A multi-sensor based early warning system and method for a radial artery compression hemostat

Abstract

The application provides a kind of early warning system and method of radial artery compression hemostat based on multiple sensors, belongs to complication early warning technical field.The method comprises the following steps: S1, multiple types of physiological signals in the radial artery compression area are synchronously collected and digitized;S2, the temperature at the proximal end and the temperature at the distal end are subjected to discrete wavelet transform;the pulse signal is subjected to time domain analysis, and the pulse amplitude is extracted;S3, a temperature distribution model of the compression area is constructed based on the temperature difference signal;S4, the resistivity distribution of the compression area is reconstructed through the electrode impedance signal, and the bleeding position is located;S5, the pressure change rate is calculated based on the pressure digital signal;the pulse amplitude attenuation rate is calculated based on the pulse amplitude;the pressure change rate, the temperature gradient vector, the pulse amplitude attenuation rate and the bleeding index are fused through a weighted summation formula, and a comprehensive risk value is obtained.The application improves the complication-related parameter coverage rate through multiple sensors, and improves the timeliness and accuracy of complication early warning.

Description

Technical Field

[0001] This invention relates to the fields of compression hemostat technology and complication early warning technology, and in particular to an early warning system and method for a radial artery compression hemostat based on multiple sensors. Background Technology

[0002] Radial artery puncture, due to its minimally invasive nature and rapid recovery, has become the preferred approach for coronary angiography (CAG) and percutaneous coronary intervention (PCI). However, various complications may occur during postoperative compression hemostasis:

[0003] Arterial hematoma and bleeding: The incidence rate is about 3%-8%, mostly caused by insufficient compression pressure or displacement of the hemostat;

[0004] Radial artery occlusion (RAO): The incidence rate is about 1%-10%, and it is directly related to excessively long compression time and excessively high pressure.

[0005] Compartment syndrome (ACS): It occurs in about 0.1% of cases, but can lead to permanent nerve damage or even amputation. Early symptoms include limb swelling, decreased skin temperature, and weakened pulse.

[0006] Currently, the routine clinical use of rotary hemostatic devices (such as the Terumo TR Band) or pneumatic tourniquets is as follows:

[0007] 1. Initial pressurization: Apply pressure based on experience (usually 20-25 ml air injection);

[0008] 2. Stepwise decompression: Manually release 2-5 ml of pressure every 2 hours post-surgery, for a total of 6-8 hours;

[0009] 3. Demolition assessment: Relying on medical staff to palpate the radial artery pulsation and observe the bleeding.

[0010] There are significant risks involved in this process:

[0011] 1. Monitoring blind spot: At night, patients are asleep, and their ability to perceive numbness and pain in their limbs is reduced;

[0012] 2. Insidious symptoms:

[0013] Early signs of compartment syndrome include only a slight decrease in skin temperature;

[0014] In arterial occlusion, distal pulse attenuation may occur earlier than the patient's subjective pain.

[0015] 3. Human error:

[0016] Excessive stress release can lead to delayed bleeding.

[0017] The hemostat was applied too tightly and was not detected in time.

[0018] The existing improvement plan still has obvious shortcomings:

[0019] 1. Single-parameter monitoring devices: such as the radial artery compression hemostat proposed in patent CN119257673A, which can only monitor wristband pressure and cannot identify RAO, bleeding or ACS (it needs to be combined with temperature and pulse parameters);

[0020] 2. Passive alarm system: such as the arterial hemostasis device with blood leakage monitoring and alarm function in patent CN118697409A, which uses a single-point sensor and has a blind zone of more than 50% for lateral leakage detection;

[0021] 3. Human dependence problem: The alarm threshold of existing equipment is fixed and cannot be dynamically adjusted according to individual patient differences (such as blood pressure and vascular elasticity).

[0022] In summary, the existing technology has the following technical defects:

[0023] 1. Limitations of single-parameter monitoring: Arterial occlusion cannot be determined by relying solely on pressure parameters (it needs to be combined with pulse and temperature changes);

[0024] 2. Incomplete bleeding detection: Existing resistive sensors use a single electrode arrangement, which easily misses lateral bleeding;

[0025] 3. Delayed early warning: The lack of multi-parameter fusion algorithms means that complication identification relies on a single threshold trigger. Summary of the Invention

[0026] This invention provides an early warning system and method for a radial artery compression hemostat based on multiple sensors, in order to solve one or more of the problems mentioned above.

[0027] To achieve the above objectives, the present invention adopts the following technical solution:

[0028] A pre-warning method for a radial artery compression hemostat based on multiple sensors, comprising:

[0029] S1. Using a multi-sensor-based radial artery compression hemostat, multiple types of physiological signals in the radial artery compression area are simultaneously collected and digitally processed to obtain pressure digital signals, proximal temperature, distal temperature, pulse signals, and electrode impedance signals.

[0030] S2. Perform discrete wavelet transform on the proximal and distal temperatures to remove motion artifacts, obtain the denoised temperature signals, and calculate the temperature difference signal between the two points to obtain the temperature difference signal.

