Finger pulse oximetry optical signal simulation method, test method and system based on dynamic pressure feedback

Abstract

The application relates to a finger pulse oxygen optical signal simulation method, a test method and a system based on dynamic pressure feedback. The application introduces dynamic pressure feedback, adopts unequal attenuation constants to perform differential amplitude attenuation modulation on red light and infrared light channels, solves the problem that an output signal of an existing simulator is too idealized, and improves the accuracy of pulse blood oxygen meter test and calibration.

Description

Technical Field

[0001] This application relates to the field of pulse oximeter simulator technology, and in particular to a method, testing method and system for simulating, testing, and testing the optical signal of pulse oximeter based on dynamic pressure feedback. Background Technology

[0002] Pulse oximeters are essential medical devices for clinical monitoring of blood oxygen saturation and heart rate, while pulse oximeter simulators are core instruments used for the research, testing, and calibration of such devices. Existing pulse oximeter simulators typically generate and output photoplethysmography (PPG) signals directly based on preset blood oxygen saturation and heart rate values. In actual physiological measurement scenarios, the physical contact pressure inevitably arises when the pulse oximeter probe is held between the fingertips. This pressure can cause deformation or even ischemia of the capillary bed. Under this pressure, not only does the amplitude of the PPG signal attenuate, accompanied by phase delay and noise interference, but also, due to changes in the optical absorption and scattering characteristics of tissues, the optical path attenuation rate of light signals of different wavelengths (such as red light and infrared light) penetrating the compressed blood vessels varies significantly.

[0003] However, existing pulse oximeter simulators often scale the light signals of various wavelengths at a uniform ratio when outputting signals, ignoring the attenuation characteristics of different wavelengths of light signals under pressure. This results in the optical signals output by existing simulators being overly idealized, differing from the physiological optical performance under real physical pressure conditions, and consequently leading to lower accuracy in testing and calibrating pulse oximeters. Summary of the Invention

[0004] In view of this, in order to at least partially improve the above-mentioned problems, this application provides a method, test method and system for simulating finger pulse oxygen optical signals based on dynamic pressure feedback.

[0005] This application provides a method for simulating optical signals of pulse oximetry based on dynamic pressure feedback, applied to a pulse oximeter simulator, including: The pressure vector is collected in real time from the pressure sensor array deployed on the surface of the simulation model of the pulse oximeter simulator, and the equivalent center pressure value is obtained based on the pressure vector. The target heart rate and target blood oxygen saturation values ​​are obtained to synthesize the reference photoplethysmography pulse wave sequences for the red and infrared light channels. Based on the equivalent center pressure value, the red light AC amplitude attenuation coefficient of the red light channel, the infrared light AC amplitude attenuation coefficient of the infrared light channel, the dynamic phase delay parameter, and the environmental artifact noise sequence are determined respectively; wherein, the red light AC amplitude attenuation coefficient is calculated using a first contact pressure attenuation constant, the infrared light AC amplitude attenuation coefficient is calculated using a second contact pressure attenuation constant, and the first contact pressure attenuation constant and the second contact pressure attenuation constant are not equal; Based on the red light AC amplitude attenuation coefficient, the infrared light AC amplitude attenuation coefficient, the dynamic phase delay parameter, and the environmental artifact noise sequence, the reference photoplethysmography pulse wave sequence is modulated to obtain a red light discrete digital sequence and an infrared light discrete digital sequence containing dynamic pressure distortion characteristics. The red light discrete digital sequence and the infrared light discrete digital sequence are converted into analog voltage signals by a digital-to-analog converter in the pulse oximeter simulator, and the light-emitting diodes on the pulse oximeter simulator are driven based on the analog voltage signals so that the light-emitting diodes output analog optical signals containing dynamic pressure distortion characteristics.

[0006] In one embodiment, the step of acquiring the pressure vector output in real time by the pressure sensor array deployed on the surface of the simulation model of the pulse oximeter simulator, and obtaining the equivalent central pressure value based on the pressure vector, includes: The pressure vector output by the pressure sensor array deployed on the surface of the simulation model is acquired in real time, and the physical space coordinates of each pressure sensor in the pressure sensor array are obtained. Using a two-dimensional interpolation algorithm, a two-dimensional pressure distribution function is constructed based on the pressure vector and the physical space coordinates corresponding to each pressure sensor. Obtain the effective contact area between the pulse oximeter probe and the simulation model, and calculate the average area integral of the two-dimensional pressure distribution function within the effective contact area. Use the average area integral as the equivalent central pressure value.

[0007] In one embodiment, the step of acquiring the set target heart rate value and target blood oxygen saturation value to synthesize the reference photoplethysmogram sequence for the red light channel and infrared light channel includes: The target heart rate and target blood oxygen saturation values ​​are obtained, and the reference AC component amplitudes of the red light channel and infrared light channel are determined based on the target blood oxygen saturation values, respectively. Based on the target heart rate value and the reference AC component amplitude, the systolic and diastolic waves of the red light channel and the infrared light channel in a single pulse cycle are synthesized using a dual Gaussian kernel function, and a single-cycle reference waveform function is obtained based on the systolic and diastolic waves. The single-cycle reference waveform function is continuously extended and discretized according to the time period corresponding to the target heart rate value to generate the reference photoplethysmography pulse wave sequence of the red light channel and the infrared light channel.

[0008] In one embodiment, determining the red light AC amplitude attenuation coefficient of the red light channel, the infrared light AC amplitude attenuation coefficient of the infrared light channel, the dynamic phase delay parameter, and the environmental artifact noise sequence based on the equivalent center pressure value includes: Call the pre-calibrated first and second contact pressure attenuation constants; Based on the equivalent center pressure value, the red light AC amplitude attenuation coefficient is calculated in combination with the first contact pressure attenuation constant, and the infrared light AC amplitude attenuation coefficient is calculated in combination with the second contact pressure attenuation constant; and the first contact pressure attenuation constant is not equal to the second contact pressure attenuation constant. Calculate the rate of change of the equivalent center pressure value over time, and determine the dynamic phase delay parameter based on the rate of change; Determine the pressure range to which the equivalent center pressure value belongs, and call the corresponding noise coupling model according to the pressure range to generate the environmental artifact noise sequence.

[0009] In one embodiment, the modulation of the reference photoplethysmography sequence based on the red light AC amplitude attenuation coefficient, the infrared light AC amplitude attenuation coefficient, the dynamic phase delay parameter, and the environmental artifact noise sequence to obtain a red light discrete digital sequence and an infrared light discrete digital sequence containing dynamic pressure distortion characteristics includes: The reference AC waveform data in the reference photoplethysmography sequences of the red light channel and the infrared light channel are obtained respectively. The dynamic phase delay parameter is converted into a corresponding data point offset, and the reference AC waveform data is shifted and modulated in the direction of time increase based on the data point offset to obtain the shifted red light AC waveform data and infrared light AC waveform data. Based on the red light AC amplitude attenuation coefficient and the infrared light AC amplitude attenuation coefficient, the AC amplitude corresponding to each discrete data point in the translated red light AC waveform data and the translated infrared light AC waveform data is subjected to multiplicative attenuation modulation to obtain the attenuated red light AC waveform data and infrared light AC waveform data. Based on the set target blood oxygen saturation value, the reference DC components corresponding to the red light channel and the infrared light channel are determined respectively; The attenuated red light AC waveform data and the attenuated infrared light AC waveform data are respectively added and superimposed with the corresponding reference DC component, and the environmental artifact noise sequence is mixed in point by point according to the time series index to generate the red light discrete digital sequence and the infrared light discrete digital sequence containing dynamic pressure distortion characteristics.

