Continuous noninvasive physiological parameter detection method and device

Abstract

The invention relates to a continuous noninvasive physiological parameter detection method and device, and belongs to the technical field of physiological parameter detection. According to the method, on the basis of photoelectric volume pulse wave detection data under multiple wavelengths, absorbance related characteristic parameters and pulse wave related characteristic parameters are extracted, a hemoglobin concentration detection result is obtained through calculation of a hemoglobin concentration calculation model, and the absorbance related characteristic parameters comprise the adjacent surface difference, the relative difference, the adjacent surface ratio, the relative ratio and the progressive surface ratio. Extracting based on a normalized absorbance fluctuation amplitude curve; the pulse wave characteristic parameters comprise rising branch maximum slope, rising time, falling time, dicrotic wave slope, dicrotic amplitude ratio, rising branch time ratio, low-high frequency energy ratio and harmonic component ratio, and extraction is carried out based on normalized pulse waves. The detection device for implementing the method comprises a probe, a data processing circuit and the like. The accuracy of the noninvasive hemoglobin concentration detection result can be effectively improved.

Description

Technical Field

[0001] The present invention relates to a continuous non-invasive physiological parameter detection method and device, which can be mainly used for non-invasive detection of hemoglobin concentration and belongs to the technical field of physiological parameter detection. Background Art

[0002] The concentrations of different blood components in the human body are important clinical biochemical indicators, such as hemoglobin concentration. Hemoglobin concentration is of great significance for the identification and classification of anemia, clinical transfusion guidance, and the diagnosis of other diseases. Currently, the detection of hemoglobin concentration can be divided into invasive, minimally invasive, and non-invasive tests. Invasive and minimally invasive tests require blood sampling, which will cause pain and psychological panic to the tester. There is also a certain risk of infection in the wound, and continuous, real-time monitoring cannot be achieved. Existing non-invasive tests do not require blood sampling and can achieve continuous, real-time monitoring. However, due to individual differences and limited scope of application, there is still much room for improvement in accuracy, which is not conducive to product implementation and technology promotion. Summary of the Invention

[0003] The purpose of the present invention is to improve the accuracy of non-invasive hemoglobin concentration detection results.

[0004] The technical solution of the present invention is: a continuous non-invasive physiological parameter detection method, which is based on multi-wavelength (using multiple different wavelengths of light) photoplethysmography pulse wave detection data, extracts absorbance-related characteristic parameters and pulse wave-related characteristic parameters, and uses a hemoglobin concentration calculation model based on the absorbance-related characteristic parameters and pulse wave-related characteristic parameters to calculate the hemoglobin concentration detection result.

[0005] Preferably, based on the photoplethysmography detection data, the absorbance fluctuation amplitude at each wavelength is calculated and obtained, and an absorbance fluctuation amplitude curve is fitted according to the absorbance fluctuation amplitude at each wavelength. A number of wavelength windows of set width (WindowSize, or size) are set, and based on the area under the line of the absorbance fluctuation amplitude curve within the wavelength window, some or all of the following characteristic parameters are extracted as absorbance-related characteristic parameters used in the hemoglobin concentration calculation model:

[0006] Neighbor difference: ;

[0007] Relative difference: ;

[0008] Interproximal ratio: ;

[0009] Comparison: ;

[0010] Progressive aspect ratio: ,

[0011] in, is the interproximal difference, is the relative difference, is the interproximal ratio, For relative comparison, is the progressive aspect ratio, is the area under the absorbance fluctuation curve in the jth wavelength window, is the area under the absorbance fluctuation curve in the j-1th wavelength window (and so on), is the area under the absorbance fluctuation curve within the wavelength window selected for calculating the relative difference. The selected The area under the absorbance fluctuation curve within a wavelength window or the sum of the areas under the absorbance fluctuation curve, , It is the sum of the areas under the absorbance fluctuation curves in all wavelength windows before the jth wavelength window. The subscripts j, j-1, j-2, K, K1, K2, KM, etc. are all the areas under the absorbance fluctuation curve. The corresponding wavelength window number.