[0031] Time-domain analysis of the pulse signal was performed to extract the pulse amplitude;

[0032] S3. Construct a temperature distribution model of the compression region based on temperature difference signals:

[0033] Data expansion: Virtual temperature values ​​of grid points are generated in the compression zone using bilinear interpolation to supplement the spatial dimension of the single-point temperature difference signal;

[0034] Surface fitting: Perform quadratic polynomial surface fitting on the virtual temperature value to establish the temperature distribution surface equation. After solving the polynomial coefficients, calculate the temperature gradient vector to reflect the rate and direction of temperature change in the plane.

[0035] S4. Reconstruct the resistivity distribution of the compression area using electrode impedance signals to locate the bleeding site:

[0036] Impedance data processing: Baseline correction is performed on the electrode impedance signal, the impedance change relative to the initial time is calculated, and the impedance change is converted into a voltage change.

[0037] Finite element modeling: The compression region is divided into multiple triangular elements. The voltage change is used as the input of the finite element method. A linear relationship between the measured voltage and the element conductivity is established. The element conductivity is updated by the Landweber iteration method. Cubic spline interpolation and surface fitting are performed on the element resistivity to generate a continuous resistivity distribution.

[0038] Bleeding feature extraction: Based on the continuous resistivity distribution, the bleeding index is calculated, and the coordinates of the grid point corresponding to the minimum resistivity value are used as the coordinates of the bleeding center point.

[0039] S5. Calculate the pressure change rate based on the pressure digital signal; calculate the pulse amplitude decay rate based on the pulse amplitude; and obtain the comprehensive risk value by fusing the pressure change rate, temperature gradient vector, pulse amplitude decay rate, and bleeding index through a weighted summation formula.

[0040] Early warning decision-making:

[0041] A Level 1 alarm is triggered when the overall risk value is ≥0.8;

[0042] A level 2 alarm is triggered when the overall risk value is ≥0.5 and <0.8.

[0043] A level 3 alarm is triggered when the overall risk value is less than 0.5.

[0044] Pressure regulation: Calculate the pressure regulation amount based on the comprehensive risk value, and adjust the compression pressure based on the pressure regulation amount.

[0045] In this specification, a dual-ring electrode impedance array is used for acquiring electrode impedance signals. Specifically, the number of electrodes in the inner and outer rings is the same, the spacing between the inner ring electrodes is 2 mm, and the spacing between the outer ring electrodes is 5 mm. A four-wire impedance measurement method is used to acquire the impedance values ​​between the electrodes by scanning with a period of 1 second. The outer ring electrode is used as the excitation electrode, and the electrodes of the inner and outer rings are used together as the detection electrode.

[0046] In this manual, the specific steps for calculating the temperature difference are as follows: the db4 wavelet basis is used to perform a 3-level decomposition on the proximal and distal temperature signals to remove high-frequency noise, and the denoised proximal and distal temperature signals are subtracted to obtain the temperature difference signal.

[0047] In this specification, the steps for calculating the temperature gradient vector include: generating virtual temperature values ​​for 10×10 grid points in the compression region using bilinear interpolation; performing quadratic polynomial surface fitting on the grid point temperature data; and calculating the temperature gradients in the x-axis and y-axis directions based on the fitted surface equations to form the temperature gradient vector.

[0048] In this manual, the pressure adjustment amount is calculated based on the comprehensive risk value, and the pressure adjustment amount is limited to the range of -10kPa to +10kPa. The adjusted pressure is then fed back as the new pressure value to the signal acquisition step for the next round of monitoring.

[0049] In this specification, the formula for quadratic polynomial surface fitting is as follows:

[0050] T surf (x,y,t)=c0+c1x+c2y+c3x 2 +c4xy+c5y 2 ;

[0051] T surf (x,y,t): The surface fitting value of the temperature field at time t and coordinates (x,y); (x,y): Two-dimensional planar coordinates of the compression region, with the center of compression as the origin, the x-axis representing the direction of the radial artery, and the y-axis representing the direction perpendicular to the artery; c0: A constant term, representing the baseline temperature value at the origin (0,0); c1, c2: Linear coefficients, representing the linear rates of change of temperature along the x-axis and y-axis, respectively; c3, c4, c5: Quadratic coefficients, representing the linear rates of change of temperature along the x-axis and y-axis, respectively. 2 xy, y 2 Nonlinear rate of change of direction;

[0052] By fitting the temperature distribution using a quadratic polynomial, the temperature gradient and curvature changes within the compression area are captured.

[0053] In this specification, the least squares method is used to solve for the coefficients c0, c1, c2, c3, c4, and c5. The objective function is to minimize the mean square error between the fitted value and the interpolated temperature value.

[0054]

[0055] Where (x) i ,y i ) represents the coordinates of the grid point, T int (x i ,y i (x, t) represents the coordinates at time t. i ,y i The virtual temperature value at ().

[0056] In this specification, based on T surf The temperature gradient vector is obtained by differentiating (x, y, t). This reflects the direction of the most drastic temperature change.