[0010] In one embodiment, the step of converting the red light discrete digital sequence and the infrared light discrete digital sequence into analog voltage signals via a digital-to-analog converter within the pulse oximeter simulator, and driving a light-emitting diode on the pulse oximeter simulator based on the analog voltage signals, so that the light-emitting diode outputs an analog optical signal containing dynamic pressure distortion characteristics, includes: The generated red light discrete digital sequence and infrared light discrete digital sequence containing dynamic pressure distortion characteristics are synchronously input into the digital-to-analog converter in the pulse oximeter simulator according to the preset system sampling rate, and converted into the corresponding red light analog voltage signal and infrared light analog voltage signal. Based on the red light analog voltage signal and the infrared light analog voltage signal, the driving current flowing through the corresponding light-emitting diode is controlled by the analog driving circuit, so that the red light-emitting diode and the infrared light-emitting diode on the pulse oximeter simulator output the analog optical signal containing dynamic pressure distortion characteristics.

[0011] This application also provides a pulse oximeter testing method that applies the dynamic pressure feedback-based finger pulse oximetry optical signal simulation method as described in any of the preceding claims, comprising: The pulse oximeter to be tested is clamped onto the simulation model of the pulse oximeter simulator, so that the pulse oximeter to be tested applies contact pressure to the surface of the simulation model, and the optical receiving end of the pulse oximeter to be tested is aligned with the light-emitting diode on the pulse oximeter simulator. The pulse oximeter simulator executes the dynamic pressure feedback-based finger pulse oximetry optical signal simulation method in real time according to the collected contact pressure, and outputs the simulated optical signal containing dynamic pressure distortion characteristics to the pulse oximeter under test. The pulse oximeter to be tested receives the simulated optical signal and performs calculations based on the simulated optical signal to output the measured heart rate value and the measured blood oxygen saturation value. The measured heart rate value and the measured blood oxygen saturation value are obtained, and their errors are compared with the target heart rate value and the target blood oxygen saturation value set in the pulse oximeter simulator, respectively, to evaluate the measurement accuracy and robustness of the pulse oximeter under dynamic pressure interference.

[0012] This application also provides a finger pulse oxygen optical signal simulation system based on dynamic pressure feedback, including: The biomimetic physical interaction unit includes a silicone base and a thin-film pressure sensor array disposed on the surface of the silicone base, which is used to sense external contact and output pressure vector in real time. The processing and driving unit is communicatively connected to the thin-film pressure sensor array, for acquiring the pressure vector and executing the dynamic pressure feedback-based finger pulse oxygen optical signal simulation method as described in any of the preceding items, to output a dynamic driving current for exciting the simulated optical signal. A hardware dual-closed-loop self-calibration unit is integrated inside the silicone base, including a light-emitting diode and a miniature photodetector. The light-emitting diode receives the dynamic driving current and outputs an analog optical signal. The miniature photodetector is positioned next to the light-emitting diode (LED) and configured to capture only the backscattered light emitted by the LED itself, converting it into a measured feedback waveform and sending it to the processing and driving unit. The processing and driving unit compares the residual between the theoretically generated reference waveform and the measured feedback waveform, and uses a proportional-integral-derivative (PID) control algorithm to dynamically compensate for the dynamic driving current it outputs. This compensated dynamic driving current then eliminates the wavelength distortion caused by thermal drift of the LED.

[0013] This application also provides a computer device including a memory and a processor, the memory storing a computer application program, and the processor executing the computer application program, wherein the computer application program is configured to perform the method as described in any of the preceding claims when executed by the processor.

[0014] This application also provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the preceding claims.

[0015] The beneficial effects of this application are as follows: This application obtains the equivalent center pressure value by arranging a pressure sensor array on the surface of a pulse oximeter simulator, calculates the AC amplitude attenuation coefficients of the red and infrared light channels using unequal contact pressure attenuation constants, and modulates the reference photoplethysmography pulse wave sequence by combining dynamic phase delay and environmental artifact noise. This method solves the problem of existing simulators uniformly scaling signals of various wavelengths while ignoring pressure attenuation characteristics. This results in the simulated optical signal output by the LED possessing differentiated attenuation characteristics of different wavelengths under pressure, thereby improving the accuracy of pulse oximeter testing and calibration. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart illustrating a method for simulating the optical signal of the finger pulse based on dynamic pressure feedback, according to one embodiment of this application.

[0017] Figure 2 This is a schematic flowchart of a pulse oximeter testing method based on a dynamic pressure feedback-based optical signal simulation method for pulse oximetry, according to one embodiment of this application.

[0018] Figure 3 This is a schematic diagram of the structure of a finger pulse oxygen optical signal simulation system based on dynamic pressure feedback according to an embodiment of this application.

[0019] Figure 4 This is a block diagram of a computer device according to an embodiment of this application.

[0020] Figure 5 This is a block diagram of a readable storage medium according to an embodiment of this application. Detailed Implementation

[0021] The terms "first," "second," and "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the term "comprising," and any variations thereof, is intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to these processes, methods, products, or apparatuses.

[0022] Please see Figure 1-5 One embodiment of this application provides a method for simulating the optical signal of pulse oximeter based on dynamic pressure feedback, which is applied to a pulse oximeter simulator. The pulse oximeter simulator includes at least a simulation model, a pressure sensor array disposed on the surface of the simulation model, a processor (e.g., a microcontroller MCU or a digital signal processor DSP), a digital-to-analog converter (DAC), and a light-emitting diode.

[0023] It should be noted that before the pulse oximeter is shipped or during the research and development phase, its accuracy needs to be tested. The pulse oximeter simulator is used to calibrate and test its accuracy. In a real human measurement scenario, when the clamping component of the pulse oximeter clamps a human finger, its light-emitting component (such as an LED) emits a light signal that penetrates the finger. The receiver on the pulse oximeter (such as a photodiode or photodetector) receives the remaining light signal after penetrating the finger. The pulse oximeter analyzes the received signal to calculate the human's blood oxygen saturation and heart rate. The pulse oximeter simulator of this application, however, actively outputs simulated optical signals with specific characteristics to the receiver to perform closed-loop testing instead of a real pressure finger.

[0024] The method includes: S1. Collect the pressure vector output in real time by the pressure sensor array deployed on the surface of the simulation model of the pulse oximeter simulator, and obtain the equivalent center pressure value based on the pressure vector.

[0025] As described above, the pressure vectors output in real time by the pressure sensor array on the surface of the simulation model are first acquired. The simulation model can be a bionic silicone finger. When the probe of the pulse oximeter is clamped onto the bionic silicone finger, contact pressure is generated. The pressure sensor array on the surface of the bionic silicone finger can sense this contact pressure in real time and output pressure vectors corresponding to different spatial positions on the contact surface of the simulation model. The processor inside the pulse oximeter simulator receives this pressure vector and converts it into an equivalent central pressure value that characterizes the core pressure force of the overall contact surface of the simulation model through a spatial interpolation algorithm, such as two-dimensional biharmonic spline interpolation. This transforms distributed discrete pressure points into a single concentrated pressure point and enables real-time sensing of external pressure.

[0026] S2. Obtain the set target heart rate value and target blood oxygen saturation value to synthesize the reference photoplethysmography pulse wave sequence of the red light channel and infrared light channel.