[0012] The absorbance fluctuation amplitude can be viewed as the peak-to-peak value of an absorbance curve (a curve showing the absorbance variation over time at a specific wavelength). The absorbance fluctuation amplitudes at different wavelengths are typically different, so an absorbance fluctuation amplitude curve (a curve showing the absorbance fluctuation amplitude versus wavelength) can be fitted based on the absorbance fluctuation amplitudes at different wavelengths. During testing, several complete absorbance waveforms can be obtained at any wavelength, and the average of the peak-to-peak values of these waveforms can be used as the absorbance fluctuation amplitude at that wavelength.

[0013] Preferably, the absorbance fluctuation amplitude curve is normalized before extracting the absorbance-related features, and the absorbance-related characteristic parameters are extracted based on the normalized absorbance fluctuation amplitude curve (the normalized absorbance fluctuation amplitude curve). For example, when the absorbance-related characteristic parameters include or relate to the area below the line of the absorbance fluctuation amplitude curve within a wavelength window, the area below the line is the area below the line of the normalized absorbance fluctuation amplitude curve within the corresponding wavelength window.

[0014] Preferably, the absorbance fluctuation amplitude curve is normalized by setting the reference wavelength (The reference wavelength should generally be within the wavelength range used for photoplethysmography testing. For example, one of the multiple wavelengths used for testing can be selected as the reference wavelength.) The absorbance fluctuation amplitude or absorbance fluctuation amplitude curve at each wavelength is normalized using the following formula:

[0015] ,

[0016] in wavelength The normalized absorbance fluctuation amplitude at the reference wavelength is 1. is the wavelength The absorbance fluctuation amplitude under Reference wavelength The absorbance fluctuation amplitude under .

[0017] The above-mentioned normalization operation can be performed on the absorbance fluctuation amplitude before fitting the absorbance fluctuation amplitude curve, and the normalized absorbance fluctuation amplitude is used to fit the normalized absorbance fluctuation amplitude curve. Alternatively, the absorbance fluctuation amplitude can be used to fit the absorbance fluctuation amplitude curve first, and then the above-mentioned normalization operation is performed on the absorbance fluctuation amplitude curve to form a normalized absorbance fluctuation amplitude curve.

[0018] Typically, the multi-wavelength photoplethysmography detection data is derived from photoplethysmography detection performed using at least three different wavelengths of light, for example, 6, 8, 10, or 12 wavelengths.

[0019] Preferably, light of some or all of the following wavelengths is used to perform photoplethysmography detection data at multiple wavelengths: 560nm, 570nm, 660nm, 920nm, 600nm, 620nm, 725nm, 950nm, 805nm, 940nm, 970nm and greater than 1100nm.

[0020] Preferably, a pulse wave (photoplethysmography) is obtained based on the photoplethysmography detection data, and amplitude normalization and heart rate normalization are performed on the pulse wave to obtain a normalized pulse wave (normalized pulse wave). Based on the normalized pulse wave, some or all of the following characteristic parameters are extracted as pulse wave-related characteristic parameters used in the hemoglobin concentration calculation model:

[0021] Maximum slope of ascending branch: ;

[0022] Rise time: For example, the time it takes for the value to rise from 10% of the peak value (10% of the peak value) to 90% of the peak value (90% of the peak value);

[0023] Fall time: For example, the time it takes for the value to fall from the peak value to 50% of the peak value (50% of the peak value);

[0024] Dicrotic wave slope: the slope of the line connecting the dicrotic wave peak and dicrotic wave trough;

[0025] Dicrotic amplitude ratio: ;

[0026] Lifting and lowering time ratio: ;

[0027] Low to high frequency energy ratio: ;

[0028] Harmonic component ratio: for example, the ratio of the third harmonic (amplitude) to the fundamental harmonic (amplitude),

[0029] in, is the maximum slope of the ascending branch; is the normalized pulse wave (normalized pulse wave function); is the dicrotic amplitude ratio; is the main wave amplitude (pulse wave amplitude); The amplitude of the first dicrotic wave after the main wave; is the ratio of lifting and lowering support time; is the rise time; is the descending branch time; It is low-high frequency energy ratio; Low-frequency component energy (the area under the normalized power spectrum density curve in the set low-frequency range); High-frequency component energy (the area under the normalized power spectral density curve in the set high-frequency range).

