A portable multi-physiological parameter measurement device and method

Abstract

The application discloses a portable multi-physiological parameter measuring device and method, and relates to the technical field of physiological parameter measurement. The device comprises a signal acquisition and preprocessing module, a PPG derived far-end respiration signal extraction module, an acceleration derived near-end respiration signal extraction module, a time delay calculation module, a blood pressure parameter calculation module, a respiration parameter extraction module and a vegetative nerve evaluation module. The signal acquisition and preprocessing module is used for acquiring a PPG signal and a three-axis acceleration signal change in a respiration process. The PPG derived far-end respiration signal extraction module is used for obtaining a peak point envelope signal of the PPG signal as a far-end respiration signal. The acceleration derived near-end respiration signal extraction module is used for extracting an intrinsic mode with maximum power spectral density in a preset range as a near-end respiration signal. The time delay calculation module is used for calculating a time delay between the near-end and far-end respiration signals. The blood pressure parameter calculation module is used for inputting the time delay into an RDT mapping model to calculate a blood pressure parameter. The respiration parameter extraction module is used for calculating each respiration parameter according to the near-end and far-end respiration signals. The vegetative nerve evaluation module is used for quantitatively evaluating sympathetic nerves and vagus nerves. The application has the advantages of low measurement cost, multiple parameters and accurate results.

Description

TECHNICAL FIELD

[0001] The present application relates to physiological parameter measurement technology, and in particular to a portable multi-physiological parameter measurement device and method. BACKGROUND

[0002] With the gradual aggravation of population aging and the continuous improvement of national health consciousness, the importance of home health measurement and management is increasingly prominent. Traditional professional medical equipment is bulky and expensive, which is difficult to meet the requirements of portability and economy for daily health monitoring. Therefore, intelligent wearable devices integrated with multiple sensors are becoming the main choice for public health management due to their advantages of lightness, efficiency and low cost.

[0003] At present, most of the wristbands on the market are equipped with photoplethysmography (PPG) and acceleration sensors, which can realize the monitoring of physiological parameters such as heart rate and blood oxygen, but still face challenges in the accurate measurement of parameters such as plant nerve activity, blood pressure, respiration and their derived complex parameters. In addition, considering the availability and wide popularity of mobile devices, health monitoring using built-in cameras, accelerometers, ultra-wideband radars (UWB) and other sensors of mobile phones has gradually emerged. Existing technologies can realize heart rate detection through the combination of cameras and flashlights, but there are fewer schemes for measuring parameters such as respiration, blood pressure and plant nerve activity based on mobile devices.

[0004] In terms of respiratory parameter measurement, there are two main methods for wearable devices to calculate respiratory parameters: one is to indirectly calculate respiratory parameters using PPG signals, but this method mainly has the following limitations: single parameter acquisition, usually only the respiratory timing feature can be extracted, and it is difficult to provide more respiratory parameter and mode information; limited measurement site, only reflects local respiratory changes. The other is to combine acceleration signals and PPG signals to calculate respiratory parameters, for example, the patent document with application number 202110619645.3 discloses a wearable device and its monitoring method and monitoring device. The patent technology scheme realizes the calibration and monitoring of respiratory signals by decomposing the motion component and respiratory component in the acceleration signal, but the wrist is less affected by respiratory fluctuations in a natural state, and the respiratory signal extracted from the acceleration signal is easily overwhelmed by noise, resulting in inaccurate measurement.

[0005] In terms of blood pressure measurement, the current wearable devices with blood pressure monitoring function mainly adopt three technologies: the first is direct measurement by pressure, which needs to be repeatedly pressurized during the measurement process, causing obvious discomfort to the user. The second is to estimate blood pressure parameters based on PPG signals, for example, the patent document with application number 202210300668.2 discloses a wearable physiological parameter detection system, which estimates blood pressure through the waveform characteristics of PPG signals. However, this method requires identifying other waveform characteristics outside the peak point, which is difficult to capture and requires high resolution of PPG signals. It is also greatly affected by individual physiological parameter differences, and the measurement result is not accurate, and the actual operability is not strong. The third is an indirect measurement method based on pulse transit time (PTT) of electrocardiogram (ECG)-PPG fusion, for example, the patent document with application number 97199737 discloses a non-invasive sleeve-free blood pressure measurement method, which calculates the time delay between ECG signals and PPG signals to estimate blood pressure. However, this method requires an ECG sensor, which has high hardware requirements and measurement cost, and is easily affected by environmental and motion interference. The quality of either ECG or PPG signals will significantly affect the accuracy of the final measurement result.

[0006] In terms of evaluation of autonomic nervous activity, the current wearable devices mainly evaluate the autonomic nervous system based on heart rate variability, without further integrating respiratory, blood pressure and other system parameters to quantitatively evaluate the activity of sympathetic and vagus nerves, and cannot achieve accurate analysis of the autonomic nervous system. SUMMARY

[0007] To solve the problems of the prior art, the purpose of the present application is to provide a portable multi-physiological parameter measurement device and method with lower cost, more comprehensive measurement results and higher accuracy.

[0008] To achieve the above-mentioned purpose of the application, the present application provides the following technical solutions:

[0009] A portable multi-physiological parameter measurement device, comprising:

[0010] A signal acquisition and preprocessing module for acquiring PPG signals of a user and three-axis acceleration signal changes derived from chest, abdominal or head fluctuations during respiration, and filtering low-frequency drift and high-frequency noise in the acquired signals;

[0011] A PPG-derived telecentric end respiratory signal extraction module for obtaining a peak point envelope signal of the PPG signal as a telecentric end respiratory signal;

[0012] an acceleration-derived proximal end respiration signal extraction module configured to perform modal decomposition on the three-axis acceleration signals to obtain a plurality of intrinsic modes, and extract an intrinsic mode with the maximum power spectral density in a preset frequency range as the proximal end respiration signal;

[0013] a time delay calculation module configured to perform millisecond-level time sequence alignment on the proximal end respiration signal and the distal end respiration signal, and calculate the time delay between the proximal end respiration signal and the distal end respiration signal;

[0014] a blood pressure parameter calculation module configured to input the time delay as a delay parameter into a pre-constructed respiratory dispersion time (RDT) mapping model to calculate the blood pressure parameter, wherein the RDT mapping model is configured to calculate the blood pressure parameter according to the delay parameter of the respiration from the proximal end to the distal end;

[0015] a respiration parameter extraction module configured to calculate the respiration parameter including the respiration depth, the respiration mode matching degree, the respiration frequency, and the inhalation-exhalation ratio according to the distal end respiration signal and the proximal end respiration signal.