[0057] A warning system for a multi-sensor-based radial artery compression hemostat, employing any one of the aforementioned warning methods for a multi-sensor-based radial artery compression hemostat, the warning system comprising:

[0058] Multi-sensor-based radial artery compression hemostat;

[0059] The data acquisition module is used to simultaneously acquire and digitally process multiple types of physiological signals in the radial artery compression area using a multi-sensor-based radial artery compression hemostat to obtain pressure digital signals, proximal temperature, distal temperature, pulse signals, and electrode impedance signals.

[0060] The data processing module is used to perform discrete wavelet transform on the proximal and distal temperatures to remove motion artifacts and obtain denoised temperature signals, and to calculate the temperature difference signal between the two points; it also performs time-domain analysis on the pulse signal to extract the pulse amplitude.

[0061] The temperature modeling module is used to construct a temperature distribution model of the compression area based on the temperature difference signal;

[0062] The bleeding calculation module is used to reconstruct the resistivity distribution of the compression area through electrode impedance signals and locate the bleeding site.

[0063] The early warning and pressure regulation module is used for:

[0064] The pressure change rate is calculated based on the digital pressure signal; the pulse amplitude decay rate is calculated based on the pulse amplitude; and the comprehensive risk value is obtained by fusing the pressure change rate, temperature gradient vector, pulse amplitude decay rate, and bleeding index through a weighted summation formula.

[0065] Early warning decision-making:

[0066] A Level 1 alarm is triggered when the overall risk value is ≥0.8;

[0067] A level 2 alarm is triggered when the overall risk value is ≥0.5 and <0.8;

[0068] A level 3 alarm is triggered when the overall risk value is less than 0.5.

[0069] The pressure adjustment amount is calculated based on the comprehensive risk value, and the compression pressure is adjusted based on the pressure adjustment amount.

[0070] The multi-sensor-based radial artery compression hemostat described in this specification includes:

[0071] The main body of the compression hemostat has wrist straps symmetrically arranged on both sides;

[0072] A pressure device is located at the top of the main body of the compression hemostat;

[0073] A pressure block is located at the bottom of the main body of the compression hemostat and is connected to the pressure device;

[0074] A pressure sensing module is arranged on the pressure surface of the pressure block;

[0075] The temperature monitoring unit has two temperature sensors symmetrically arranged at the proximal and distal ends of the pressure surface of the pressure block;

[0076] A photoelectric pulse module is arranged at the edge of the pressure surface of the pressure block;

[0077] The bleeding detection module has multiple electrodes arranged in a multi-ring pattern on the pressure surface of the pressure block.

[0078] In summary, the present invention has at least the following beneficial effects:

[0079] This invention uses four types of sensors—pressure, temperature, pulse, and impedance—to collect key indicators such as compression intensity, temperature gradient, pulse intensity, and bleeding status in real time. Compared with traditional single-pressure monitoring solutions, it improves the coverage of complication-related parameters and enhances the timeliness and accuracy of complication early warning.

[0080] By using an inner and outer dual-ring 8-electrode array (4 electrodes in the inner ring + 4 electrodes in the outer ring) in conjunction with 32-channel impedance measurement, the coverage of bleeding detection angles is improved.

[0081] By calculating comprehensive risk values, the timeliness and accuracy of complication early warning are improved, as well as the ability to accurately regulate stress. Attached Figure Description

[0082] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0083] Figure 1 This is a schematic diagram of the early warning method for a multi-sensor-based radial artery compression hemostat involved in this invention.

[0084] Figure 2 This is a flowchart illustrating the early warning method for a multi-sensor-based radial artery compression hemostat involved in this invention.

[0085] Figure 3 This is a schematic diagram of the multi-sensor-based radial artery compression hemostat involved in this invention.

[0086] Figure 4 This is a schematic diagram of the electrode layout involved in the present invention.

[0087] Figure label:

[0088] 1. Compression hemostat body; 2. Wristband; 3. Pressurizing device; 4. Pressurizing block; 5. Pressure sensing module; 6. Temperature sensor; 7. Proximal end; 8. Distal end; 9. Electrode. Detailed Implementation

[0089] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the embodiments of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0090] The following disclosure provides many different implementations or examples for carrying out different structures of the embodiments of the present invention. To simplify the disclosure of the embodiments of the present invention, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the embodiments of the present invention. Furthermore, reference numerals and / or reference letters may be repeated in different examples of the embodiments of the present invention; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various implementations and / or arrangements discussed.

[0091] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0092] like Figure 1 and Figure 2 As shown, this embodiment provides an early warning method for a radial artery compression hemostat based on multiple sensors, including:

[0093] S1. Acquisition of raw data from multimodal sensors

[0094] Using a multi-sensor-based radial artery compression hemostat, physiological signals from the radial artery compression area are simultaneously acquired and digitized through integrated sensors:

[0095] Pressure digital signal P(t): The real-time pressure of the pressure surface is collected and converted into a digital signal using a ring-shaped flexible thin film pressure sensor (pressure sensing module 5), with a sampling frequency of 100Hz.

