amplitudes after integration smoothing. This condition can initially filter out false candidate points with low feature intensity.

[0108] The integral rise slope constraint condition is: the rise slope of the target integral signal at the current target sampling point is ≥ 0.5 times the amplitude of the differential signal D(t1) at the current sampling point. Authentic ECG QRS waves and systolic blood pressure rise edges exhibit continuous steep slope changes, and the integral waveform synchronously shows a stable upward trend; single-point noise and isolated spurious waves only have instantaneous amplitude abrupt changes, resulting in extremely low integral rise slopes. This constraint can further eliminate interference waveforms without continuous rise characteristics.

[0109] The traditional Tompkins algorithm requires waiting for the integral waveform to reach its peak before it can determine the heartbeat, resulting in a large detection delay. This solution directly performs dual verification synchronously at the candidate position of the rising edge obtained by differential analysis, without waiting for the integral peak. This reduces the detection delay from the root cause, and the detection delay of the entire algorithm is ≤35ms per heartbeat.

[0110] Step S703: Determine the valid rising edge in the target detection signal based on the candidate time.

[0111] If both the integral amplitude and the integral rise slope conditions in step S702 are met, then the candidate time t1 is marked as the effective heartbeat rising edge in the target detection signal, the time coordinate is retained and sent to the subsequent detection post-processing process. If any of the judgment conditions are not met, the candidate point is determined to be a false detection point caused by noise or pseudo waveform, and the candidate time is directly removed and will no longer participate in subsequent calculations such as refractory period and missed detection backtracking. The output of valid rising edge timing data verified by the integral window serves as the benchmark timing for cardiac cycle calculation and IABP balloon inflation / deflation timing calculation, ensuring that the subsequent counterpulsation control timing is accurately matched with the cardiac physiological cycle.

[0112] The following is a simplified description of the above methods, which you can refer to. Figure 3 The flowchart shown here illustrates another signal detection method for IABP devices, which mainly includes the following steps: Signal input: Synchronously acquire two types of physiological signals, namely electrocardiogram and blood pressure, and provide dual signal source detection input.

[0113] Signal source selection: Supports manual specification of ECG / blood pressure signals, or selection of input pathways through signal quality evaluation in automatic mode.

[0114] ECG or blood pressure signal filtering: Differentiated bandpass filter parameters are used for the two types of signals to filter out interference such as baseline drift, power frequency, and blood flow fluctuations, and highlight the rising edge slope characteristics.

[0115] Differential thresholding method: Extracts the instantaneous slope of the signal through first-order differential operation, and quickly locks the candidate rising edge with adaptive dynamic threshold to ensure real-time detection.

[0116] Tompkins integral window validation: Squared enhancement and sliding integral processing are applied to candidate points. Noise, P / T wave and other false features are eliminated by dual constraints of integral amplitude and rising slope, thereby reducing false detections.

[0117] Post-detection processing: The results are further optimized by shielding during the refractory period and retrospectively supplementing detections for long-term missed detections, taking into account both accuracy and detection coverage.

[0118] Inflation and deflation timing settings: Based on the effective heartbeat rising edge, the duration of the cardiac cycle is calculated, and the timing nodes of balloon inflation and deflation are precisely calibrated to achieve synchronous control of counterpulsation action and cardiac cycle.

[0119] The effect diagram of the signal detection method for IABP equipment in this embodiment is shown below. Figure 4 , Figure 5 , Figure 6 As shown; where, Figure 4 IABP ECG signal data with the main wave direction of the QRS wave being positive; Figure 5 IABP ECG signal data corresponding to a negative QRS wave main wave direction; Figure 6 The IABP electrocardiogram data corresponding to the atrial fibrillation signal.

[0120] refer to Figure 4 , Figure 5 and Figure 6 The graph shows the detection results of the differential thresholding method, where "*" indicates the position of time t1 detected by the differential thresholding method. The graph reveals that the differential thresholding method misdetects large-amplitude P waves, T waves, and F waves in atrial fibrillation signals as QRS waves, leading to missed detections of true QRS waves.