[0016] a plant nerve evaluation module configured to fuse the blood pressure parameter, the respiration parameter, and the heart rate variability feature to realize quantitative evaluation of the sympathetic nerve and the vagus nerve.

[0017] Optionally, the signal acquisition and preprocessing module can be a wearable device, the wearable device being provided with a photoelectric unit, an accelerometer, and a preprocessing unit, the photoelectric unit being configured to acquire the PPG signal of a user, the accelerometer being configured to acquire three-axis acceleration signal changes derived from chest, abdominal, or head fluctuations during respiration, and the preprocessing unit being configured to sequentially perform bandpass, non-phase shift, and finite impulse response filtering on the PPG signal and the three-axis acceleration signal.

[0018] Optionally, the signal acquisition and preprocessing module can also be a mobile device, the mobile device being provided with a flash, a camera, an accelerometer, and a data acquisition and processing unit, the flash and the camera being controlled by the data acquisition and processing unit to flash the flash and take pictures of a measurement site at a predetermined frequency, the accelerometer being configured to acquire three-axis acceleration signal changes derived from chest, abdominal, or head fluctuations during respiration, and the data acquisition and processing unit being configured to extract the PPG signal of a user according to the pictures, and sequentially perform bandpass, non-phase shift, and finite impulse response filtering on the PPG signal and the three-axis acceleration signal.

[0019] Further, the PPG-derived distal end respiration signal extraction module specifically includes:

[0020] a segmentation unit configured to divide the acquired PPG signal into a plurality of PPG signal segments;

[0021] a standard deviation calculation unit configured to calculate a standard deviation SD of the PPG signal segment and the PPG template waveform according to a pre-constructed PPG template waveform, according to the following formula:

[0022]

[0023] wherein SD is the standard deviation, S(n) is the sampled PPG signal, T(n) is the PPG template waveform, and N is the total number of sampling points of the PPG signal segment;

[0024] a judgment unit configured to judge whether the SD is less than a set threshold SDT, and if yes, determine that the current PPG signal segment is valid, otherwise, determine that the current PPG signal segment is invalid;

[0025] a telecentric end respiration signal generation unit configured to search for peak points of the valid PPG signal segments in the PPG signal, connect all the peak points, and fill in the data of the invalid PPG signal segments through interpolation to obtain a processed PPG signal, and then resample the processed PPG signal to obtain a telecentric end respiration signal R1(n).

[0026] Further, the acceleration-derived proximal end respiration signal extraction module specifically comprises:

[0027] a modal decomposition unit configured to perform modal decomposition on the three-axis acceleration signal to obtain an intrinsic modal set {IMF i , i = 1, 2, …, M}, wherein IMF i is the i-th intrinsic mode obtained after modal decomposition of the three-axis acceleration signal, and M is the number of intrinsic modes;

[0028] a power spectral density calculation unit configured to calculate the power spectral density of each intrinsic mode in the frequency domain;

[0029] a proximal end respiration signal generation unit configured to calculate the proximal end respiration signal according to the following formula:

[0030]

[0031] wherein R2(n) represents the proximal end respiration signal, and PSD(IMF i (f)) represents the power spectral density of IMF i at frequency f.

[0032] Further, the time delay calculation module specifically comprises:

[0033] a time alignment unit configured to obtain the signal sampling time stamp of the signal acquisition and preprocessing module, so as to millisecond-level align the timing of the telecentric end respiration signal and the proximal end respiration signal;

[0034] a peak extraction unit configured to extract the peak points of the telecentric end respiration signal and the proximal end respiration signal.

[0035] a time delay unit configured to calculate a time delay between the apical respiratory signal and the basal respiratory signal at the peak points as the time delay between the apical respiratory signal and the basal respiratory signal in a current respiratory cycle.

[0036] Further, the blood pressure parameter calculation module specifically comprises:

[0037] a morphological parameter extraction unit configured to search for valley points before and after the peak points of the basal respiratory signal respectively, obtain start and end times of the current respiratory cycle according to the peak points and the valley points, and find a PPG signal segment located in the start and end times of the respiratory cycle in the PPG signal, and calculate average rising and falling times of all PPG waveforms in the signal segment;

[0038] a blood pressure calculation unit configured to input the time delay and the average heart rate into a pre-constructed RDT mapping model to calculate the blood pressure parameter, wherein the pre-constructed RDT mapping model is specifically:

[0039]

[0040] wherein SBP and DBP are systolic pressure and diastolic pressure respectively, a S , b S , c S , d S , a D , b D , c D , d D are adjustment coefficients fitted by historical data using a multiple linear regression function, τ is the time delay, UT is the average rising time of the PPG waveform, and DT is the average falling time of the PPG waveform.

[0041] Further, the respiratory parameter extraction module specifically comprises:

[0042] a respiratory depth calculation unit configured to calculate an average amplitude A1 of the apical respiratory signal, and calculate the respiratory depth according to the average amplitude A1:

[0043] D=k·A1

[0044] wherein k is an individualized parameter, and D is the respiratory depth.

[0045] a respiratory mode matching degree calculation unit configured to calculate an average amplitude A2 of the basal respiratory signal, and calculate the respiratory mode matching degree according to the average amplitude A1 of the apical respiratory signal and the average amplitude A2 of the basal respiratory signal according to the following formula:

[0046]

[0047] Wherein, μ represents a phase synchronization coefficient, N represents a number of sampling points, ψ (R1 (n)) represents a phase of the distal end respiratory signal R1 (n), ψ (R2 (n)) represents a phase of the proximal end respiratory signal R2 (n), and M represents a respiratory mode matching degree;

[0048] The respiratory frequency calculation unit is configured to identify peak points of the proximal end respiratory signal, calculate an average value S of interval times between two peak points, and calculate the respiratory frequency according to the following formula:

[0049]

[0050] Wherein, f represents the respiratory frequency.

[0051] The inhalation-exhalation ratio calculation unit is configured to search for valley points before and after each peak point of the proximal end respiratory signal, calculate an average value I of interval times between the peak point and the front valley point, and an average value E of interval times between the peak point and the rear valley point, and obtain the inhalation-exhalation ratio:

[0052]

[0053] Wherein, R represents the inhalation-exhalation ratio.