[0096] Temperature signals (proximal end temperature, distal end temperature): The temperature at the two points is collected and converted into digital signals by dual PT100 thermistors (temperature monitoring units) symmetrically arranged at the proximal end 7 and distal end 8 of the compression zone. The sampling frequency is 20Hz.

[0097] Pulse signal: The MAX30102 chip (photoelectric pulse module) is used to collect the red light intensity signal, convert it into a digital signal (dimensionless), and the sampling frequency is 50Hz for subsequent pulse feature analysis.

[0098] Electrode impedance signal (bleed detection module): An inner and outer double-ring 8-electrode array is adopted (the inner ring has 4 electrodes 9 with a spacing of 2mm and the outer ring has 4 electrodes 9 with a spacing of 5mm). The impedance value between the electrodes 9 is scanned once per second using the four-wire impedance measurement method. The outer ring electrode 9 is used as the excitation electrode, and the inner and outer ring electrodes 9 together are used as the detection electrode to generate an impedance digital signal.

[0099] S2. Wavelet Transform Preprocessing and Feature Extraction

[0100] Noise reduction and feature extraction are performed on temperature and pulse signals, while the unprocessed original signal is transmitted.

[0101] Temperature signal processing: Discrete wavelet transform (using db4 wavelet basis, 3-level decomposition) is performed on the digital temperature signals of the proximal end 7 and the distal end 8 to remove high-frequency noise such as motion artifacts, and the temperature difference signal between the two points is calculated (calculated temperature difference signal = denoised temperature at point A - denoised temperature at point B).

[0102] Pulse signal processing: Time-domain analysis of the red light pulse digital signal is performed to extract the pulse amplitude A(t) (the difference between the peak and trough).

[0103] S3. Temperature Field Surface Fitting and Gradient Calculation

[0104] A temperature distribution model of the compression region is constructed based on temperature difference signals to analyze the spatial temperature variation trend.

[0105] Data expansion: Virtual temperature values ​​of 10×10 grid points are generated in the compression zone (5cm×5cm rectangular area) using bilinear interpolation to supplement the spatial dimension of single-point temperature difference.

[0106] Surface fitting: Perform quadratic polynomial surface fitting on the temperature data of grid points to establish the temperature distribution surface equation, and solve the fitting coefficients by the least squares method.

[0107] Gradient calculation: Based on the fitted surface equation, calculate the rate of temperature change in the x-axis and y-axis directions to form a temperature gradient vector, which reflects the intensity of spatial temperature change in the compression region.

[0108] S4. Impedance Tomography (EIT) and Bleeding Analysis

[0109] Resistivity distribution was reconstructed using multi-electrode impedance data to pinpoint the location of bleeding.

[0110] Impedance data processing: Baseline correction is performed on the digital impedance signal, and the impedance change between the current time and the initial time is calculated; the impedance change is converted into voltage change through a constant current source, which is used as the measured voltage input of the finite element model.

[0111] Finite element modeling: The compression region is divided into 200 triangular elements. A linear relationship between the measured voltage and the element conductivity is established. The resistivity estimate is updated by the Landweber iteration method until the iteration error is less than 5%, and the resistivity distribution of the compression region is reconstructed.

[0112] Bleeding feature extraction: The reconstructed resistivity distribution is fitted with a surface to extract the bleeding index (value range 0-1, the larger the value, the higher the risk) and the coordinates of the bleeding center point, where the low resistivity area corresponds to the bleeding location.

[0113] S5. Multi-parameter fusion, early warning and pressure regulation

[0114] By integrating multi-dimensional physiological parameters, it enables early warning of complications and dynamic adjustment of compression pressure.

[0115] Parameter calculation:

[0116] Pressure change rate: Calculated based on the pressure change per unit time from a continuous pressure digital signal.

[0117] Pulse amplitude decay rate: Analyze continuous pulse amplitude data and calculate the amplitude decay value per unit time.

[0118] Risk fusion: The pressure change rate, temperature gradient, pulse amplitude decay rate and bleeding index are used to generate a comprehensive risk value through a weighted summation model, and the weight coefficients are determined based on clinical data training.

[0119] Tiered early warning:

[0120] Overall risk value ≥ 0.8: Level 1 alarm (red light + remote SMS).

[0121] 0.5 ≤ Comprehensive Risk Value < 0.8: Level 2 Alarm (Yellow Light + Vibration).

[0122] Overall risk value < 0.5: Level 3 alarm (green light + buzzer).

[0123] Pressure Regulation: The pressure regulation amount (limited to ±5kPa) is calculated based on the comprehensive risk value. The STM32-driven micro-pump (pressurization device 3) adjusts the pressure of the airbag (pressurization block 4). The new pressure value is fed back to S1 for cyclical acquisition, forming a "monitoring-analysis-intervention" closed loop. Alternatively, a mechanical pressurization device (pressurization device 3), such as a hydraulic pump or electric telescopic rod, can be used, paired with a pressurization block 4 of a certain hardness, such as a planar or curved / arc-shaped pressurization block 4 made of flexible polymer materials. The flexible polymer material can be silicone, polyurethane, thermoplastic elastomers, etc.