[0030] Preferably, in a case involving (for example, existing or capable of calculating) normalized pulse waves at several wavelengths, the normalized pulse wave at the set wavelength is used as the normalized pulse wave for extracting pulse wave-related characteristic parameters, or the average value of the normalized pulse waves at each relevant wavelength is used as the normalized pulse wave for extracting pulse wave-related characteristic parameters.

[0031] Preferably, a hemoglobin concentration calculation model based on absorbance-related characteristic parameters and pulse wave-related characteristic parameters is constructed in the following manner: extracting absorbance-related characteristic parameters and pulse wave-related characteristic parameters based on multi-wavelength (using multiple wavelengths of light) photoplethysmography detection data; and constructing a hemoglobin concentration calculation model based on absorbance-related characteristic parameters and pulse wave-related characteristic parameters based on the extracted absorbance-related characteristic parameters and pulse wave-related characteristic parameters and the corresponding hemoglobin concentration detection data using a one-step method or a two-step method:

[0032] 1) One-step method: Using the absorbance-related characteristic parameters and the pulse wave-related characteristic parameters as model input data and the corresponding hemoglobin concentration detection data as model output data, one-step method sample data is constructed. The one-step method sample data is used to construct a hemoglobin concentration calculation model based on the absorbance-related characteristic parameters and the pulse wave-related characteristic parameters. The hemoglobin concentration calculation model based on the absorbance-related characteristic parameters and the pulse wave-related characteristic parameters (the one-step method hemoglobin concentration calculation model based on the absorbance-related characteristic parameters and the pulse wave-related characteristic parameters) can take the following form:

[0033] ,

[0034] in, Table shows hemoglobin concentration, Indicates the absorbance-related characteristic parameters used; Indicates the characteristic parameters related to each pulse wave used;

[0035] 2) Two-step method:

[0036] Step 1: Using the absorbance-related characteristic parameters as model input data and the corresponding hemoglobin concentration detection data as model output data, construct sample data of a first hemoglobin concentration calculation model (a hemoglobin concentration calculation model based on the absorbance-related characteristic parameters), and use the sample data of the first hemoglobin concentration calculation model to construct the first hemoglobin concentration calculation model; using the pulse wave-related characteristic parameters as model input data and the corresponding hemoglobin concentration detection data as model output data, construct sample data of a second hemoglobin concentration calculation model (a hemoglobin concentration calculation model based on the pulse wave-related characteristic parameters), and use the sample data of the second hemoglobin concentration calculation model to construct the second hemoglobin concentration calculation model;

[0037] The first calculation model for hemoglobin concentration can take the following form:

[0038] ,

[0039] in, For the first calculation model hemoglobin concentration, Represents each absorbance-related characteristic parameter;

[0040] The second calculation model for hemoglobin concentration may take the following form:

[0041] ,

[0042] in, For the second calculation model hemoglobin concentration, Indicates the characteristic parameters related to each pulse wave;

[0043] Step 2: Based on the first calculation model for hemoglobin concentration, absorbance-related characteristic parameters are used to calculate and obtain the hemoglobin concentration calculated value of the first calculation model; based on the second calculation model for hemoglobin concentration, pulse wave-related characteristic parameters are used to calculate and obtain the hemoglobin concentration calculated value of the second calculation model; the hemoglobin concentration calculated value of the first calculation model and the hemoglobin concentration calculated value of the second calculation model are used as model input data; corresponding hemoglobin concentration detection data are used as model output data to construct sample data of the hemoglobin concentration calculation model based on the first calculation model for hemoglobin concentration and the second calculation model for hemoglobin concentration; the sample data of the hemoglobin concentration calculation model based on the first calculation model for hemoglobin concentration and the second calculation model for hemoglobin concentration are used to construct the hemoglobin concentration calculation model based on the first calculation model for hemoglobin concentration and the second calculation model for hemoglobin concentration, and use the sample data of the hemoglobin concentration calculation model based on the first calculation model for hemoglobin concentration and the second calculation model for hemoglobin concentration as the hemoglobin concentration calculation model based on the absorbance-related characteristic parameters and the pulse wave-related characteristic parameters;