[0054] Further, the autonomic nerve evaluation module specifically comprises:

[0055] The heart rate calculation unit is configured to identify peak points in the PPG signal, obtain interval times between two peak points, and calculate a heart rate sequence according to the following formula:

[0056]

[0057] Wherein, HR (m) represents an mth heart rate value, FM represents a total number of peak points of the PPG signal, and PP (m) represents a time interval between an mth peak point and an (m+1)th peak point.

[0058] The sympathetic nerve evaluation unit is configured to fuse blood pressure fluctuation, heart rate long-range fluctuation, and respiratory-blood pressure coupling strength, and realize quantitative evaluation of the sympathetic nerve according to the following formula:

[0059] SAI = w1·HRV LF +w2·SPV + w3·DPV + w4·(1-PLV RB )+w5·Gain LF

[0060]

[0061] Wherein, SAI represents sympathetic nerve activity, SPV represents a standard deviation of a systolic pressure sequence, DPV represents a standard deviation of a diastolic pressure sequence, HRV LF represents a low-frequency part power spectrum of the heart rate, and PLV represents a respiratory-blood pressure coupling strength.RB is the phase-lock value between the proximal end respiration signal and the systolic blood pressure sequence, HR(f) is the Fourier discrete spectrum of HR(m), PSD(HR(f)) represents the power spectral density of HR(f), ψ(R2(n)) represents the phase of the proximal end respiration signal R2(n), ψ(SBP(n)) represents the phase of the systolic blood pressure sequence SBP(n), j is an imaginary unit, e is a natural constant, Gain LF is a transfer function gain, P RB (f) is the cross-spectral density between the proximal end respiration signal and the systolic blood pressure sequence at frequency f, P RR represents the auto-spectral density of the proximal end respiration signal at frequency f, w1, w2, w3, w4, w5 are variable coefficients determined by calibration; N represents the number of sampling points;

[0062] a vagus nerve evaluation unit, for fusing heart rate short-range fluctuation and respiration-heart rate coupling strength, and performing quantitative evaluation of the vagus nerve according to the following formula:

[0063] PAI = v1·HRV HF + v2·(1-PLV RH ) + v3·Gain HF

[0064]

[0065]

[0066] In the formula, PAI represents vagus nerve activity, HRV HF is the high-frequency part power spectrum of heart rate, PLV RH is the phase-lock value between the proximal end respiration signal and the heart rate sequence, ψ(R2(n)) represents the phase of the proximal end respiration signal R2(n), and ψ(HR(n)) represents the phase of the heart rate sequence HR(n), P RH (f) is the cross-spectral density between the proximal end respiration signal and the heart rate sequence at frequency f, P RR (f) represents the auto-spectral density of the proximal end respiration signal at frequency f, and v1, v2, v3 are variable coefficients determined by calibration. A portable multi-physiological parameter measurement method comprises the following steps:

[0067] acquire PPG signals and three-axis acceleration signal changes derived from chest, abdominal or head fluctuations during respiration of a user, and filter low-frequency drift and high-frequency noise in the acquired signals;

[0068] obtain a peak point envelope signal of the PPG signal as a distal end respiration signal;

[0069] Modal decomposition is performed on the triaxial acceleration signal to obtain multiple intrinsic modes, and the intrinsic mode with the maximum power spectral density in the preset frequency range is extracted as the near-end respiratory signal;

[0070] The near-end respiratory signal and the far-end respiratory signal are time-aligned at the millisecond level, and the time delay between the near-end respiratory signal and the far-end respiratory signal is calculated;

[0071] The time delay is input into the pre-constructed RDT mapping model as a delay parameter, and the blood pressure parameter is calculated, wherein the RDT mapping model is used to calculate the blood pressure parameter according to the delay parameter;

[0072] According to the far-end respiratory signal and the near-end respiratory signal, the respiratory depth, the respiratory mode matching degree, the respiratory frequency and the inhalation-exhalation ratio are calculated.

[0073] The blood pressure parameter, the respiratory parameter and the heart rate variation feature are fused to realize quantitative evaluation of the sympathetic nerve and the vagus nerve.

[0074] Compared with the prior art, the present application has the following advantages:

[0075] (1) From the technical point of view, the present application extracts the far-end respiratory signal based on the PPG signal, extracts the near-end respiratory signal based on the acceleration signal, and realizes the calculation of multiple respiratory parameters according to the far-end respiratory signal and the near-end respiratory signal, so that the calculation result is more accurate. The present application also calculates the blood pressure parameter according to the RDT model using the time delay between the far-end respiratory signal and the near-end respiratory signal, which is based on the respiratory diffusion process and has more advantages than the PTT model in continuous acquisition, environmental requirements and signal source types;

[0076] (2) From the economic point of view, the present application has lower requirements for hardware and sensor types, and is suitable for basic wearable devices and most mobile devices. Without introducing other sensors (such as ECG sensors, wrist pulse pressure sensors, respiratory pressure sensors, etc.), the present application realizes accurate determination of multiple physiological parameters such as respiration and blood pressure, effectively reducing the cost of personal health management. BRIEF DESCRIPTION OF DRAWINGS

[0077] Figure 1 is a structural schematic diagram of a portable multi-physiological parameter measuring device provided by the embodiment of the present application;

[0078] Figure 2 is a schematic diagram of signal acquisition and respiratory signal extraction principle of the present application;

[0079] Figure 3 is a schematic diagram of blood pressure parameter calculation principle of the present application;

[0080] Figure 4is a schematic diagram of a principle of calculating a respiratory parameter of the present application. DETAILED DESCRIPTION

[0081] The technical solutions in the embodiments of the present application will be clearly and completely described in combination with the drawings in the embodiments of the present application.

[0082] Embodiment one

[0083] The embodiments of the present application provide a portable multi-physiological parameter measuring device, as shown in the drawings, comprising: Figure 1

[0084] The signal acquisition and preprocessing module is configured to acquire PPG signals of a user and three-axis acceleration signal changes derived from chest, abdominal or head fluctuations in the breathing process, and filter low-frequency drift and high-frequency noise in the acquired signals.

[0085] The PPG-derived far-end respiratory signal extraction module is configured to obtain a peak point envelope signal of the PPG signal as a far-end respiratory signal.