[0124] In some embodiments, the formula for quadratic polynomial surface fitting is as follows:

[0125] T surf (x,y,t)=c0+c1x+c2y+c3x 2 +c4xy+c5y 2 ;

[0126] T surf (x,y,t): The surface fitting value of the temperature field at time t and coordinates (x,y); (x,y): Two-dimensional planar coordinates of the compression region, with the center of compression as the origin, the x-axis representing the direction of the radial artery, and the y-axis representing the direction perpendicular to the artery; c0: A constant term, representing the baseline temperature value at the origin (0,0); c1, c2: Linear coefficients, representing the linear rates of change of temperature along the x-axis and y-axis, respectively; c3, c4, c5: Quadratic coefficients, representing the linear rates of change of temperature along the x-axis and y-axis, respectively. 2 xy, y 2 Nonlinear rate of change of direction;

[0127] By fitting the temperature distribution using a quadratic polynomial, the temperature gradient and curvature changes within the compression area are captured. For example, c1>0 indicates that the temperature increases with increasing x, and c3>0 indicates that the temperature is convex in the x-axis direction. A temperature field thermogram can be generated using the surface equation, which visually displays the temperature difference between the proximal end 7 and the distal end 8, as well as abnormally low temperature regions (indicating blood flow obstruction).

[0128] In some embodiments, the least squares method is used to solve for the coefficients c0, c1, c2, c3, c4, and c5, and the objective function is to minimize the mean square error between the fitted value and the interpolated temperature value.

[0129]

[0130] Where (x) i ,yi ) represents the coordinates of the grid point, T int (x i ,y i (x, t) represents the coordinates at time t. i ,y i The virtual temperature value at ().

[0131] In some embodiments, based on T surf The temperature gradient vector is obtained by differentiating (x, y, t). It reflects the direction of the most drastic temperature change and helps assess the risk of arterial occlusion. If the quadratic coefficients c3 or c5 deviate significantly from zero, it may indicate abnormal local temperature accumulation (such as low-temperature diffusion in a bleeding area). Combining this with the bleeding index can improve the accuracy of early warning.

[0132] In some embodiments, the resistivity distribution is reconstructed using impedance data to locate the bleeding site:

[0133] Impedance data processing: processing the digital impedance signal Z mn Baseline correction is performed on (t) (i.e., the impedance value of the electrode pair at time t (m,n), where m is the excitation electrode number and n is the detection electrode number), and the impedance change ΔZ is calculated. mn (t):

[0134] ΔZ mn (t)=Z mn (t)-Z mn (t0);

[0135] At the initial moment t0, the voltage change is converted into a constant current source (I = 1mA): ΔV mn (t)=I·ΔZ mn (t);

[0136] Finite element modeling and inversion: The compression region is divided into 200 triangular elements, and the relationship between voltage and conductivity is established.

[0137]

[0138] G mnk Sensitivity matrix elements (reflecting the spatial coupling between the electrode pair (m,n) and the unit k, dimensionless), σ k : Conductivity of unit k, ρ k Resistivity of unit k (unit: Ω·m), n mn : Measurement of noise.

[0139] Update ρ using the Landweber iteration method k :

[0140]

[0141] ω = 0.2: relaxation factor, iteration terminates when root mean square error (RMSE) < 5%.

[0142] Bleeding feature extraction: for ρ k Perform cubic spline interpolation to generate a continuous resistivity distribution ρ(x,y,t) (resistivity at time t at coordinates (x,y)).

[0143] Calculate the bleeding index:

[0144]

[0145] ρ dry =10 4 Ω·m, the baseline value for dry skin;

[0146] If the minimum resistivity of the compressed area, min(ρ(x,y,t)) = 8000 Ω·m (close to the value of dry skin), then If 0.2 < threshold 0.5 (this can also be determined in conjunction with other embodiments), a level 3 alarm is triggered (green light + buzzer), indicating that the current state is safe and there is no risk of bleeding.

[0147] For example, if the minimum resistivity of the compression area is min(ρ(x,y,t)) = 5000 Ω·m (the resistivity of blood is approximately 100–1000 Ω·m, and venous exudate is close to the middle value), then... 0.5 (or other embodiments can be used for judgment) triggers a level 2 alarm (yellow light + vibration), indicating that there is mild bleeding and the compression status needs to be monitored.

[0148] For example, if the minimum resistivity of the compression region is min(ρ(x,y,t)) = 500 Ω·m (close to the low resistivity characteristics of arterial blood), then... If 0.95 > threshold 0.8 (this can also be determined in conjunction with other embodiments), a level one alarm is triggered (red light + NB-IoT SMS), indicating severe arterial bleeding that requires immediate intervention.

[0149] Locating the coordinates of the center point of the bleeding:

[0150] (x bleed ,y bleed =argmin(ρ(x,y,t)).