[0027] As described above, the processor can obtain the target heart rate and target blood oxygen saturation values ​​set by the user through the human-computer interaction interface or the external communication interface, thereby generating ideal pulse waveform data, namely the reference photoplethysmography pulse wave sequence of the red light channel and the infrared light channel, without being affected by the pressure interference of the probe of the external pulse oximeter. This reference photoplethysmography pulse wave sequence is an ideal reference waveform.

[0028] S3. Based on the equivalent center pressure value, determine the red light AC amplitude attenuation coefficient of the red light channel, the infrared light AC amplitude attenuation coefficient of the infrared light channel, the dynamic phase delay parameter, and the environmental artifact noise sequence; wherein, the red light AC amplitude attenuation coefficient is calculated using a first contact pressure attenuation constant, the infrared light AC amplitude attenuation coefficient is calculated using a second contact pressure attenuation constant, and the first contact pressure attenuation constant and the second contact pressure attenuation constant are not equal.

[0029] As mentioned above, when the pulse oximeter is used to test a user's blood oxygen level, the pressure on the finger during the actual process of the probe clamping the finger can cause deformation or collapse of the capillary bed, resulting in local compression and ischemia. This causes the generated light signal to not only be severely attenuated in amplitude, but also to have phase delay due to blood displacement and artifact noise introduced by muscle deformation. Therefore, the light signal will be distorted, which means that the photoelectric signal cannot truly reflect the real state under the actual clamping environment. Based on this, after obtaining the equivalent center pressure value, this application redetermines the red light AC amplitude attenuation coefficient of the red light channel, the infrared light AC amplitude attenuation coefficient of the infrared light channel, the dynamic phase delay parameter, and the environmental artifact noise sequence based on the equivalent center pressure value. The red light AC amplitude attenuation coefficient is a proportional factor characterizing the attenuation degree of the pulse AC component in the red light band under pressure, mainly used for dynamic compression adjustment of the pulsation amplitude of the red light channel in the reference photoplethysmography pulse wave sequence. The infrared light AC amplitude attenuation coefficient is a proportional factor characterizing the attenuation degree of the pulse AC component in the infrared light band under the same pressure, mainly used for independent dynamic compression adjustment of the pulsation amplitude of the infrared light channel in the reference photoplethysmography pulse wave sequence. In practical applications, due to being pinched, the finger has compressed and ischemic tissue. Because a large amount of blood is displaced, the composition of the medium through which light penetrates changes, resulting in asymmetrical changes in the scattering and absorption rates of different wavelengths of light. For example, the contact pressure attenuation constant of red light in ischemic tissue under pressure is approximately 0.045 kPa. -1 The attenuation constant of infrared light is approximately 0.032 kPa. -1Therefore, if equal contact pressure attenuation constants are used, the physical ratio (R value) of the generated dual-wavelength signal will deviate from the variation law under actual pressure. Based on this, this application uses unequal first and second contact pressure attenuation constants for different wavelengths of light, so that the red light AC amplitude attenuation coefficient and the infrared light AC amplitude attenuation coefficient calculated based on the first and second contact pressure attenuation constants more accurately reflect the independent optical attenuation characteristics under pressure, and greatly improve the reproduction of the real physical pressure-induced distortion phenomenon. The dynamic phase delay parameter refers to the time lag caused by the sudden pressure of the clamp causing local blood to be squeezed out of the measurement area. The environmental artifact noise sequence refers to the high-frequency white noise or low-frequency baseline drift and other interference signals that accompany different pressure ranges (such as loose fitting or severe pressure). By determining the dynamic phase delay parameter and the environmental artifact noise sequence, the single amplitude distortion can be extended to the time domain and noise domain, constructing a full-dimensional physical forced distortion feature.

[0030] S4. Based on the red light AC amplitude attenuation coefficient, the infrared light AC amplitude attenuation coefficient, the dynamic phase delay parameter, and the environmental artifact noise sequence, the reference photoplethysmography pulse wave sequence is modulated to obtain a red light discrete digital sequence and an infrared light discrete digital sequence containing dynamic pressure distortion characteristics.

[0031] As described above, the processor modulates the reference photoplethysmography sequence based on the red light AC amplitude attenuation coefficient, the infrared light AC amplitude attenuation coefficient, the dynamic phase delay parameter, and the environmental artifact noise sequence. By applying the attenuation coefficient, introducing the phase delay, and superimposing artifact noise, the processor ultimately generates a data stream with physically forced distortion attributes in the digital domain, namely the red light discrete digital sequence and the infrared light discrete digital sequence containing dynamic pressure distortion characteristics.

[0032] S5. The red light discrete digital sequence and the infrared light discrete digital sequence are converted into analog voltage signals by a digital-to-analog converter in the pulse oximeter simulator, and the light-emitting diodes on the pulse oximeter simulator are driven based on the analog voltage signals so that the light-emitting diodes output analog optical signals containing dynamic pressure distortion characteristics.

[0033] As described above, the processor transmits the aforementioned discrete digital sequences of red light and infrared light to a digital-to-analog converter (DAC). The DAC converts these discrete digital sequences into a continuously fluctuating analog voltage signal. This analog voltage signal then serves as a driving source, directly driving the light-emitting diodes (typically including red and infrared LEDs) to emit light. The analog optical signal output by the LEDs not only carries the set physiological information of blood oxygen and heart rate but also accurately reproduces the optical attenuation, delay, and noise distortion (i.e., dynamic pressure distortion characteristics) caused by the physical pressure of the actual probe clamping. In other words, this analog optical signal is equivalent to the transmitted light signal after penetrating a real human finger under pressure and can be received by the pulse oximeter being tested. This avoids the output analog optical signal being overly idealized, improving the accuracy of testing and calibrating the pulse oximeter.

[0034] In one embodiment, step S1, which involves acquiring the pressure vector output in real time by the pressure sensor array deployed on the surface of the simulation model of the pulse oximeter simulator and obtaining the equivalent central pressure value based on the pressure vector, includes: S101. Real-time acquisition of the pressure vector output by the pressure sensor array deployed on the surface of the simulation model of the pulse oximeter simulator, and acquisition of the physical space coordinates of each pressure sensor in the pressure sensor array. For example, the simulation model of the pulse oximeter simulator includes at least one bionic silicone finger for testing. The pressure sensor array is a unified sensor network, composed of multiple thin-film pressure sensors distributed on the surface of the bionic silicone finger (e.g., at anatomical structures such as the fingertip and sidewalls). When the probe of the pulse oximeter being tested clamps the bionic silicone finger, it generates uneven physical contact pressure on the bionic silicone finger. The processor synchronously reads the transient pressure values ​​detected by each thin-film pressure sensor in the pressure sensor array to form a pressure vector {P1, P2, ..., P...}. n Simultaneously, the processor reads the physical spatial coordinates (x, y, x) of each thin-film pressure sensor in the pressure sensor array, which are pre-stored in the internal storage medium, representing the bionic silicone finger. i y i ).

[0035] S102. Based on the two-dimensional interpolation algorithm, construct a two-dimensional pressure distribution function according to the pressure vector and physical space coordinates; For example, since the vascular displacement caused by the actual probe clamping is a continuous physical deformation process, while the sensor array only collects discrete data points, a two-dimensional pressure distribution function P(x, y) that reflects the continuous pressure state of the entire fingertip contact surface can be constructed by performing smooth surface fitting in the digital domain based on a two-dimensional interpolation algorithm, according to the physical spatial coordinates and the corresponding discretized pressure vector. The two-dimensional interpolation algorithm can be a two-dimensional biharmonic spline interpolation algorithm.