[0086] The acceleration-derived near-end respiratory signal extraction module is configured to perform modal decomposition on the three-axis acceleration signals to obtain a plurality of intrinsic modes, and extract an intrinsic mode with the maximum power spectral density in a preset frequency range as a near-end respiratory signal.

[0087] The time delay calculation module is configured to perform millisecond-level time sequence alignment on the near-end respiratory signal and the far-end respiratory signal, and calculate a time delay between the near-end respiratory signal and the far-end respiratory signal.

[0088] The blood pressure parameter calculation module is configured to input the time delay as a delay parameter into a pre-constructed RDT mapping model to calculate a blood pressure parameter, wherein the RDT mapping model is configured to calculate the blood pressure parameter according to the delay parameter.

[0089] The respiratory parameter extraction module is configured to calculate a respiratory parameter including a breathing depth, a breathing mode matching degree, a breathing frequency and an inhalation-exhalation ratio according to the far-end respiratory signal and the near-end respiratory signal.

[0090] The autonomic nerve evaluation module is configured to fuse the blood pressure parameter, the respiratory parameter and heart rate variability characteristics to realize quantitative evaluation of sympathetic nerves and vagus nerves.

[0091] In specific implementation, as shown in the drawings, Figure 2 ​As shown, the signal acquisition and preprocessing module can be a wearable device, which is provided with a photoelectric unit, an accelerometer and a preprocessing unit. The photoelectric unit is used to acquire the PPG signal of the user. The accelerometer is used to acquire the three-axis acceleration signal changes derived from the chest, abdominal or head fluctuation in the breathing process. The preprocessing unit is used to sequentially perform band-pass, non-phase shift and finite impulse response filtering on the PPG signal and the three-axis acceleration signal, wherein the PPG signal is subjected to 0.05-5Hz band-pass, and the three-axis acceleration signal is subjected to 0.05-2Hz band-pass. The wearable device can be a smart watch, a smart bracelet, a smart ring, a smart earphone, a smart glasses, a smart fabric or other wearable devices with PPG detection and acceleration detection functions, or any combination of wearable devices with PPG detection function and wearable devices with acceleration detection function. The photoelectric unit can be realized based on one or more of visible light or non-visible light such as red light, green light and infrared light. Specifically, for limb-type wearable devices such as smart watches, smart bracelets and smart rings, after the user wears the wearable device and places the limbs on the chest, abdomen or head, the photoelectric module and the built-in three-axis accelerometer of the device are used to acquire the PPG signal and the chest, abdominal or head fluctuation in the breathing process. For head-mounted wearable devices such as smart earphones and smart glasses, after the user wears the device, the photoelectric module and the built-in three-axis accelerometer of the device are used to acquire the PPG signal and the head fluctuation in the breathing process. The PPG-derived far-end respiratory signal extraction module, the acceleration-derived near-end respiratory signal extraction module, the time delay calculation module, the blood pressure parameter calculation module and the respiratory parameter extraction module can be integrated into an application installed on the wearable device, or installed on other electronic devices such as mobile phones and tablets.

[0092] In other embodiments, the signal acquisition and preprocessing module can also be a mobile device, or any combination of a mobile device, a mobile device and a wearable device. The mobile device is provided with a flash, a camera, an accelerometer and a data acquisition and processing unit. The flash and the camera are controlled by the data acquisition and processing unit to flash the flash and take pictures of the measurement site at a predetermined frequency. The accelerometer is used to acquire the three-axis acceleration signal changes derived from the chest, abdominal or head fluctuation in the breathing process. The data acquisition and processing unit can be a program installed on the mobile device, which is used to extract the PPG signal of the user according to the pictures, and sequentially perform band-pass, non-phase shift and finite impulse response filtering on the PPG signal and the three-axis acceleration signal. Figure 2As shown, the mobile device is placed on the user's chest and abdomen, the PPG measurement site is placed at the flash and camera, when the user is detected to place the mobile device on the chest / abdomen and press the PPG measurement site against the flash and camera in the specified manner, the acceleration signal is collected at a frequency of 20Hz to obtain the chest / abdominal fluctuation three-axis acceleration signal; At the same time, configure the flash to pulse modulation (PWM) mode, adjust the light intensity of the LED lamp to 50%-70% (avoid overexposure / underexposure), collect the camera sensor raw imaging parameters at a frequency of 20Hz; Separate the RGB three channels from the raw photo data, and select the green channel as the source of the PPG main signal; Normalize the brightness data to eliminate ambient light baseline interference and obtain the PPG signal:

[0093]

[0094] Wherein, the mobile device can be a smartphone, a tablet computer or any mobile device with an optoelectronic module and a three-axis accelerometer, the PPG measurement site can be a finger, a wrist or any body part with significant hemodynamics, the flash can be green, white or any color light different from the color of blood; The camera raw imaging parameters can be measured by a single camera, or can be obtained by fusing multiple camera sampling raw imaging parameters. The PPG-derived distal respiratory signal extraction module, the acceleration-derived proximal respiratory signal extraction module, the time delay calculation module, the blood pressure parameter calculation module, and the respiratory parameter extraction module can be integrated into an application installed on the mobile device.

[0095] The PPG-derived distal respiratory signal extraction module specifically comprises:

[0096] The segmentation unit is configured to divide the collected PPG signal into a plurality of PPG signal segments.

[0097] The standard deviation calculation unit is configured to calculate the standard deviation of the PPG signal segment and the template waveform according to the following formula based on a pre-constructed PPG template waveform:

[0098]

[0099] In the formula, SD is the standard deviation, S(n) is the sampled PPG signal, T(n) is the PPG template waveform, and N is the total number of PPG signal segment sampling points.

[0100] The judgment unit is configured to determine whether the SD is less than a set threshold SDT, if yes, it is determined that the current PPG signal segment is valid, otherwise it is determined that the current PPG signal segment is invalid.

[0101] A telecentric end respiration signal generation unit is configured to search for peak points of valid PPG signal segments in the PPG signal, connect all the peak points, and fill in data of invalid PPG signal segments by cubic spline interpolation to obtain a processed PPG signal, and then resample the processed PPG signal to obtain a telecentric end respiration signal R1(n).

[0102] The acceleration-derived proximal end respiration signal extraction module specifically includes:

[0103] A modal decomposition unit is configured to perform modal decomposition on the three-axis acceleration signal to obtain an intrinsic modal set {IMF i , i = 1, 2, …, M}, where IMF i is the i th intrinsic mode obtained after modal decomposition of the three-axis acceleration signal, and M is the number of intrinsic modes; and the modal decomposition can be EMD decomposition.