[0151] In some embodiments, the rate of pressure change is: Δt = 0.1, sampling interval;

[0152] Pulse amplitude decay rate: T = 0.8s, mean pulse cycle;

[0153] Overall risk value:

[0154]

[0155] Weight coefficient: α=0.4, β=0.3, γ=0.2, δ=0.1; P th =10 kPa / s: Threshold for pressure change rate, T th =2℃ / cm: Temperature gradient threshold, A th =0.5mV / s: Pulse amplitude decay rate threshold.

[0156] Tiered early warning logic:

[0157] R(t)≥0.8: Level 1 alarm (red light + NB-IoT SMS), high-risk condition requiring immediate medical intervention; serious complications may occur, such as:

[0158] Massive arterial bleeding (sudden drop in pressure and high oozing index);

[0159] Compartment syndrome (abnormal temperature gradient and significant pulse attenuation);

[0160] Acute radial artery occlusion (sudden drop in pulse amplitude accompanied by excessively high pressure).

[0161] Handling strategy:

[0162] Light alarm: Red light flashes continuously (brightness ≥ 200 lumens);

[0163] Remote alarm: Sends a text message containing the patient ID, time, and risk value to the medical terminal via NB-IoT.

[0164] Automatic operation: Pauses automatic pressure adjustment, maintains the current pressure, and waits for manual confirmation.

[0165] 0.5≤R(t)<0.8: Level 2 alarm (yellow light + vibration), medium-risk condition, requiring close monitoring and preparation for intervention; indicating potential complications, such as:

[0166] Insufficient pressure leads to slow bleeding (increased bleeding index);

[0167] Early stage of local tissue ischemia (mildly abnormal temperature gradient);

[0168] Abnormal pulse fluctuations (possibly related to hemostat displacement).

[0169] Handling strategy:

[0170] Light alarm: Yellow light flashes intermittently (1 time / second);

[0171] Vibration alarm: Equipment vibration module activated (amplitude ≥ 2mm, frequency 50Hz);

[0172] Automatic operation: Initiate pressure fine-tuning (±1kPa each time), and if the pressure does not recover after 30 minutes of continuous monitoring, it will be upgraded to a level one alarm.

[0173] R(t)<0.5: Level 3 alarm (green light + buzzer), low-risk state, indicating that the system is operating normally or requires basic status confirmation.

[0174] Pressure regulation amount: ΔP adj =clip(E·R(t),-10kPa,+10kPa);

[0175] `clip` is the limiting function; `E=2` is used to amplify or reduce the influence of the overall risk value on pressure regulation; the pressure is adjusted to: The pressure is driven by an STM32 micro-pump (pressurization device 3, the specific type is selected according to the actual design, such as a hydraulic pump, electric telescopic rod, etc.).

[0176] P new (t)=P(t)+ΔP adj ;

[0177] P new (t): The adjusted pressure value is re-acquired by S1 to form a closed-loop control.

[0178] Mapping of comprehensive risk values ​​to physiological indicators:

[0179]

[0180] A warning system for a multi-sensor-based radial artery compression hemostat, employing any one of the aforementioned warning methods for a multi-sensor-based radial artery compression hemostat, wherein the warning system comprises:

[0181] Multi-sensor-based radial artery compression hemostat;

[0182] The data acquisition module is used to simultaneously acquire and digitally process multiple types of physiological signals in the radial artery compression area using a multi-sensor-based radial artery compression hemostat to obtain pressure digital signals, proximal temperature, distal temperature, pulse signals, and electrode impedance signals.

[0183] The data processing module is used to perform discrete wavelet transform on the proximal and distal temperatures to remove motion artifacts and obtain denoised temperature signals, and to calculate the temperature difference signal between the two points; it also performs time-domain analysis on the pulse signal to extract the pulse amplitude.

[0184] The temperature modeling module is used to construct a temperature distribution model of the compression area based on the temperature difference signal;

[0185] The bleeding calculation module is used to reconstruct the resistivity distribution of the compression area through electrode impedance signals and locate the bleeding site.

[0186] The early warning and pressure regulation module is used for:

[0187] The pressure change rate is calculated based on the digital pressure signal; the pulse amplitude decay rate is calculated based on the pulse amplitude; and the comprehensive risk value is obtained by fusing the pressure change rate, temperature gradient vector, pulse amplitude decay rate, and bleeding index through a weighted summation formula.

[0188] Early warning decision-making:

[0189] A Level 1 alarm is triggered when the overall risk value is ≥0.8;

[0190] A level 2 alarm is triggered when the overall risk value is ≥0.5 and <0.8.

[0191] A level 3 alarm is triggered when the overall risk value is less than 0.5.

[0192] The pressure adjustment amount is calculated based on the comprehensive risk value, and the compression pressure is adjusted based on the pressure adjustment amount.