[0121] refer to Figure 4 , Figure 5 and Figure 6 The graph shows the curves corresponding to the detection results of the differential threshold method initial detection + integral window verification. The "*" indicates the time t1 of the final determined true QRS wave rising edge. As can be seen from the graph, after differential threshold (quickly detecting the rising edge) + Tompkins integral window verification (eliminating false detection waves below the threshold) + post-detection processing (further reducing false detections and finding missed heartbeats), the requirement of quickly and accurately detecting the QRS rising edge is achieved, laying the foundation for the subsequent accurate output of the inflation and deflation timing position.

[0122] As can be seen from the above signal detection method for IABP devices, this method uses differential threshold calculation to obtain the rising edge in the target detection signal, and then uses an integral window to perform integral verification on the rising edge to obtain the effective rising edge in the target detection signal. The method accurately obtains the heartbeat rising edge in the target detection signal through a two-level fusion strategy, and can freely select the ECG signal or blood pressure signal as the target detection signal through a signal source selection switch, realizing flexible switching between the two.

[0123] The specific benefits of this method are as follows: 1. Rising edge leading dual threshold judgment: Abandoning the traditional R-wave peak lag beat judgment method, relying on the dual verification of rising edge slope and local energy, heartbeat recognition can be completed in the rising edge interval of QRS, eliminating the inherent delay of peak detection and greatly improving the real-time performance of detection.

[0124] 2. Locally limited integration window, low computational overhead: Unlike the redundant calculation of continuous integration across the entire domain in traditional algorithms, it only performs accurate integration on the local interval of the difference candidate peak, reducing cache and computational load. Without reducing detection accuracy, it is compatible with low-cost, low-power embedded MCU devices.

[0125] 3. Low-latency dual-feature fusion architecture: Construct a hierarchical fusion mechanism of differential + rising edge integral verification to avoid the dual latency of window length and peak value in traditional algorithms, and take into account both anti-interference ability and ultra-low latency characteristics to achieve real-time high-precision detection of physiological signals.

[0126] 4. Multi-signal universal adaptation: Breaking through the limitations of traditional algorithms in single signal detection, the architecture can simultaneously adapt to two types of physiological signals, namely ECG and blood pressure, greatly improving the algorithm's versatility and engineering application scope.

[0127] Corresponding to the above-described embodiment of the signal detection method for IABP devices, this embodiment of the invention also provides a signal detection system for IABP devices, such as... Figure 7 As shown, the system includes: Initialization module 100: used to obtain the target detection signal from the ECG and blood pressure signals collected in real time by the target IABP device, determine the heart beat cycle according to the time interval between adjacent heart beats in the target detection signal, and obtain the sliding integral window corresponding to the target detection signal; Rising edge determination module 200: It is used to calculate the differential signal between adjacent sampling points in the target detection signal using a preset sampling interval, calculate the differential threshold based on the peak value of the differential signal, count the sampling points that meet the differential threshold, and use the sampling point count to determine the rising edge in the target detection signal. Effective rising edge determination module 300: After acquiring the differential square signal at the sampling point where the rising edge is located, it calculates the integral signal of the differential square signal within the sliding integral window using the sampling interval, and determines the effective rising edge in the target detection signal based on the integration threshold of the integral signal. Signal detection result determination module 400: used to determine the signal detection result of the target IABP device through a valid rising edge.

[0128] As can be seen from the above signal detection system for IABP devices, the system uses differential threshold calculation to obtain the rising edge in the target detection signal, and then uses an integral window to verify the rising edge to obtain the effective rising edge in the target detection signal. The system accurately obtains the heartbeat rising edge in the target detection signal through a two-level fusion strategy, and can freely select the ECG signal or blood pressure signal as the target detection signal through a signal source selection switch, realizing flexible switching between the two.

[0129] The signal detection system for IABP devices provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned signal detection method for IABP devices. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned signal detection method for IABP devices.

[0130] This embodiment also provides an IABP device, such as Figure 8 As shown, the IABP device includes a signal acquisition module 10, a signal processing and detection module 20, an inflation / deflation timing calculation module 30, an balloon counterpulsation module 40, a user interface module 50, and a controller 60; wherein the controller 60 is connected to the signal acquisition module 10, the signal processing and detection module 20, the inflation / deflation timing calculation module 30, the balloon counterpulsation module 40, and the user interface module 50 respectively.