[0104] A power spectral density calculation unit is configured to calculate the power spectral density of each intrinsic mode in the frequency domain.

[0105] A proximal end respiration signal generation unit is configured to calculate the proximal end respiration signal according to the following formula:

[0106]

[0107] In the formula, R2(n) represents the proximal end respiration signal, PSD(IMF i (f)) represents the power spectral density of IMF i at frequency f.

[0108] As shown in Figure 3 , the time delay calculation module specifically includes:

[0109] A time alignment unit is configured to obtain a signal sampling timestamp of a device on-board crystal oscillator of the signal acquisition and preprocessing module, so as to millisecond-level align the timing of the telecentric end respiration signal and the proximal end respiration signal.

[0110] A peak extraction unit is configured to extract peak points of the telecentric end respiration signal and the proximal end respiration signal.

[0111] A time delay unit is configured to calculate a time delay between the telecentric end respiration signal and the proximal end respiration signal at the peak points as a time delay between the telecentric end respiration signal and the proximal end respiration signal in a current respiration cycle, i.e., a blood conduction time.

[0112] The blood pressure parameter calculation module specifically includes:

[0113] The morphological parameter extraction unit is configured to search for valley points before and after the peak point of the proximal end respiratory signal, obtain the start time and the end time of the current respiratory cycle according to the peak point and the valley points, and search for a PPG signal segment located in the start time and the end time of the respiratory cycle in the PPG signal, and calculate the average rising time and the average falling time of all PPG waveforms in the signal segment;

[0114] The blood pressure calculation unit is configured to input the time delay and the average heart rate into a pre-constructed RDT mapping model to obtain blood pressure parameters, wherein the pre-constructed RDT mapping model is specifically:

[0115]

[0116] In the formula, SBP and DBP respectively represent the systolic pressure and the diastolic pressure, a S , b S , c S , d S , a D , b D , c D , d D are adjustment coefficients obtained by fitting historical data by using a multiple linear regression function, τ is the time delay, UT is the average rising time of the PPG waveform, and DT is the average falling time of the PPG waveform.

[0117] As shown in Figure 4 , the respiratory parameter extraction module specifically includes:

[0118] The respiratory depth calculation unit is configured to calculate the average amplitude A1 of the distal end respiratory signal, and calculate the respiratory depth D according to the average amplitude A1:

[0119] D=k·A1

[0120] wherein k is an individualized parameter related to the height, the weight, the vital capacity, the tightness of the wristband, and the like, and can be obtained by calibration, and D is the respiratory depth.

[0121] The respiratory mode matching degree calculation unit is configured to calculate the average amplitude A2 of the proximal end respiratory signal, and calculate the respiratory mode matching degree M according to the average amplitude A1 of the distal end respiratory signal according to the following formula:

[0122]

[0123] In the formula, μ represents the phase synchronization coefficient, N represents the number of sampling points, ψ(R1(n)) represents the phase of the distal end respiratory signal R1(n), ψ(R2(n)) represents the phase of the proximal end respiratory signal R2(n), and M represents the respiratory mode matching degree.

[0124] a respiration frequency calculation unit configured to identify peak points of the proximal end respiration signal, calculate an average value S of interval time between two peak points, and calculate the respiration frequency according to the following formula:

[0125]

[0126] wherein f represents the respiration frequency;

[0127] a breath ratio calculation unit configured to search for valley points before and after each peak point of the proximal end respiration signal, calculate an average value I of interval time between the peak point and the front valley point, and an average value E of interval time between the peak point and the rear valley point, and obtain the breath ratio R according to the following formula:

[0128]

[0129] wherein R represents the breath ratio.

[0130] The autonomic nerve evaluation module specifically comprises:

[0131] a heart rate calculation unit configured to identify peak points in the PPG signal, obtain interval time between two peak points, and calculate a heart rate sequence according to the following formula:

[0132]

[0133] wherein HR(m) represents the mth heart rate value, FM represents the total number of peak points in the PPG signal, and PP(m) represents interval time between the mth peak point and the m+1th peak point.

[0134] a sympathetic nerve evaluation unit configured to fuse blood pressure fluctuation, heart rate long-range fluctuation, and respiration-blood pressure coupling strength, and realize quantitative evaluation of the sympathetic nerve according to the following formula:

[0135] SAI = w1·HRV LF +w2·SPV + w3·DPV + w4·(1-PLV RB )+w5·Gain LF

[0136]

[0137] wherein SAI represents sympathetic nerve activity, SPV represents standard deviation of the systolic pressure sequence, DPV represents standard deviation of the diastolic pressure sequence, HRV LF represents low-frequency part power spectrum of the heart rate, and PLV RBis the phase lock value between the proximal respiratory signal and the systolic pressure sequence, HR(f) is the Fourier discrete spectrum of HR(m), PSD(HR(f)) represents the power spectral density of HR(f), ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), ψ(SBP(n)) represents the phase of the systolic pressure sequence SBP(n), j is an imaginary unit, e is a natural constant, Gain LF is the transfer function gain, P RB (f) is the cross spectral density between the proximal respiratory signal and the systolic pressure sequence at frequency f, P RR represents the autospectral density of the proximal respiratory signal at frequency f, w1, w2, w3, w4, and w5 are the variable coefficients determined by calibration; N represents the number of sampling points;

[0138] The vagus nerve assessment unit is used to integrate short-term heart rate fluctuations and respiratory-heart rate coupling strength to perform quantitative vagus nerve assessment according to the following formula:

[0139] PAI=v1·HRV HF +v2·(1-PLV RH )+v3·Gain HF

[0140]

[0141] Where PAI represents vagus nerve activity, HRV HF is the power spectrum of the high frequency part of the heart rate, PLV RH is the phase lock value between the proximal respiratory signal and the heart rate sequence, ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), ψ(HR(n)) represents the phase of the heart rate sequence HR(n), P RH (f) is the cross spectral density between the proximal respiratory signal and the heart rate sequence at frequency f, P RR (f) represents the autospectral density of the proximal respiratory signal at frequency f, and v1, v2, and v3 are the variable coefficients determined by calibration.