[0193] like Figure 3 and Figure 4 As shown, this embodiment provides a multi-sensor-based radial artery compression hemostat, comprising:

[0194] The main body 1 of the compression hemostat has wristbands 2 symmetrically arranged on both sides;

[0195] The pressure device 3 is located at the top of the main body 1 of the compression hemostat;

[0196] The pressure block 4 is located at the bottom of the compression hemostat body 1 and is connected to the driving end of the pressure device 3 that passes through the compression hemostat body 1;

[0197] Pressure sensing module 5 is arranged on the pressure surface of the pressure block 4;

[0198] The temperature monitoring unit has two temperature sensors 6 symmetrically arranged at the proximal end 7 and distal end 8 of the pressure surface of the pressure block 4;

[0199] A photoelectric pulse module is arranged at the edge of the pressure surface of the pressure block 4;

[0200] The bleeding detection module has multiple electrodes 9 arranged in a multi-ring shape (such as inner and outer rings) on the pressure surface of the pressure block 4.

[0201] The embodiments described above are for illustrative purposes only and are not intended to limit the invention. Therefore, any changes in numerical values ​​or substitutions of equivalent elements should still fall within the scope of this invention.

[0202] The above detailed description will enable those skilled in the art to understand that the present invention can indeed achieve the aforementioned objectives and has complied with the provisions of the Patent Law.

[0203] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention. The above descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

[0204] It should be noted that the above description of the process is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to the process under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0205] The basic concepts have been described above. Obviously, for those skilled in the art who have read this application, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore, such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0206] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different positions in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0207] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Therefore, aspects of this application can be implemented entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. All of the above hardware or software can be referred to as a “unit,” “module,” or “system.” Furthermore, aspects of this application can take the form of a computer program product embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0208] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, and Python; general programming languages ​​such as C; Visual Basic, Fortran2103, Perl, COBOL2102, PHP, and ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0209] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although some currently considered useful embodiments of the invention have been discussed in the foregoing disclosure by way of various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a purely software solution, such as an installation on an existing server or mobile device.

[0210] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this approach of the present application should not be construed as reflecting an intention that the claimed subject matter requires more features than expressly recited in each claim. Rather, the subject of the invention should possess fewer features than in any single embodiment described above.

Claims

1. A pre-warning method for a radial artery compression hemostat based on a multi-sensor device, characterized in that, include: S1. Simultaneously acquire multiple types of physiological signals from the radial artery compression area and perform digital processing to obtain digital pressure signals, proximal temperature, distal temperature, pulse signals, and electrode impedance signals; S2. Perform discrete wavelet transform on the proximal and distal temperatures to remove motion artifacts, obtain the denoised temperature signals, and calculate the temperature difference signal between the two points to obtain the temperature difference signal. Time-domain analysis of the pulse signal was performed to extract the pulse amplitude; S3. Construct a temperature distribution model of the compression region based on temperature difference signals: Data expansion: Virtual temperature values ​​of grid points are generated in the compression zone using bilinear interpolation to supplement the spatial dimension of the single-point temperature difference signal; Surface fitting: Perform quadratic polynomial surface fitting on the virtual temperature value to establish the temperature distribution surface equation. After solving the polynomial coefficients, calculate the temperature gradient vector to reflect the rate and direction of temperature change in the plane. S4. Reconstruct the resistivity distribution of the compression area using electrode impedance signals to locate the bleeding site: Impedance data processing: Baseline correction is performed on the electrode impedance signal, the impedance change relative to the initial time is calculated, and the impedance change is converted into a voltage change. Finite element modeling: The compression region is divided into multiple triangular elements. The voltage change is used as the input of the finite element method. A linear relationship between the measured voltage and the element conductivity is established. The element conductivity is updated by the Landweber iteration method. Cubic spline interpolation and surface fitting are performed on the element resistivity to generate a continuous resistivity distribution. Bleeding feature extraction: Based on the continuous resistivity distribution, the bleeding index is calculated, and the coordinates of the grid point corresponding to the minimum resistivity value are used as the coordinates of the bleeding center point. S5. Calculate the rate of pressure change based on digital pressure signals; Calculate the pulse amplitude attenuation rate based on pulse amplitude; A comprehensive risk value is obtained by integrating the pressure change rate, temperature gradient vector, pulse amplitude decay rate, and bleeding index using a weighted summation formula. Early warning is issued based on the comprehensive risk value, and the pressure adjustment amount is calculated based on the comprehensive risk value. The pressure is then adjusted based on the pressure adjustment amount.

2. The early warning method for a multi-sensor-based radial artery compression hemostat according to claim 1, characterized in that, In the acquisition of electrode impedance signals, a dual-ring electrode impedance array is used, specifically: the number of electrodes in the inner and outer rings is the same, the spacing between the inner ring electrodes is 2mm, and the spacing between the outer ring electrodes is 5mm; a four-wire impedance measurement method is used, and the impedance values ​​between the electrodes are acquired by a timed scan with a period of 1 second, wherein the outer ring electrode is used as the excitation electrode, and the electrodes of the inner and outer rings together serve as the detection electrode.