[0036] S103. Obtain the effective contact area between the probe of the pulse oximeter being tested and the simulation model, and calculate the average area integral of the two-dimensional pressure distribution function within the effective contact area to obtain the equivalent central pressure value.

[0037] For example, after obtaining the two-dimensional pressure distribution function representing the continuously compressed surface, in order to convert the distributed pressure into a single physical parameter that can be used to control the distortion and attenuation of the optical signal, the processor calls the pre-stored or calibrated actual stress area covered by the light-emitting probe, i.e., the effective contact area, and uses the effective contact area as the integration region to calculate the equivalent central pressure value P in combination with the two-dimensional pressure distribution function. eq The calculation formula is as follows: ; Where S represents the effective contact area, P(x, y) is a two-dimensional pressure distribution function that reflects the local dynamic pressure at each spatial coordinate point on the pressure surface, x and y are two-dimensional continuous spatial coordinate variables on the contact surface of the simulation model, and dx and dy are differential elements in the horizontal and vertical coordinate directions. The product of the two forms the differential area.

[0038] In one embodiment, in order to construct an ideal reference photoplethysmography (PPG) sequence in the digital domain that is not affected by pressure, step S2, which involves acquiring a set target heart rate value and a target blood oxygen saturation value to synthesize a reference PPG sequence for the red and infrared light channels, includes: S201. Obtain the set target heart rate value and target blood oxygen saturation value, and determine the reference DC component and reference AC component amplitude of the red light channel and infrared light channel respectively based on the target blood oxygen saturation value.

[0039] Specifically, the processor receives the target heart rate and target blood oxygen saturation values ​​set by the user through a human-machine interface or an external communication interface. Based on the target blood oxygen saturation value, it obtains the reference AC / DC ratio between the red light channel and the infrared light channel under ideal, pressure-free conditions (i.e., without interference from physical clamping deformation of the probe). Based on this reference AC / DC ratio, the processor determines (initializes) the reference DC component and reference AC component amplitudes of the red light channel and the infrared light channel in its internal registers, respectively. The reference DC component represents the constant absorption of light by static human tissue, and the reference AC component amplitude represents the dynamic light absorption fluctuation amplitude caused by changes in target blood volume due to heartbeats.

[0040] S202. Based on the target heart rate value and the reference AC component amplitude, the systolic and diastolic waves of the red light channel and the infrared light channel in a single pulse cycle are synthesized using a dual Gaussian kernel function, and a single-cycle reference waveform function is obtained based on the systolic and diastolic waves.

[0041] For example, the human pulse wave is composed of the main wave (systolic wave) generated by ventricular ejection and the dilatational wave (diastolic wave) generated by peripheral vascular reflection. Therefore, in this embodiment, the dual Gaussian kernel function called by the processor synthesizes the ideal waveform within a single pulse cycle for the red light channel and the infrared light channel, respectively.

[0042] The double Gaussian kernel function is: ; Wherein, x(t1) represents the amplitude of the single-cycle reference waveform function that changes continuously with time; t1 is the time variable within a single pulse cycle, and its value range is constrained by the single-cycle duration corresponding to the target heart rate value (discretely incremented according to the system sampling step size during digital domain generation); a1 represents the amplitude parameter of the systolic wave (main wave), and its value is constrained by the amplitude of the initialized reference AC component, representing the light absorption conversion amount corresponding to the maximum blood volume during cardiac systole; exp is the symbol of an exponential function with the natural constant e as the base; b1 represents the center time position parameter of the systolic wave, and its time period span is constrained by the target heart rate value; c1 represents the morphological width parameter of the systolic wave; a2 represents the amplitude parameter of the diastolic wave (reflected wave), representing the secondary influence of the blood volume reflected back from peripheral blood vessels on light absorption; b2 represents the center time position parameter of the diastolic wave; c2 represents the morphological width parameter of the diastolic wave, which, together with the time difference between b1 and b2, determines the depth and time position of the dicrotic notch in the pulse wave.

[0043] S203. The single-cycle reference waveform function is continuously extended and discretized according to the time period corresponding to the target heart rate value to generate the reference photoplethysmography pulse wave sequence of the red light channel and the infrared light channel.

[0044] For example, after obtaining the single-cycle reference waveform function, the processor calculates the time period step T of adjacent pulse waves based on the acquired target heart rate value using the formula T=60 / HR, where HR represents the target heart rate value. Subsequently, the processor continuously extends x(t) obtained by the above double Gaussian kernel function formula along the time axis with a period of T, and discretizes it according to a preset system sampling rate, thereby generating a multi-cycle reference photoplethysmography pulse wave sequence of the red light channel and infrared light channel in the digital domain, which does not contain any physically forced distortion attributes.

[0045] In one embodiment, in order to fully reproduce the optical attenuation, phase delay, and contact coupling noise caused by real physical pressure in the digital domain, step S3, which determines the red light AC amplitude attenuation coefficient of the red light channel, the infrared light AC amplitude attenuation coefficient of the infrared light channel, the dynamic phase delay parameter, and the environmental artifact noise sequence based on the equivalent center pressure value, includes: S301. Obtain the pre-calibrated first contact pressure attenuation constant and second contact pressure attenuation constant, and calculate the red light AC amplitude attenuation coefficient based on the equivalent center pressure value and the first contact pressure attenuation constant; calculate the infrared light AC amplitude attenuation coefficient based on the second contact pressure attenuation constant; and the first contact pressure attenuation constant is not equal to the second contact pressure attenuation constant.

[0046] For example, when the bionic silicone finger is physically clamped by the probe, its internal equivalent capillary bed is compressed, and blood is expelled from the optical path measurement area. Since the scattering and absorption rates of non-blood-bearing tissues, such as muscle and fat, vary asymmetrically for different wavelengths of light, a uniform attenuation ratio cannot be used. In this embodiment, the processor first reads the pre-calibrated attenuation constant for the optical characteristics of the simulation model, and then calculates the attenuation coefficients for the red and infrared light channels respectively. The calculation formula is as follows: K red =exp(-α red ·P eq ); K ir =exp(-α ir ·P eq ); Among them, K red α represents the red light AC amplitude attenuation coefficient. redThe first contact pressure attenuation constant being invoked is preferably 0.045 kPa in this embodiment. -1 , representing the specific optical attenuation rate of red light in ischemic tissue under pressure. K ir α represents the amplitude attenuation coefficient of the infrared light AC. ir The second contact pressure attenuation constant being invoked is preferably 0.032 kPa in this embodiment. -1 , representing the specific optical attenuation rate of infrared light under the same pressure conditions. Among them, the red light AC amplitude attenuation coefficient and the infrared light AC amplitude attenuation coefficient are both dimensionless scaling factors between [0,1].

[0047] Through α red ≠α ir This allows the calculated dual-wavelength attenuation coefficient to accurately fit the nonlinear shift of the physical dual ratio (R value) under real pressure conditions, thereby solving the technical problem of blood oxygen calibration distortion caused by the use of proportional attenuation in existing simulators.

[0048] S302. Calculate the rate of change of the equivalent center pressure value over time, and determine the dynamic phase delay parameter based on the rate of change.