[0044] The hemoglobin concentration calculation model based on the first hemoglobin concentration calculation model and the second hemoglobin concentration calculation model (the hemoglobin concentration calculation model based on the absorbance-related characteristic parameters and the pulse wave-related characteristic parameters of the two-step method) can take the following forms:

[0045] ,

[0046] or,

[0047] ,

[0048] in, Table shows hemoglobin concentration, is the hemoglobin concentration of the first calculation model (calculated value), is the hemoglobin concentration (calculated value) of the second calculation model, and The coefficients of the first calculation model and the second calculation model respectively, is a constant.

[0049] Continuous non-invasive physiological parameter detection device, including:

[0050] The probe adopts a multi-wavelength photoplethysmography probe for performing multi-wavelength photoplethysmography detection and forming an optical signal for multi-wavelength photoplethysmography detection;

[0051] The host is provided with a signal sampling circuit, a data processing circuit (microprocessor), a human-computer interaction device and a storage device, wherein the signal sampling circuit is used for collecting data of optical signals of multi-wavelength photoplethysmography detection to form multi-wavelength photoplethysmography detection data; the data processing circuit uses any one of the continuous non-invasive physiological parameter detection methods disclosed in the present invention to perform data processing to obtain hemoglobin concentration detection results; the human-computer interaction device is used for human-computer interaction and is provided with a display screen for displaying hemoglobin detection results (usually other data can also be displayed); the storage device is used for data storage and model (hemoglobin concentration calculation model) storage.

[0052] The probe may be any suitable existing technology, for example, a transmission probe, a reflection probe, or a transmission-reflection combination probe; a probe for detecting a finger, or a probe for detecting an arm.

[0053] The beneficial effects of the present invention are as follows: based on the influence mechanism of hemoglobin concentration on the fluctuation amplitude of absorbance at different wavelengths and on the pulse wave morphology, a hemoglobin concentration calculation model based on absorbance-related characteristic parameters and pulse wave-related characteristic parameters is constructed, and more factors related to hemoglobin concentration are incorporated into the calculation of hemoglobin density, thereby not only improving the accuracy of the calculation results, but also helping to avoid or reduce deviations caused by individual factors; since the extraction of each characteristic parameter is based on a normalized correlation curve, it helps to compare the relevant parameters between individuals, further improving the accuracy of the model and the detection results.

[0054] The present invention is used for processing photoplethysmography detection data and obtains hemoglobin concentration detection results through calculation based on a corresponding calculation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is an example of the photoplethysmography (electrical signal) involved in the present invention;

[0056] Figure 2 This is an example of a curve of the absorption coefficient (as a function of wavelength) of different blood components involved in the present invention;

[0057] Figure 3 This is an example of a curve of normalized absorbance fluctuation amplitude (varying with wavelength) at different hemoglobin concentrations according to the present invention;

[0058] Figure 4 Examples of normalized (heart rate normalized and amplitude normalized) pulse waves at different hemoglobin concentrations according to the present invention are provided;

[0059] Figure 5 This is an example of a normalized power spectral density curve at different hemoglobin concentrations according to the present invention;

[0060] Figure 6 Schematic diagram of the structure of the detection device for measuring through the finger part of the present invention;

[0061] Figure 7 Schematic diagram of the structure of the detection device (reflective type) for measuring through the wrist of the present invention;

[0062] Figure 8 A schematic diagram of the structure of a detection device capable of implementing the detection method of the present invention is shown.