[0142] It is worth noting that in the embodiment of the above-mentioned device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0143] The embodiments described above are only illustrative, wherein the modules illustrated as separate components can or can not be physically separated, and the components shown as modules can or can not be physical modules, i.e., can be located in one place or distributed to multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments. Those skilled in the art can clearly understand that each embodiment can be realized by means of software and necessary general hardware platform, and of course, can also be realized only by hardware, as long as the function or effect can be realized.

[0144] Embodiment two

[0145] The embodiment of the present application provides a portable multi-physiological parameter measurement method, comprising:

[0146] The PPG signal of the user and the three-axis acceleration signal changes derived from the chest, abdomen or head fluctuation in the breathing process are collected, and low-frequency drift and high-frequency noise in the collected signal are filtered out;

[0147] The peak point envelope signal of the PPG signal is obtained as a far-end respiratory signal;

[0148] The three-axis acceleration signal is modally decomposed to obtain a plurality of intrinsic modes, and the intrinsic mode with the maximum power spectral density is extracted in a preset frequency range as a near-end respiratory signal;

[0149] The near-end respiratory signal and the far-end respiratory signal are aligned in millisecond-level time sequence, and the time delay between the near-end respiratory signal and the far-end respiratory signal is calculated;

[0150] The time delay is input as a delay parameter into a pre-constructed RDT mapping model to calculate a blood pressure parameter, wherein the RDT mapping model is used to calculate the blood pressure parameter according to the delay parameter;

[0151] According to the far-end respiratory signal and the near-end respiratory signal, the respiratory depth, the respiratory mode matching degree, the respiratory frequency and the inhalation-exhalation ratio are calculated.

[0152] The blood pressure parameter, the respiratory parameter and the heart rate variability feature are fused to realize quantitative evaluation of the sympathetic nerve and the vagus nerve.

[0153] The calculation step of the far-end respiratory signal specifically comprises:

[0154] The collected PPG signal is divided into a plurality of PPG signal segments;

[0155] According to the pre-constructed PPG template waveform, the standard deviation of the PPG signal segment and the template waveform is calculated according to the following formula:

[0156]

[0157] In the formula, SD is a standard deviation, S(n) is a PPG signal, T(n) is a PPG template waveform, and N is a total number of PPG signal segment sampling points.

[0158] It is judged whether SD is less than a set threshold SDT, and if yes, it is determined that the current PPG signal segment is valid, and if not, it is determined that the current PPG signal segment is invalid.

[0159] The peak points of the valid PPG signal segments in the PPG signal are searched, all the peak points are connected, and the data of the invalid PPG signal segments is filled through interpolation, to obtain a processed PPG signal, and then the processed PPG signal is resampled to obtain a telecentric end respiratory signal R1(n).

[0160] The calculation steps of the proximal end respiratory signal specifically include:

[0161] The three-axis acceleration signal is subjected to modal decomposition to obtain an intrinsic modal set {IMF i , i = 1, 2, …, M}, IMF i is the i th intrinsic mode obtained after modal decomposition of the three-axis acceleration signal, and M is the number of intrinsic modes; wherein the modal decomposition can adopt EMD decomposition;

[0162] The power spectral density of each intrinsic mode in the frequency domain is calculated.

[0163] The proximal end respiratory signal is calculated according to the following formula:

[0164]

[0165] In the formula, R2(n) represents the proximal end respiratory signal, PSD(IMF i (f)) represents the power spectral density of IMF i at frequency f.

[0166] The specific steps of the time delay calculation specifically include:

[0167] The signal sampling time stamp of the signal acquisition and preprocessing module is acquired, so as to millisecond-level align the time sequences of the telecentric end respiratory signal and the proximal end respiratory signal;

[0168] The peak points of the telecentric end respiratory signal and the proximal end respiratory signal are extracted.

[0169] The time delay between the telecentric end respiratory signal and the proximal end respiratory signal at the peak points is calculated as the time delay between the telecentric end respiratory signal and the proximal end respiratory signal in the current respiratory cycle, i.e., the blood conduction time.

[0170] The specific steps of the blood pressure parameter calculation specifically include:

[0171] Search for valley points before and after the peak point of the proximal end respiratory signal, obtain the start and end time of the current respiratory cycle according to the peak point and the valley point, and find the PPG signal segment located in the start and end time of the respiratory cycle in the PPG signal, calculate the average rising and falling time of all PPG waveforms in the signal segment, which is used to calibrate the indirect influence of PPG morphological changes on the time delay between the distal end respiratory signal and the proximal end respiratory signal;

[0172] Input the time delay and the average heart rate into the pre-constructed RDT mapping model to calculate the blood pressure parameters, wherein the pre-constructed RDT mapping model is specifically:

[0173]

[0174] In the formula, SBP and DBP are systolic pressure and diastolic pressure respectively, a S , b S , c S , d S , a D , b D , c D , d D are adjustment coefficients fitted by historical data using a multiple linear regression function, τ is the time delay, UT is the average rising time of the PPG waveform, and DT is the average falling time of the PPG waveform.

[0175] The calculation step of the respiratory parameter specifically includes:

[0176] Calculate the average amplitude A1 of the distal end respiratory signal, and calculate the respiratory depth according to the average amplitude A1:

[0177] D=k·A1

[0178] Wherein, k is an individualized parameter related to height, weight, vital capacity, wristband wearing tightness, etc., which can be obtained by calibration, and D is the respiratory depth;

[0179] Calculate the average amplitude A2 of the proximal end respiratory signal, and combine the average amplitude A1 of the distal end respiratory signal to calculate the respiratory mode matching degree according to the following formula:

[0180]

[0181] In the formula, μ represents the phase synchronization coefficient, N represents the number of sampling points, ψ(R1(n)) represents the phase of the distal end respiratory signal R1(n), ψ(R2(n)) represents the phase of the proximal end respiratory signal R2(n), and M represents the respiratory mode matching degree.

[0182] The peak points of the proximal end respiration signal are identified, the average value S of the interval time between each two peak points is calculated, and the respiration frequency is calculated according to the following formula:

[0183]

[0184] In the formula, f represents the respiration frequency;

[0185] The valley points before and after each peak point of the proximal end respiration signal are searched, the average value I of the interval time between the front valley point and the peak point and the average value E of the interval time between the rear valley point and the peak point are calculated, and the inspiration-expiration ratio is obtained:

[0186]

[0187] In the formula, R represents the inspiration-expiration ratio.