[0049] For example, when the probe clamps or applies dynamic pressure, the expulsion of local blood from the measurement area is not instantaneous but occurs with a time lag. Therefore, the processor performs time differentiation on the real-time acquired equivalent central pressure value to obtain the pressure change rate. The processor compares the absolute value of this pressure change rate with a preset dynamic trigger threshold (e.g., 50 kPa / s). When the absolute value of the pressure change rate is greater than the dynamic trigger threshold, the processor generates the corresponding dynamic phase delay parameter ΔΦ according to a preset mapping function; when the pressure tends to stabilize (the change rate is lower than or equal to the threshold), the delay parameter gradually decays to zero.

[0050] Specifically: ① When the absolute value of the pressure change rate is greater than the dynamic trigger threshold, the preset linear mapping function is: ; Wherein, k is the pre-calibrated fluid dynamic hysteresis coefficient corresponding to the existence of time lag. It should be noted that the dynamic trigger threshold and calibration coefficient k are obtained in advance and stored in the processor through previous experimental methods. That is, the empirical parameters are extracted by synchronously collecting real photoelectric signals and contact pressure data of real human fingers when subjected to step dynamic pressing, and then fitting the data.

[0051] ② When the absolute value of the pressure change rate is not greater than the dynamic trigger threshold, in order to simulate the slow physical recovery process of capillary bed blood return after compression stops, the processor uses a first-order exponential decay model to gradually reduce the delay parameter to zero. The specific formula is as follows: ΔΦ(t2)=ΔΦ0·exp(- t2 / τ); Wherein, ΔΦ(t2) represents the dynamic phase delay parameter accumulated over time variable t2; ΔΦ0 is the initial delay retained at the instant when the pressure just tends to stabilize, which is equal to the last dynamic phase delay parameter ΔΦ calculated by the linear mapping function when the pressure change rate just drops to the dynamic trigger threshold, thereby ensuring the continuity of the algorithm during state switching and avoiding abrupt changes in the simulated waveform; τ is the time constant characterizing the elastic recovery of the vascular bed, and t2 is the accumulated time variable after the absolute value of the change rate drops to or below the dynamic trigger threshold.

[0052] S303. Determine the pressure range to which the equivalent center pressure value belongs, and call the corresponding noise coupling model according to the pressure range to generate the environmental artifact noise sequence.

[0053] For example, the processor compares the equivalent central pressure value acquired in real time with an internally preset pressure range threshold: ① When the equivalent center pressure value is in the first interval (e.g., the low-pressure loose interval, < 10 kPa), it indicates that the probe is loosely fitted, and ambient light can easily leak in and relative slippage can easily occur. At this time, the processor calls the high-frequency noise model to generate the environmental artifact noise sequence, which is mainly composed of high-frequency white noise and random spike interference.

[0054] ② When the equivalent central pressure value is in the second interval (e.g., the severe compression interval, > 40 kPa), it indicates that the probe clamp is too tight, obstructing venous blood return and easily introducing muscle twitching interference. At this time, the processor calls the low-frequency noise model to generate the environmental artifact noise sequence, which is mainly composed of low-frequency baseline drift and motion artifacts.

[0055] ③ When the equivalent center pressure value is in the third interval (e.g., the normal measurement interval, [10, 40] kPa), it indicates that the probe clamping force is moderate and the optical coupling state is good. At this time, the processor calls the basic noise model to generate the environmental artifact noise sequence containing only a very small amplitude of the system's inherent background noise (such as analog circuit thermal noise).

[0056] In one embodiment, in order to accurately map the calculated red light AC amplitude attenuation coefficient, infrared light AC amplitude attenuation coefficient, dynamic phase delay parameter, and environmental artifact noise sequence onto an ideal digital signal waveform in the digital domain, step S4, which modulates the reference photoplethysmography pulse wave sequence based on the red light AC amplitude attenuation coefficient, the infrared light AC amplitude attenuation coefficient, the dynamic phase delay parameter, and the environmental artifact noise sequence to obtain a red light discrete digital sequence and an infrared light discrete digital sequence containing dynamic pressure distortion characteristics, includes: S401. Acquire the reference AC waveform data in the reference photoplethysmography sequence of the red light channel and the infrared light channel respectively; convert the dynamic phase delay parameter into the corresponding data point offset, and perform translation modulation on the reference AC waveform data in the direction of time increase based on the data point offset to obtain the translated red light AC waveform data and infrared light AC waveform data.

[0057] For example, the processor first extracts reference AC waveform data from a discrete reference photoplethysmography (PPG) sequence, and then, in conjunction with a preset system sampling rate, transforms the dynamic phase delay parameter to obtain discrete data point offsets. The transformation formula is as follows: N offset =round(ΔΦ×(f s / 1000)); Where, N offset This represents the calculated data point offset, where ΔΦ is the dynamic phase delay parameter, and f s The preset system sampling rate is used, and round(·) represents the rounding function to ensure that the offset is an integer.

[0058] After obtaining the data point offset, the processor shifts the reference AC waveform data of the red light channel and infrared light channel in the direction of increasing time (i.e., backward) by N in its internal memory. offset The data points are used to accurately discretize the physical delay phenomenon in the digital domain, thereby obtaining the translated red light AC waveform data and infrared light AC waveform data.

[0059] S402. Based on the red light AC amplitude attenuation coefficient and the infrared light AC amplitude attenuation coefficient, the shifted red light AC waveform data and infrared light AC waveform data are attenuated and modulated to obtain attenuated red light AC waveform data and infrared light AC waveform data.

[0060] For example, the AC amplitude corresponding to each discrete data point in the translated red AC waveform data is multiplied by the red AC amplitude attenuation coefficient K. redThis yields the attenuated red light AC waveform data. Simultaneously, the AC amplitude corresponding to each discrete data point in the shifted infrared AC waveform data is multiplied by the infrared AC amplitude attenuation coefficient K. ir The attenuated infrared AC waveform data is obtained. Because K... red and K ir The signals are not equal and are between [0,1]. Multiplication is used for attenuation modulation, which can compress the amplitude of the vertical axis waveform proportionally only in the digital domain, accurately reproducing the physical distortion characteristics of the non-proportional attenuation of the dual-wavelength AC pulsating signal under pressure and ischemia.

[0061] It should be noted that this application uses unequal AC amplitude attenuation coefficients (i.e., K) for the red and infrared light channels. red ≠K ir This is because a real pulse oximeter relies on the AC / DC ratio (R-value) of red and infrared light when calculating blood oxygen saturation. Ideally, the target blood oxygen saturation corresponds to a baseline ratio; however, in the simulated mechanical compression scenario of a finger in this embodiment, the change in optical path due to blood flow from the capillary bed not only drastically compresses the amplitude of the AC signal but also causes differences in the pressure response at different wavelengths. The actual physical ratio R-value after mechanical compression... red Satisfy the following mathematical relationship: ; Among them, R red Indicates the true physical ratio after compression distortion; AC red_base and AC ir_base These represent the amplitude values ​​of the red and infrared light reference AC components set during initialization; DC red_base and DC ir_base K represents the DC components of the red and infrared light references, respectively. dc_red and K dc_ir The DC attenuation coefficients of red and infrared light under pressure are respectively characterized (in a specific capillary pressure model, due to the relatively slow change in absorption by venous blood and tissue, this DC attenuation coefficient is often approximately 1 or exhibits a constant bias response). From the above real physical double ratio formula, it can be concluded that it is precisely because this application introduces dynamic pressure feedback and makes K... red ≠K ir This leads to the calculated distortion physical ratio R. red It inevitably deviates from the ideal baseline ratio, thus mathematically explaining and restoring the complex physiological phenomenon of pressure interference causing inaccurate pulse oximeter measurements.