[0063] Markings in the figure: 1. Host; 2. Fingertip photoelectric probe; 3. Optical cable; 4. Wrist strap; 5. Reflective probe. DETAILED DESCRIPTION

[0064] The present invention is based on multi-wavelength photoplethysmography detection. According to the different absorbance changes (fluctuation amplitude, which is the difference between the maximum and minimum values, or peak-to-peak value) and different pulse wave morphologies at different wavelengths (detection light wavelength) for different hemoglobin concentrations, based on the corresponding normalized curves, characteristic parameters related to absorbance and pulse wave that are sensitive to hemoglobin concentration are extracted, and a regression model is established as a calculation model for hemoglobin, thereby improving the accuracy of non-invasive measurement results.

[0065] 1. Detection device

[0066] See also Figure 8 and Figure 6 and Figure 7 The present invention can utilize the existing photoelectric volume flow detection device and adopt a multi-wavelength probe to implement the continuous non-invasive physiological parameter detection of the present invention with the support of software. The detection device mainly includes three modules: probe (sensor) acquisition module, signal processing module and human-computer interaction module.

[0067] The probe acquisition module (or probe), taking transmission detection as an example, includes a multi-wavelength light emitter and a photodetector. The multi-wavelength light emitter is composed of multiple wavelength emission lamp cores integrated together to produce an emission lamp sensor that is close to a point light source; the photodetector is a semiconductor device that converts light signals into electrical signals, and can convert the light energy passing through the finger into a current signal.

[0068] The signal processing module is mainly used to obtain electrical signals with clinical value, and requires the acquisition system to have high precision, high stability, high input impedance, high common mode rejection ratio and low noise.

[0069] The LED driver circuit controls the timing and intensity of the light source. It can adjust the current gain and thus the light intensity according to the different finger sizes and structures of different individuals. The light intensity adjustment is performed at the beginning of the measurement, and the measurement is performed after the adjustment is completed. The incident light intensity remains unchanged during the measurement process.

[0070] The microcontroller can control the front-end signal controller in the signal processing module to perform gain control, control the LED drive, and then control the light emitter to realize functions such as probe detection, ADC signal acquisition, physiological signal processing, storage and transmission.

[0071] Physiological signal processing mainly involves extracting the transmitted light intensity and waveform characteristics corresponding to different wavelengths, thereby establishing a mathematical model to achieve the measurement of different blood component concentrations.

[0072] A storage device that stores electrical signals of different wavelengths. The stored data can be read and identified by a PC, and a playback function can be set up based on existing technology.

[0073] The human-computer interaction module can be equipped with a touch LCD display, etc. It has the advantages of low power consumption and small size, and can input and display patient information and corresponding physiological parameter measurement values.

[0074] 2. Non-invasive and continuous hemoglobin concentration measurement algorithm

[0075] The detection method of photoelectric capacitance can adopt existing detection methods, which can be transmission, emission, or a combination of transmission and reflection. The probe (light emitter) used adopts a multi-wavelength light emitter that can emit light of several different wavelengths (frequencies) determined based on the detection method of the present invention. Accordingly, the photodetector can effectively detect or collect these different wavelengths of light.

[0076] See also Figure 1 , based on Beer-Lambert law, the wavelength is When , the change in absorbance (the fluctuation amplitude of absorbance) is recorded as , is the difference between the maximum absorbance and the minimum absorbance:

[0077] (1)

[0078] in, is the incident light intensity. When the blood vessels are dilated to the maximum, the optical path length increases to the maximum value, and the outgoing light intensity is the minimum at this time. When the blood vessels contract to the minimum, the optical path length is reduced to the minimum value, and the output light intensity is the maximum at this time. , is the peak-to-peak value of the pulse wave signal, is a DC signal.