3. The early warning method for a multi-sensor-based radial artery compression hemostat according to claim 1, characterized in that, The specific steps for calculating the temperature difference are as follows: use the db4 wavelet basis to perform a 3-level decomposition on the proximal and distal temperature signals to remove high-frequency noise, and subtract the denoised proximal and distal temperature signals to obtain the temperature difference signal.

4. The early warning method for a multi-sensor-based radial artery compression hemostat according to claim 1, characterized in that, The steps for calculating the temperature gradient vector include: generating virtual temperature values ​​for 10×10 grid points in the compression region using bilinear interpolation; performing quadratic polynomial surface fitting on the grid point temperature data; and calculating the temperature gradients in the x-axis and y-axis directions based on the fitted surface equations to form the temperature gradient vector.

5. The early warning method for a multi-sensor-based radial artery compression hemostat according to claim 1, characterized in that, The pressure adjustment amount is calculated based on the comprehensive risk value, and the pressure adjustment amount is limited to the range of -10kPa to +10kPa. The adjusted pressure is then fed back to the signal acquisition step as the new pressure value for the next round of monitoring.

6. The early warning method for a multi-sensor-based radial artery compression hemostat according to claim 1, characterized in that, The formula for fitting a quadratic polynomial surface is as follows: T surf (x,y,y)=c0+c1x+c2y+c3x 2 +c4xy+c5y 2 ; T surf (x,y,t): The surface fitting value of the temperature field at time t and coordinates (x,y); (x,y): Two-dimensional planar coordinates of the compression region, with the center of compression as the origin, the x-axis representing the direction of the radial artery, and the y-axis representing the direction perpendicular to the artery; c0: A constant term, representing the baseline temperature value at the origin (0,0); c1, c2: Linear coefficients, representing the linear rates of change of temperature along the x-axis and y-axis, respectively; c3, c4, c5: Quadratic coefficients, representing the linear rates of change of temperature along the x-axis and y-axis, respectively. 2 xy, y 2 Nonlinear rate of change of direction; By fitting the temperature distribution using a quadratic polynomial, the temperature gradient and curvature changes within the compression area are captured.

7. The early warning method for a multi-sensor-based radial artery compression hemostat according to claim 6, characterized in that, The coefficients c0, c1, c2, c3, c4, and c5 are solved using the least squares method. The objective function is to minimize the mean square error between the fitted value and the interpolated temperature value. Where (x) i ,y i ) represents the coordinates of the grid point, T int (x i ,y i (x, t) represents the coordinates at time t. i ,y i The virtual temperature value at ().

8. The early warning method for a multi-sensor-based radial artery compression hemostat according to claim 7, characterized in that, Based on T surf The temperature gradient vector is obtained by differentiating (x, y, t). The direction that reflects the most drastic temperature change.

9. A warning system for a radial artery compression hemostat based on a multi-sensor device, characterized in that, The early warning method for the multi-sensor-based radial artery compression hemostat according to any one of claims 1 to 8, wherein the early warning system comprises: Multi-sensor-based radial artery compression hemostat; The data acquisition module is used to simultaneously acquire and digitally process multiple types of physiological signals in the radial artery compression area using a multi-sensor-based radial artery compression hemostat to obtain pressure digital signals, proximal temperature, distal temperature, pulse signals, and electrode impedance signals. The data processing module is used to perform discrete wavelet transform on the proximal and distal temperatures to remove motion artifacts and obtain denoised temperature signals, and to calculate the temperature difference signal between the two points; it also performs time-domain analysis on the pulse signal to extract the pulse amplitude. The temperature modeling module is used to construct a temperature distribution model of the compression area based on the temperature difference signal; The bleeding calculation module is used to reconstruct the resistivity distribution of the compression area through electrode impedance signals and locate the bleeding site. The early warning and pressure regulation module is used for: The pressure change rate is calculated based on the digital pressure signal; the pulse amplitude decay rate is calculated based on the pulse amplitude; the pressure change rate, temperature gradient vector, pulse amplitude decay rate, and bleeding index are fused by a weighted summation formula to obtain a comprehensive risk value; an early warning is issued based on the comprehensive risk value, and the pressure adjustment amount is calculated based on the comprehensive risk value, and the compression pressure is adjusted based on the pressure adjustment amount.

10. The early warning system for a multi-sensor-based radial artery compression hemostat according to claim 9, characterized in that, The multi-sensor-based radial artery compression hemostat includes: The main body of the compression hemostat has wrist straps symmetrically arranged on both sides; A pressure device is located at the top of the main body of the compression hemostat; A pressure block is located at the bottom of the main body of the compression hemostat and is connected to the pressure device; A pressure sensing module is arranged on the pressure surface of the pressure block; The temperature monitoring unit has two temperature sensors symmetrically arranged at the proximal and distal ends of the pressure surface of the pressure block; A photoelectric pulse module is arranged at the edge of the pressure surface of the pressure block; The bleeding detection module has multiple electrodes arranged in a multi-ring pattern on the pressure surface of the pressure block.

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