[0188] The autonomic nerve evaluation step specifically includes:

[0189] The peak points in the PPG signal are identified, the interval time between each two peak points is obtained, and the heart rate sequence is calculated according to the following formula:

[0190]

[0191] In the formula, HR(m) represents the mth heart rate value, FM is the total number of peak points of the PPG signal, and PP(m) represents the interval time between the mth peak point and the m+1 peak point;

[0192] The blood pressure fluctuation, the long-range fluctuation of the heart rate, and the respiration-blood pressure coupling strength are fused, and the quantitative evaluation of the sympathetic nerve is realized according to the following formula:

[0193] SAI = w1·HRV LF +w2·SPV + w3·DPV + w4·(1-PLV RB )+w5·Gain LF

[0194]

[0195] In the formula, SAI represents the sympathetic nerve activity, SPV is the standard deviation of the systolic pressure sequence, DPV is the standard deviation of the diastolic pressure sequence, HRV LF is the low-frequency part power spectrum of the heart rate, PLV RB is the phase locking value between the proximal end respiration signal and the systolic pressure sequence, HR(f) is the Fourier discrete spectrum of HR(m), PSD(HR(f)) represents the power spectral density of HR(f), ψ(R2(n)) represents the phase of the proximal end respiration signal R2(n), ψ(SBP(n)) represents the phase of the systolic pressure sequence SBP(n), j is the imaginary unit, e is the natural constant, and Gain LFFor transfer function gain, P RB (f) is the cross-spectral density between the near-end respiratory signal and the systolic pressure sequence at frequency f, P RR represents the auto-spectral density of the near-end respiratory signal at frequency f, w1, w2, w3, w4, w5 are variable coefficients determined by calibration; N represents the number of sampling points;

[0196] The vagus nerve is quantitatively evaluated according to the following formula by fusing the short-range fluctuation of heart rate and the strength of respiratory-heart rate coupling:

[0197] PAI = v1·HRV HF + v2·(1-PLV RH ) + v3·Gain HF

[0198]

[0199] In the formula, PAI represents the vagus nerve activity, HRV HF is the high-frequency part of the power spectrum of heart rate, PLV RH is the phase locking value between the near-end respiratory signal and the heart rate sequence, ψ(R2(n)) represents the phase of the near-end respiratory signal R2(n), and ψ(HR(n)) represents the phase of the heart rate sequence HR(n), P RH (f) is the cross-spectral density between the near-end respiratory signal and the heart rate sequence at frequency f, P RR (f) represents the auto-spectral density of the near-end respiratory signal at frequency f, and v1, v2, v3 are variable coefficients determined by calibration.

[0200] The method provided by the embodiment of the application is implemented based on the device of the first embodiment, and has the corresponding functions and advantages of the device.

[0201] It should be understood that the above embodiments and descriptions in the specification are only principles, main features and advantages of the application, and the application can have various changes and improvements without departing from the spirit and scope of the application. These changes and improvements fall within the scope of protection of the application.

Claims

1. A portable multi-physiological parameter measuring device, characterized in that: include: The signal acquisition and preprocessing module is used to collect the user's PPG signal and the changes in the three-axis acceleration signal derived from the rise and fall of the chest, abdomen, or head during breathing, and filter out low-frequency drift and high-frequency noise in the collected signal; A PPG-derived distal respiratory signal extraction module is used to obtain the peak point envelope signal of the PPG signal as the distal respiratory signal; The acceleration-derived proximal respiratory signal extraction module is used to perform modal decomposition on the triaxial acceleration signal to obtain multiple eigenmodes, and extract the eigenmode with the maximum power spectral density within a preset frequency range as the proximal respiratory signal; A time delay calculation module is used to perform millisecond-level timing alignment on the proximal respiratory signal and the distal respiratory signal, and calculate the time delay between the proximal respiratory signal and the distal respiratory signal; a blood pressure parameter calculation module, configured to input the time delay as a delay parameter into a pre-built RDT mapping model to calculate the blood pressure parameter, wherein the RDT mapping model is configured to calculate the blood pressure parameter based on the delay parameter; The respiratory parameter extraction module is used to calculate the respiratory parameters, including breathing depth, breathing pattern matching, respiratory rate, and inspiration-expiration ratio, based on the distal and proximal respiratory signals. The autonomic nervous system assessment module is used to integrate blood pressure parameters, respiratory parameters, and heart rate variability characteristics to achieve quantitative assessment of the sympathetic and vagus nerves.

2. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The signal acquisition and preprocessing module is specifically a wearable device, which is provided with a photoelectric unit, an accelerometer and a preprocessing unit. The photoelectric unit is used to collect the user's PPG signal, and the accelerometer is used to collect the changes in the three-axis acceleration signal derived from the ups and downs of the chest, abdomen or head during breathing. The preprocessing unit is used to perform bandpass, phase-shift-free, and finite impulse response filtering on the PPG signal and the three-axis acceleration signal in sequence.

3. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The signal acquisition and preprocessing module is specifically a mobile device, which is equipped with a flash, a camera, an accelerometer, and a data acquisition and processing unit. Under the control of the data acquisition and processing unit, the flash and camera flash at a predetermined frequency and the camera captures an image of the measurement site. The accelerometer is used to collect changes in the three-axis acceleration signal derived from the rise and fall of the chest, abdomen, or head during breathing. The data acquisition and processing unit is used to extract the user's PPG signal based on the image and sequentially perform bandpass, phase-shift-free, and finite impulse response filtering on the PPG signal and the three-axis acceleration signal.

4. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The PPG-derived distal respiratory signal extraction module specifically includes: A segmentation unit, configured to divide the collected PPG signal into a plurality of PPG signal segments; The standard deviation calculation unit is used to calculate the standard deviation between the PPG signal segment and the template waveform according to the following formula based on the pre-built PPG template waveform: ， Where, is the standard deviation, is the sampled PPG signal, is the PPG template waveform, is the total number of sampling points of the PPG signal segment; Judgment unit, used to determine whether SD is less than the set threshold If yes, the current PPG signal segment is determined to be valid; otherwise, the current PPG signal segment is determined to be invalid; The distal respiratory signal generation unit is used to search for the peak points of the valid PPG signal segment in the PPG signal, connect all the peak points, and fill the data of the invalid PPG signal segment by interpolation to obtain the processed PPG signal, and then resample the processed PPG signal to obtain the distal respiratory signal .