[0062] It should be noted that the horizontal axis of the red light AC waveform data and the infrared light AC waveform data is the time axis or index axis, and the vertical axis is the amplitude axis.

[0063] S403. Based on the set target blood oxygen saturation value, determine the reference DC components corresponding to the red light channel and the infrared light channel respectively; and add the attenuated red light AC waveform data and infrared light AC waveform data to the corresponding reference DC components respectively, and mix the environmental artifact noise sequence point by point according to the time series index to generate a red light discrete digital sequence and an infrared light discrete digital sequence containing dynamic pressure distortion characteristics.

[0064] Specifically, in the digital signal processing link, the reference DC component is a scalar constant characterizing the static absorption, while the attenuated AC waveform data and the environmental artifact noise sequence are both discrete one-dimensional arrays. The processor uses a point-by-point additive superposition algorithm strictly based on time series indexing for signal reconstruction, and the corresponding mathematical expression is: S red [n]=AC red [n]+DC red +N[n]; S ir [n]=AC ir [n]+DC ir +N[n]; Where n is the time series index (i.e., the integer subscript representing the order of data points) generated by discretizing the time axis based on a preset system sampling rate, and S red [n] and S ir [n] represents the total amplitude of the final generated red light discrete digital sequence and infrared light discrete digital sequence at the nth index point, respectively; AC red [n] and AC ir [n] represents the AC amplitude of the attenuated red light AC waveform data and infrared light AC waveform data at the nth index point, respectively; DC red and DC ir The red light reference DC component and the infrared light reference DC component are respectively characterized, and N[n] represents the noise amplitude of the environmental artifact noise sequence at the nth index point.

[0065] Through the point-by-point addition operation based on the time series index, the processor linearly superimposes the attenuated AC waveform data, the reference DC component, and the environmental artifact noise sequence to generate the red light discrete digital sequence and the infrared light discrete digital sequence containing dynamic pressure distortion characteristics in the digital domain.

[0066] In one embodiment, to convert the red light discrete digital sequence and the infrared light discrete digital sequence containing dynamic pressure distortion characteristics generated in the digital domain into optical signals that can be truly received by the pulse oximeter under test, the step S5 of converting the red light discrete digital sequence and the infrared light discrete digital sequence into analog voltage signals through a digital-to-analog converter in the pulse oximeter simulator, and driving the light-emitting diode on the pulse oximeter simulator based on the analog voltage signals so that the light-emitting diode outputs an analog optical signal containing dynamic pressure distortion characteristics, includes: S501. The generated red light discrete digital sequence and infrared light discrete digital sequence containing dynamic pressure distortion characteristics are synchronously input into the digital-to-analog converter in the pulse oximeter simulator according to the preset system sampling rate, and converted into the corresponding red light analog voltage signal and infrared light analog voltage signal.

[0067] For example, the processor uses the aforementioned preset system sampling rate to synchronously output the generated red light discrete digital sequence and infrared light discrete digital sequence point by point to a high-precision dual-channel digital-to-analog converter (DAC). The DAC, combined with its internal hold circuit and low-pass reconstruction filter, directly and smoothly converts the input discrete digital sequence into the time-continuous and amplitude-continuous red light analog voltage signal and infrared light analog voltage signal. At this point, the analog voltage signal already carries the aforementioned dynamic pressure distortion characteristics, including amplitude attenuation, phase delay, and environmental artifacts.

[0068] S502. Based on the red light analog voltage signal and the infrared light analog voltage signal, the driving current flowing through the corresponding light-emitting diode is controlled by the analog driving circuit, so that the red light-emitting diode and the infrared light-emitting diode on the pulse oximeter simulator output the analog optical signal containing dynamic pressure distortion characteristics.

[0069] For example, since the luminous intensity (luminous flux) of a light-emitting diode (LED) is linearly proportional to its forward drive current, the pulse oximeter simulator internally includes an analog drive circuit (e.g., a high-bandwidth voltage-controlled current source circuit) that performs voltage-to-current (VI) conversion. The red light analog voltage signal and the infrared light analog voltage signal are respectively input to their respective analog drive circuits and linearly converted into corresponding dynamic drive currents. This dynamic drive current is then injected into the red light-emitting diode and the infrared light-emitting diode located at the bionic finger probe of the pulse oximeter simulator, respectively. As the dynamic drive current changes in real time, the luminous intensity of the LED changes synchronously, thereby outputting the analog optical signal containing dynamic pressure distortion characteristics to the photoelectric receiver of the pulse oximeter under test, which is clamped on the pulse oximeter simulator.

[0070] This application also provides a pulse oximeter testing method that applies the dynamic pressure feedback-based finger pulse oximetry optical signal simulation method as described in any of the preceding claims, comprising: S11. The pulse oximeter to be tested is clamped onto the simulation model of the pulse oximeter simulator, so that the pulse oximeter to be tested applies contact pressure to the surface of the simulation model, and the optical receiving end of the pulse oximeter to be tested is aligned with the light-emitting diode on the pulse oximeter simulator. S21. The pulse oximeter simulator executes the dynamic pressure feedback-based finger pulse oximetry optical signal simulation method in real time according to the collected contact pressure, and outputs the simulated optical signal containing dynamic pressure distortion characteristics to the pulse oximeter under test. S31. The pulse oximeter to be tested receives the simulated optical signal and performs calculations based on the simulated optical signal to output the measured heart rate value and the measured blood oxygen saturation value. S41. Obtain the measured heart rate value and the measured blood oxygen saturation value, and compare them with the target heart rate value and the target blood oxygen saturation value set in the pulse oximeter simulator, respectively, to evaluate the measurement accuracy and robustness of the pulse oximeter under dynamic pressure interference.

[0071] As described above, by combining the pulse oximeter under test with a simulation model of actual applied contact pressure, the pulse oximeter simulator outputs a dynamic distortion optical signal that highly replicates the human body's pressure-induced ischemia state in real time as a standard test excitation source. The measured heart rate and blood oxygen saturation values ​​output by the pulse oximeter under test are accurately compared with the target setting parameters at the bottom layer of the pulse oximeter simulator. Thus, without the need for complex and uncontrollable large-scale human clinical trials, a quantitative and standardized evaluation of the measurement accuracy and anti-interference robustness of the pulse oximeter under complex physical interference scenarios such as dynamic pressure and poor contact is achieved, which greatly reduces the R&D testing cost and verification cycle of exercise-resistant pulse oximeters.

[0072] It is understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0073] Based on the same inventive concept, this application also provides a system for implementing the above-described dynamic pressure feedback-based finger pulse oxygen optical signal simulation system. The solution provided by this system is similar to the solution described in the above method; therefore, the specific limitations in one or more system embodiments provided below can be found in the limitations of the method described above, and will not be repeated here.

[0074] In one exemplary embodiment, such as Figure 3 As shown, a finger pulse oxygen optical signal simulation system based on dynamic pressure feedback is provided, including: a biomimetic physical interaction unit 1, a processing and driving unit 2, and a hardware dual closed-loop self-calibration unit 3.

[0075] The above system corresponds to the aforementioned method embodiments and can implement the corresponding method steps. Its implementation principle and technical effects are similar. To further illustrate the technical solution of this application and its actual working process intuitively, the following, combined with a specific R&D testing scenario, details the collaborative workflow of each hardware unit and software algorithm of this system: Preparation phase: The testers clamped the novel anti-exercise pulse oximeter to be tested onto the silicone base surface of the system's biomimetic physical interaction unit. In the host computer interface, the initial ideal baseline parameters were set as follows: target heart rate 75 bpm, target blood oxygen saturation 98%. The test began, and the system's processing and drive unit entered the real-time dynamic simulation calculation process.