[0131] Specifically, signal acquisition module 10 is used to acquire electrocardiogram, blood pressure, and balloon pressure signals; Signal processing and detection module 20: used to detect heartbeats using the above signal processing and detection methods; Inflation / Discharge Timing Calculation Module 30: Used to calculate the inflation / discharge timing using the following method: Real-time calculation of RR interval: Continuously identify the peak value of the ECG R wave, calculate the time interval between two adjacent R waves, and obtain the total duration of the current cardiac cycle: T RR =T R (n+1) T R (n).

[0132] Determine when to inflate: (1) Select the timing start point: Take the detected rising edge position as the start point T s ; (2) Determine the inflation position: a. Initially identify a region: If the signal source is blood pressure, set it to 10-40%; if the signal source is ECG, set it to 20-80%. b. Determine the optimal inflation point for IABP: The search range for the blood pressure waveform is T. RR *(10-40%), find the maximum valley point T within this region. inf The search range for the electrocardiogram waveform is T. RR *(20-80%), find the endpoint T of the maximum wave peak within this region. inf ; c. Inflation Timing: The formula for calculating the inflation timing is: Inflate = (T... inf T s ) / T RR ×100.

[0133] Determine the timing of venting: The venting time is set to T before the start of the next wave. defthd Location, T defthd The time can be set from 120ms to 300ms. The formula for calculating the venting timing is: Deflate = (T RR T defthd ) / T RR ×100.

[0134] The balloon counterpulsation module 40 uses an air pump to inflate and deflate the balloon according to the calculated inflation and deflation timing to perform IABP counterpulsation.

[0135] User interface module 50: The IABP system user interface can display real-time waveforms and the detected heartbeat position, and mark the position corresponding to the current inflation / deflation timing.

[0136] The attached image Figure 9 and Figure 10 Examples of inflation / deflation timing displays for ECG and AP signal sources, respectively; Figure 9 The "*" indicates the rising edge position detected by QRS, the red vertical line indicates the inflation position, and the black vertical line indicates the deflation position. This is an example of the IABP system in ECG trigger mode. Figure 10 The asterisk (*) indicates the AP rising edge detection point, the red vertical line indicates the inflation position, and the black vertical line indicates the deflation position. This is an example of an IABP system in AP trigger mode. In addition, the user interface can also display: balloon pressure waveform, inflation / deflation pulse waves, system-related alarms, and other information.

[0137] The IABP device has the following technical advantages: 1. High accuracy of rising edge detection: The differential threshold method is used to quickly lock the candidate points of rising edge. Combined with the noise resistance verification of Tompkins integral window, it can achieve rapid capture and accurate screening. The accuracy of rising edge detection of ECG and blood pressure signals is ≥99%, effectively avoiding missed detection and false detection.

[0138] 2. Strong anti-interference ability: The noise smoothing properties of the Tompkins integral window make up for the weakness of the differential threshold method in anti-interference. Especially under complex conditions such as electromyography interference, baseline drift, and hemodynamic fluctuations, it can still stably extract rising edge features.

[0139] 3. Flexible dual-signal adaptation: Through targeted filtering parameters and integration window length design, seamless switching between ECG and blood pressure signals is achieved, meeting the requirement of IABP devices to simultaneously and accurately detect the rising edge of ECG or blood pressure signals, thus improving system reliability.

[0140] 4. Precise calculation of inflation and deflation timing: The inflation and deflation timing is precisely adjusted based on the real-time heartbeat pattern, and the display module helps medical staff quickly identify the inflation and deflation position.

[0141] 5. Balancing real-time performance and engineering feasibility: The algorithm involves no complex calculations, and the single-heart beat detection delay is ≤35ms, meeting the real-time requirements of IABP synchronous control; it only relies on basic digital signal processing operations and can be directly integrated into existing IABP equipment, resulting in low hardware cost and strong compatibility.

[0142] In addition, the specific structure of the controller is as follows: Figure 11 As shown, the device includes a processor 101 and a memory 102; wherein, the memory 102 is used to store one or more computer instructions, which are executed by the processor to implement the steps of the signal detection method for the IABP device described above.