5. The portable multi-physiological parameter measuring device according to claim 4, characterized in that: The acceleration-derived proximal respiratory signal extraction module specifically includes: Modal decomposition unit, used to perform modal decomposition on the three-axis acceleration signal to obtain the eigenmode set , is the i-th eigenmode obtained after modal decomposition of the triaxial acceleration signal, is the number of eigenmodes; A power spectrum density calculation unit, used to calculate the power spectrum density of each eigenmode in the frequency domain; The proximal end respiratory signal generating unit is used to calculate the proximal end respiratory signal according to the following formula: ， Where, Represents the proximal respiratory signal, express The power spectral density at frequency f.

6. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The time delay calculation module specifically includes: The time alignment unit is used to obtain the signal sampling timestamps of the signal acquisition and preprocessing modules, thereby aligning the timing of the distal end respiratory signal with the proximal end respiratory signal at the millisecond level; A peak extraction unit, used to extract peak points of the distal end respiratory signal and the proximal end respiratory signal; The time delay unit is used to calculate the time delay between the distal end respiratory signal and the proximal end respiratory signal at the peak points as the time delay between the distal end respiratory signal and the proximal end respiratory signal in the current respiratory cycle.

7. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The blood pressure parameter calculation module specifically includes: A morphological parameter extraction unit is used to search for valley points before and after the peak point of the proximal respiratory signal, obtain the start and end time of the current respiratory cycle based on the peak and valley points, search the PPG signal for a PPG signal segment within the start and end time of the respiratory cycle, and calculate the average rise and fall time of all PPG waveforms in the signal segment; The blood pressure calculation unit is used to input the time delay into a pre-built RDT mapping model to calculate the blood pressure parameters, wherein the pre-built RDT mapping model is specifically: ， Where, 、 are systolic and diastolic blood pressure, respectively. 、 、 、 、 、 、 、 is the adjustment coefficient obtained by fitting the historical data using the multiple linear regression function. is the time delay, is the average rise time of the PPG waveform, is the average fall time of the PPG waveform.

8. The portable multi-physiological parameter measuring device according to claim 5, characterized in that: The respiratory parameter extraction module specifically includes: Respiratory depth calculation unit, used to calculate the average amplitude of the distal respiratory signal , based on the average amplitude Calculate the breathing depth: ， in, is the individualized parameter, is the depth of breathing; Respiratory mode matching calculation unit, used to calculate the average amplitude of the proximal respiratory signal , and combined with the average amplitude of the distal respiratory signal , the breathing mode matching degree is calculated according to the following formula: ， ， Where, represents the phase synchronization coefficient, Indicates the number of sampling points of the PPG signal segment, Represents distal respiratory signal The phase, Represents the proximal respiratory signal The phase, Indicates the matching degree of breathing method; Respiratory frequency calculation unit, used to identify the peak point of the proximal respiratory signal and calculate the average time between the two peak points , and the respiratory rate is calculated according to the following formula: ， Where, Indicates respiratory rate; The I / O ratio calculation unit is used to search for valley points before and after each peak point of the proximal respiratory signal and calculate the average time interval between the valley point and the peak point. , and the average time interval between the valley point and the peak point , and get the inspiration-expiration ratio: ， Where, Indicates the inspiration-expiration ratio.

9. The portable multi-physiological parameter measuring device according to claim 5, characterized in that: The autonomic nerve assessment module specifically includes: The heart rate calculation unit is used to identify the peak points in the PPG signal, obtain the time interval between each peak point, and calculate the heart rate sequence using the following formula: ， Where, represents the nth heart rate value, is the total peak points of the PPG signal, Indicates the time interval between the nth peak point and the n+1th peak point; The sympathetic nerve assessment unit is used to integrate blood pressure fluctuations, long-term heart rate fluctuations, and respiratory-blood pressure coupling strength to achieve quantitative assessment of the sympathetic nerves according to the following formula: ， ， ， ， Where, Indicates sympathetic nerve activity, is the standard deviation of the systolic blood pressure series, is the standard deviation of the diastolic pressure series, is the power spectrum of the low-frequency part of the heart rate, is the phase lock value between the proximal respiratory signal and the systolic pressure sequence, for The Fourier discrete spectrum of express The power spectral density, Represents the proximal respiratory signal The phase, Represents systolic blood pressure series The phase, is the imaginary unit, is a natural constant, is the transfer function gain, is the cross spectral density between the proximal respiratory signal and the systolic pressure sequence at frequency f, represents the autospectral density of the proximal respiratory signal at frequency f, 、 、 、 、 The coefficient of variation determined for the calibration; Indicates the total number of sampling points of the PPG signal segment; The vagus nerve assessment unit is used to integrate short-term heart rate fluctuations and respiratory-heart rate coupling strength to perform quantitative vagus nerve assessment according to the following formula: ， ， ， ， Where, Indicates vagus nerve activity, is the power spectrum of the high frequency part of the heart rate, is the phase lock value between the proximal respiratory signal and the heart rate sequence, Represents the proximal respiratory signal The phase, Represents heart rate sequence Phase, is the cross spectral density between the proximal respiratory signal and the heart rate sequence at frequency f, represents the autospectral density of the proximal respiratory signal at frequency f, 、 、 The coefficient of variation determined for the calibration.

10. A portable multi-physiological parameter measurement method, characterized in that: include: Collect the user's PPG signal and the changes in the three-axis acceleration signal derived from the rise and fall of the chest, abdomen, or head during breathing, and filter out low-frequency drift and high-frequency noise in the collected signal; Obtain the peak envelope signal of the PPG signal as the distal respiratory signal; Perform modal decomposition on the triaxial acceleration signal to obtain multiple eigenmodes, and extract the eigenmode with the largest power spectrum density within a preset frequency range as the proximal respiratory signal; Perform millisecond-level timing alignment on the proximal and distal respiratory signals, and calculate the time delay between them. Inputting the time delay as a delay parameter into a pre-built RDT mapping model to calculate a blood pressure parameter, wherein the RDT mapping model is used to calculate the blood pressure parameter based on the delay parameter; According to the distal respiratory signal and the proximal respiratory signal, the respiratory depth, respiratory mode matching, respiratory rate and inspiration-expiration ratio are calculated; The blood pressure parameters, respiratory parameters, and heart rate variability characteristics are integrated to achieve quantitative evaluation of the sympathetic and vagus nerves.

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