[0076] Step A: Action occurs: The spring clamping force of the pulse oximeter is large, and it tightly clamps the silicone base.

[0077] System Sensing: The thin-film pressure sensor array deployed on the surface of the silicone base acquired a non-uniform pressure vector in real time, for example: (18, 16, 20, 15, 17) kPa. After acquiring this pressure vector, the processing and driving unit did not simply take the average value, but instead used a two-dimensional interpolation algorithm (such as spatial spline interpolation) to calculate the equivalent center pressure value directly below the luminous probe of the pulse oximeter to be tested, which is 18 kPa. It is known that the optimal contact pressure range is usually 5 to 15 kPa, and at this point, 18 kPa is already in an abnormal state of "blood vessel collapse" due to heavy pressure.

[0078] Step B: When a real human body is subjected to a pressure of 18 kPa, blood in the capillary bed is displaced. At this time, there is a significant difference in the rate of change of absorption of red light (660 nm) and infrared light (940 nm) in the tissue fluid. Because the equivalent central pressure value is in an excessive state, the processing and driving units use different contact pressure attenuation constants for decoupling calculations: the AC amplitude attenuation coefficient for the red light channel is 0.65, and the AC amplitude attenuation coefficient for the infrared light channel is 0.80. This step realistically simulates the spectral distortion caused by physical pressure.

[0079] Step C: Action occurred: The tester suddenly released the clamp, causing it to clamp the silicone base instantly.

[0080] The processing and actuation unit calculated the time derivative (i.e., the rate of pressure change) of the equivalent central pressure value and found that the pressure spiked to 18 kPa in an extremely short time, with a very large pressure gradient exceeding the dynamic trigger threshold. Based on this rate of pressure change, the processing and actuation unit introduced a dynamic phase delay parameter (e.g., 40 ms) into the baseline waveform sequence generated using a double Gaussian kernel function. This physically simulates the phenomenon where "when a clamp suddenly tightens, local blood is instantly squeezed out, causing a millisecond-level lag in pulse wave transmission to the fingertip."

[0081] Step D: The current pressure is 18 kPa, and the clamping is extremely tight, making it difficult for the testing instrument to experience high-frequency physical relative slippage (light leakage). However, excessively high pressure can obstruct venous blood return and cause local volume stagnation. The processing and drive unit automatically cuts off high-frequency white noise based on the noise coupling model invoked within the pressure range, and instead mixes a 0.1 Hz low-frequency environmental artifact noise sequence (i.e., low-frequency baseline drift) into the waveform. Conversely, if the clamping force is extremely weak (e.g., 3 kPa), high-frequency light leakage and motion artifact noise will be injected in the opposite direction.

[0082] Step E: After the aforementioned independent attenuation and superposition modulation, the processing and driving unit generates the final discrete digital sequences of red and infrared light. Because unequal attenuation was forcibly introduced in step B, the underlying AC / DC ratio R of this digital sequence undergoes a substantial shift. The processing and driving unit converts the digital sequence into an analog voltage via a digital-to-analog converter and outputs a dynamic driving current to excite the analog optical signal. This current is injected into the light-emitting diode (LED) in the hardware dual-loop self-calibration unit integrated within the silicone substrate to make it emit light.

[0083] During this process, a miniature photodetector positioned next to the LED captures only the backscattered light emitted by the LED itself and converts it into a measured feedback waveform, which is then sent to the processing and driving unit. If the brightness of the red LED decreases due to heat, the processing and driving unit immediately compares the residual between the theoretical reference waveform and the measured feedback waveform, and dynamically compensates for this difference by using a PID control algorithm to adjust the dynamic driving current output by the LED. This compensated dynamic driving current then completely eliminates the thermal drift wavelength distortion of the LED, ensuring that the final output analog optical signal 100% reproduces the previously calculated red and infrared discrete digital sequences, which include dynamic pressure distortion characteristics.

[0084] After receiving the simulated optical signal generated by this system, if the anti-pressure and anti-interference algorithm inside the novel pulse oximeter under test is good enough, it can eliminate the above interference and still display a pulse oxygen value of 98%; if its algorithm is not robust enough, it will display an incorrectly low value (e.g., 91%) due to the offset of the double ratio R, thus realizing the ultimate test of the anti-interference performance of the device under test.

[0085] Please see Figure 4 This application also provides a computer device. The computer device 90 may include a processor 91, a memory 92, and computer applications, wherein: The memory 92 is used to store the computer application, and the memory may also be flash memory. The computer application is, for example, an application that implements the various method embodiments described above.

[0086] Processor 91 is configured to execute the computer application stored in the memory to implement the steps in the various method embodiments described above. For details, please refer to the relevant descriptions in the preceding method embodiments.

[0087] Alternatively, the memory 92 can be either standalone or integrated with the processor 91.

[0088] When the memory 92 is a device independent of the processor 91, the computer device 90 may further include: Bus 93 is used to connect the memory 92 and the processor 91.

[0089] Please see Figure 5 This application also provides a readable storage medium storing a computer application program, which, when executed by a processor, implements the methods of the above-described method embodiments.

[0090] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located on an Application-Specific Integrated Circuit (ASIC). s In an ASIC (Integrated Circuit-Based ASIC), the processor and readable storage medium can reside within the user equipment. Alternatively, the processor and readable storage medium can also exist as discrete components within the communication device.

[0091] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein, and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for simulating optical signals of pulse oximetry based on dynamic pressure feedback, applied to a pulse oximeter simulator, characterized in that, include: The pressure vector is collected in real time from the pressure sensor array deployed on the surface of the simulation model of the pulse oximeter simulator, and the equivalent center pressure value is obtained based on the pressure vector. The target heart rate and target blood oxygen saturation values ​​are obtained to synthesize the reference photoplethysmography pulse wave sequences for the red and infrared light channels. Based on the equivalent center pressure value, the red light AC amplitude attenuation coefficient of the red light channel, the infrared light AC amplitude attenuation coefficient of the infrared light channel, the dynamic phase delay parameter, and the environmental artifact noise sequence are determined respectively; wherein, the red light AC amplitude attenuation coefficient is calculated using a first contact pressure attenuation constant, the infrared light AC amplitude attenuation coefficient is calculated using a second contact pressure attenuation constant, and the first contact pressure attenuation constant and the second contact pressure attenuation constant are not equal; Based on the red light AC amplitude attenuation coefficient, the infrared light AC amplitude attenuation coefficient, the dynamic phase delay parameter, and the environmental artifact noise sequence, the reference photoplethysmography pulse wave sequence is modulated to obtain a red light discrete digital sequence and an infrared light discrete digital sequence containing dynamic pressure distortion characteristics. The red light discrete digital sequence and the infrared light discrete digital sequence are converted into analog voltage signals by a digital-to-analog converter in the pulse oximeter simulator, and the light-emitting diodes on the pulse oximeter simulator are driven based on the analog voltage signals so that the light-emitting diodes output analog optical signals containing dynamic pressure distortion characteristics.