[0143] Figure 11 The controller also includes a bus 103 and a communication interface 104. The processor 101, the communication interface 104 and the memory 102 are connected via the bus 103.

[0144] The memory 102 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. The bus 103 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 11 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0145] The communication interface 104 is used to connect to at least one user terminal and other network units through a network interface, and to send encapsulated IPv4 packets or IPv4 packets to the user terminal through the network interface.

[0146] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. The processor 101 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, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 102. The processor 101 reads the information in memory 102 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0147] This invention also provides a storage medium storing a computer program, which, when executed by a processor, performs the steps of the signal detection method for the IABP device described in the foregoing embodiments.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, devices, and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0151] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0152] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, 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 covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A signal detection method for an IABP device, characterized in that, The method includes: The target detection signal is obtained from the ECG and blood pressure signals collected in real time by the target IABP device. The heart beat cycle is determined according to the time interval between adjacent heart beats in the target detection signal, and the sliding integral window corresponding to the target detection signal is obtained. The differential signal between adjacent sampling points in the target detection signal is calculated using a preset sampling interval. After calculating the differential threshold based on the peak value of the differential signal, the number of sampling points that meet the differential threshold is counted, and the rising edge in the target detection signal is determined using the sampling point count. After acquiring the differential square signal at the sampling point where the rising edge is located, the integral signal of the differential square signal within the sliding integral window is calculated using the sampling interval, and the effective rising edge in the target detection signal is determined according to the integration threshold of the integral signal. The signal detection result of the target IAPB device is determined by the effective rising edge.

2. The signal detection method for an IABP device according to claim 1, characterized in that, The target detection signal is obtained based on the real-time acquisition of ECG and blood pressure signals from the target IABP device, including: Based on the selection result of the signal source selection switch in the target IABP device, a target signal selection instruction is generated, and the electrocardiogram signal or blood pressure signal corresponding to the target signal selection instruction is used as the signal to be processed. When the signal to be processed is the electrocardiogram (ECG) signal, the first cutoff frequency of the first bandpass filter is set by the baseline offset value and electromyographic noise value of the ECG signal, and the first cutoff frequency is used to control the first bandpass filter to filter the ECG signal collected in real time by the target IABP device, and the resulting ECG processed signal is used as the target detection signal. When the signal to be processed is the blood pressure signal, the second cutoff frequency of the second bandpass filter is set by the hemodynamic fluctuation value of the blood pressure signal, and the second cutoff frequency is used to control the second bandpass filter to filter the blood pressure signal collected in real time by the target IABP device, and the resulting blood pressure processed signal is used as the target detection signal.

3. The signal detection method for an IABP device according to claim 2, characterized in that, Based on the selection result of the signal source selection switch in the target IABP device, a target signal selection command is generated, and the ECG signal or blood pressure signal corresponding to the target signal selection command is used as the signal to be processed, including: When the selection result of the signal source selection switch corresponds to the automatic mode selection result, a signal quality calculation strategy corresponding to the automatic mode selection result is generated. Based on the aforementioned signal quality calculation strategy, the ECG signal quality value and the blood pressure signal quality value are calculated respectively using the effective heartbeat detection rate, noise energy ratio, spurious wave false detection ratio, and waveform amplitude characteristics corresponding to the original ECG signal and the blood pressure signal. The maximum signal quality value between the electrocardiogram signal quality value and the blood pressure signal quality value is taken as the target signal, and the corresponding target signal selection instruction is obtained based on the target signal; The electrocardiogram signal or the blood pressure signal corresponding to the target signal selection instruction is used as the signal to be processed.

4. The signal detection method for an IABP device according to claim 1, characterized in that, Calculating the difference signal between adjacent sampling points in the target detection signal using a preset sampling interval, and calculating the difference threshold based on the peak value of the difference signal, including: The first signal and the second signal between adjacent sampling points in the target detection signal are obtained according to a preset sampling interval; After performing a first-order difference operation on the first signal and the second signal through the sampling interval, the difference signal between the adjacent sampling points is obtained; The differential threshold corresponding to each heartbeat cycle is calculated using the current differential peak value of all differential signals under each heartbeat cycle; wherein, the differential threshold is calculated by the following formula: ； This is the first differential threshold corresponding to the current heartbeat cycle; This is the second differential threshold corresponding to the previous heartbeat cycle; The current differential peak value in the differential signal during the current heartbeat cycle; and These are the first weight coefficient and the second weight coefficient, respectively, satisfying... .