2. The method for simulating optical signals of finger pulse oxygenation based on dynamic pressure feedback according to claim 1, characterized in that, The process of acquiring the pressure vector, which is output in real time by the pressure sensor array deployed on the surface of the simulation model of the pulse oximeter simulator, and obtaining the equivalent central pressure value based on the pressure vector, includes: The pressure vector output by the pressure sensor array deployed on the surface of the simulation model is acquired in real time, and the physical space coordinates of each pressure sensor in the pressure sensor array are obtained. Using a two-dimensional interpolation algorithm, a two-dimensional pressure distribution function is constructed based on the pressure vector and the physical space coordinates corresponding to each pressure sensor. Obtain the effective contact area between the pulse oximeter probe and the simulation model, and calculate the average area integral of the two-dimensional pressure distribution function within the effective contact area. Use the average area integral as the equivalent central pressure value.

3. The method for simulating optical signals of finger pulse oxygenation based on dynamic pressure feedback according to claim 1, characterized in that, The process of acquiring the set target heart rate and target blood oxygen saturation values ​​to synthesize reference photoplethysmography (PPG) sequences for the red and infrared light channels includes: The target heart rate and target blood oxygen saturation values ​​are obtained, and the reference AC component amplitudes of the red light channel and infrared light channel are determined based on the target blood oxygen saturation values, respectively. Based on the target heart rate value and the reference AC component amplitude, the systolic and diastolic waves of the red light channel and the infrared light channel in a single pulse cycle are synthesized using a dual Gaussian kernel function, and a single-cycle reference waveform function is obtained based on the systolic and diastolic waves. The single-cycle reference waveform function is continuously extended and discretized according to the time period corresponding to the target heart rate value to generate the reference photoplethysmography pulse wave sequence of the red light channel and the infrared light channel.

4. The method for simulating optical signals of finger pulse oxygenation based on dynamic pressure feedback according to claim 1, characterized in that, The determination of the red light AC amplitude attenuation coefficient of the red light channel, the infrared light AC amplitude attenuation coefficient of the infrared light channel, the dynamic phase delay parameter, and the environmental artifact noise sequence based on the equivalent center pressure value includes: Call the pre-calibrated first and second contact pressure attenuation constants; Based on the equivalent center pressure value, the red light AC amplitude attenuation coefficient is calculated in combination with the first contact pressure attenuation constant, and the infrared light AC amplitude attenuation coefficient is calculated in combination with the second contact pressure attenuation constant; and the first contact pressure attenuation constant is not equal to the second contact pressure attenuation constant. Calculate the rate of change of the equivalent center pressure value over time, and determine the dynamic phase delay parameter based on the rate of change; Determine the pressure range to which the equivalent center pressure value belongs, and call the corresponding noise coupling model according to the pressure range to generate the environmental artifact noise sequence.

5. The method for simulating finger pulse oxygen optical signals based on dynamic pressure feedback according to claim 1, characterized in that, The modulation of the reference photoplethysmography (PPG) sequence based on the red light AC amplitude attenuation coefficient, the infrared light AC amplitude attenuation coefficient, the dynamic phase delay parameter, and the environmental artifact noise sequence yields a red light discrete digital sequence and an infrared light discrete digital sequence containing dynamic pressure distortion characteristics, including: The reference AC waveform data in the reference photoplethysmography sequences of the red light channel and the infrared light channel are obtained respectively. The dynamic phase delay parameter is converted into a corresponding data point offset, and the reference AC waveform data is shifted and modulated in the direction of time increase based on the data point offset to obtain the shifted red light AC waveform data and infrared light AC waveform data. Based on the red light AC amplitude attenuation coefficient and the infrared light AC amplitude attenuation coefficient, the AC amplitude corresponding to each discrete data point in the translated red light AC waveform data and the translated infrared light AC waveform data is subjected to multiplicative attenuation modulation to obtain the attenuated red light AC waveform data and infrared light AC waveform data. Based on the set target blood oxygen saturation value, the reference DC components corresponding to the red light channel and the infrared light channel are determined respectively; The attenuated red light AC waveform data and the attenuated infrared light AC waveform data are respectively added and superimposed with the corresponding reference DC component, and the environmental artifact noise sequence is mixed in point by point according to the time series index to generate the red light discrete digital sequence and the infrared light discrete digital sequence containing dynamic pressure distortion characteristics.

6. The method for simulating finger pulse oxygen optical signals based on dynamic pressure feedback according to claim 1, characterized in that, The process of converting the red light discrete digital sequence and the infrared light discrete digital sequence into analog voltage signals via a digital-to-analog converter within the pulse oximeter simulator, and driving the light-emitting diodes (LEDs) on the pulse oximeter simulator based on the analog voltage signals, so that the LEDs output analog optical signals containing dynamic pressure distortion characteristics, includes: The generated red light discrete digital sequence and infrared light discrete digital sequence containing dynamic pressure distortion characteristics are synchronously input into the digital-to-analog converter in the pulse oximeter simulator according to the preset system sampling rate, and converted into the corresponding red light analog voltage signal and infrared light analog voltage signal. Based on the red light analog voltage signal and the infrared light analog voltage signal, the driving current flowing through the corresponding light-emitting diode is controlled by the analog driving circuit, so that the red light-emitting diode and the infrared light-emitting diode on the pulse oximeter simulator output the analog optical signal containing dynamic pressure distortion characteristics.

7. A pulse oximeter testing method using the dynamic pressure feedback-based finger pulse oximetry optical signal simulation method as described in any one of claims 1-6, characterized in that, include: The pulse oximeter to be tested is clamped onto the simulation model of the pulse oximeter simulator, so that the pulse oximeter to be tested applies contact pressure to the surface of the simulation model, and the optical receiving end of the pulse oximeter to be tested is aligned with the light-emitting diode on the pulse oximeter simulator. The pulse oximeter simulator executes the dynamic pressure feedback-based finger pulse oximetry optical signal simulation method in real time according to the collected contact pressure, and outputs the simulated optical signal containing dynamic pressure distortion characteristics to the pulse oximeter under test. The pulse oximeter to be tested receives the simulated optical signal and performs calculations based on the simulated optical signal to output the measured heart rate value and the measured blood oxygen saturation value. The measured heart rate value and the measured blood oxygen saturation value are obtained, and their errors are compared with the target heart rate value and target blood oxygen saturation value set in the pulse oximeter simulator, respectively, to generate the evaluation result of the pulse oximeter under dynamic pressure interference.

8. A finger pulse oxygen optical signal simulation system based on dynamic pressure feedback, characterized in that, include: The biomimetic physical interaction unit includes a silicone base and a thin-film pressure sensor array disposed on the surface of the silicone base, which is used to sense external contact and output pressure vector in real time. The processing and driving unit is communicatively connected to the thin-film pressure sensor array, and is used to acquire the pressure vector and execute the finger pulse oxygen optical signal simulation method based on dynamic pressure feedback as described in any one of claims 1 to 6, so as to output a dynamic driving current for exciting the simulated optical signal. A hardware dual-closed-loop self-calibration unit is integrated inside the silicone base, including a light-emitting diode and a miniature photodetector. The light-emitting diode receives the dynamic driving current and outputs an analog optical signal. The miniature photodetector is positioned next to the light-emitting diode (LED) and configured to capture only the backscattered light emitted by the LED itself, converting it into a measured feedback waveform and sending it to the processing and driving unit. The processing and driving unit compares the residual between the theoretically generated reference waveform and the measured feedback waveform, and uses a proportional-integral-derivative (PID) control algorithm to dynamically compensate for the dynamic driving current it outputs. This compensated dynamic driving current then eliminates the wavelength distortion caused by thermal drift of the LED.

9. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer application, and the processor executing the computer application, wherein the computer application is used to implement the method as described in any one of claims 1-6 when executed by the processor.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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