5. The signal detection method for an IABP device according to claim 4, characterized in that, The process of counting the number of sampling points that satisfy the differential threshold and using the sampling point count to determine the rising edge in the target detection signal includes: Obtain all target sampling points contained in the target detection signal within each heartbeat cycle, and determine the target differential signal of each target sampling point; When the number of sampling points at the current target sampling point that is not less than the differential threshold satisfies the preset counting threshold condition, and the distance between the current target sampling point and the previous heartbeat cycle is greater than the refractory period of the target IABP device, the target time corresponding to the current target sampling point is obtained. The rising edge of the target detection signal is determined based on the target time.

6. The signal detection method for an IABP device according to claim 1, characterized in that, After acquiring the differential squared signal at the sampling point where the rising edge is located, the integral signal of the differential squared signal within the sliding integration window is calculated using the sampling interval, including: Obtain the current target sampling point corresponding to the rising edge, and obtain the differential squared signal at the sampling point based on the squared result of the target differential signal at the current target sampling point; Starting from the current target sampling point, the time period corresponding to the sliding integral window before the starting point in the target detection signal is obtained, and each sampling point within the time period is obtained; The integral signal of the differential squared signal within the sliding integral window is calculated using the sampling interval and each sampling point.

7. The signal detection method for an IABP device according to claim 1, characterized in that, The process of obtaining the integration threshold of the integral signal includes: The integration threshold for each heartbeat cycle is calculated using the current integration peak value of all integral signals in each heartbeat cycle; wherein, the integration threshold is calculated using the following formula: ； This is the first integral threshold corresponding to the current heartbeat cycle; The second integral threshold corresponding to the previous heartbeat cycle; The current integral peak value in the integral signal during the current heartbeat cycle; and These are the first weight coefficient and the second weight coefficient, respectively, satisfying... .

8. The signal detection method for an IABP device according to claim 7, characterized in that, Determining the effective rising edge in the target detection signal based on the integration threshold of the integrated signal includes: Obtain all target sampling points contained in the target detection signal within each heartbeat cycle, and determine the target integral signal of each target sampling point; When the target integral signal at the current target sampling point is detected to be not less than the integration threshold, and the rising slope of the target integral signal is not less than 0.5 times the differential signal corresponding to the current target sampling point, the candidate time corresponding to the current target sampling point is obtained. The effective rising edge in the target detection signal is determined based on the candidate time.

9. A signal detection system for an IABP device, characterized in that, The system includes: Initialization module: used to obtain the target detection signal based on the ECG and blood pressure signals collected in real time by the target IABP device, determine the heart beat cycle according to the time interval between adjacent heart beats in the target detection signal, and obtain the sliding integral window corresponding to the target detection signal; Rising edge determination module: used to calculate the differential signal between adjacent sampling points in the target detection signal using a preset sampling interval, calculate the differential threshold based on the peak value of the differential signal, count the sampling points that meet the differential threshold, and use the sampling point count to determine the rising edge in the target detection signal; Effective rising edge determination module: After acquiring the differential square signal at the sampling point where the rising edge is located, it uses the sampling interval to calculate the integral signal of the differential square signal within the sliding integral window, and determines the effective rising edge in the target detection signal according to the integration threshold of the integral signal; Signal detection result determination module: used to determine the signal detection result of the target IABP device based on the effective rising edge.

10. An IABP device, characterized in that, The IABP device includes a signal acquisition module, a signal processing and detection module, an inflation / deflation timing calculation module, an balloon counterpulsation module, a user interface module, and a controller; wherein the controller is connected to the signal acquisition module, the signal processing and detection module, the inflation / deflation timing calculation module, the balloon counterpulsation module, and the user interface module respectively; The controller includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the steps of the signal detection method for an IABP device according to any one of claims 1 to 8.

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