[0079] 1. Extract absorbance-related characteristic parameters based on detection data

[0080] Different components in the blood have different absorption levels for light of the same wavelength. There are specific wavelengths of light that make the various components in the blood absorb the corresponding light energy significantly differently. Therefore, wavelengths with large differences in the absorption coefficients of different components in the blood and some special wavelengths with equal absorption points can be selected. After passing through the tissue, the light of different output intensities corresponding to different wavelengths is received at the receiving end, and the absorbance changes at different wavelengths are calculated (which can be represented by the absorbance fluctuation amplitude). Based on this, the absorbance fluctuation amplitude curve (or function) that changes with wavelength is fitted. After normalization, a normalized absorbance fluctuation amplitude curve is formed. Based on this curve, the area of the normalized absorbance fluctuation amplitude curve at different wavelengths in a certain wavelength window (the area below the line) is calculated. The area below the line changes with the change of wavelength (wavelength window position), compared to the existing single ratio of absorbance changes at different wavelengths. (in, The results are more stable (for two different light wavelengths), and the absorbance-related characteristic parameters related to hemoglobin concentration can be extracted based on this area change.

[0081] The specific method is:

[0082] 1) Select multiple wavelengths for multi-wavelength photoplethysmography detection and calculate the absorbance fluctuation amplitude (the difference between the peak and valley absorbance values) at each wavelength.

[0083] Based on the wavelengths with large differences in the absorption coefficients of different components in the blood and some special wavelengths with isosbestic points, a limited number of wavelengths can be selected for detection. For example:

[0084] Human oxygenated hemoglobin and reduced hemoglobin absorb the maximum green light wavelength, for example: 560nm, 570nm;

[0085] The wavelength at which the absorption difference between oxygenated hemoglobin and reduced hemoglobin in the human body is the largest, for example: 660nm, 920nm;

[0086] The wavelength used to distinguish human oxygenated hemoglobin from methemoglobin, for example: 600nm, 620nm;

[0087] Different components in human blood have different absorption levels, for example: 725nm, 950nm;

[0088] Absorption points of oxygenated hemoglobin and reduced hemoglobin in human blood, for example: 805nm;

[0089] Absorption points of oxygenated hemoglobin and carboxyhemoglobin in human blood, for example: 640nm;

[0090] The wavelength used to distinguish the concentration of water and hemoglobin in human blood, for example: 970nm, wavelength greater than 1100nm.

[0091] All of the aforementioned wavelengths may be selected. Where appropriate, to reduce computational complexity, only a portion of the aforementioned wavelengths (e.g., multiple wavelengths with similar spacing) may be selected. Furthermore, other wavelengths may be added to all or some of the aforementioned wavelengths, or other suitable wavelengths may be used in addition to the aforementioned wavelengths.

[0092] 2) Normalize the absorbance fluctuation amplitude based on the absorbance at the reference wavelength.

[0093] For example, the wavelength of the isosbestic point of oxygenated and reduced hemoglobin concentrations can be selected as the reference wavelength , based on the reference wavelength Absorbance fluctuation amplitude under , the absorbance fluctuation amplitude calculated at each wavelength Normalize and get the wavelength Normalized absorbance fluctuation amplitude under :

[0094] .

[0095] The reference wavelength can be flexibly determined based on actual conditions. For example, a wavelength at which water absorption is sensitive can be selected as the reference wavelength, or a wavelength at which the absorption coefficient of oxyhemoglobin is the largest can be selected as the reference wavelength.

[0096] 3) Fitting to obtain the normalized absorbance fluctuation amplitude curve.

[0097] The normalized absorbance fluctuation amplitude curve (normalized absorbance fluctuation amplitude curve) can be obtained by fitting using a traditional interpolation signal processing method (interpolation method) or other appropriate methods;

[0098] 4) Extract (calculate) absorbance-related characteristic parameters from the normalized absorbance fluctuation amplitude curve

[0099] Taking the transmission type as an example, different hemoglobin concentrations have different absorption degrees for the light source of the same wavelength. Therefore, there are differences in the absorbance fluctuation amplitude curves corresponding to different wavelengths. The characteristic parameters of the normalized absorbance fluctuation amplitude curve are extracted to achieve the measurement of different hemoglobin concentrations:

[0100] ①The area below the absorbance fluctuation curve within a certain wavelength range (wavelength window) (the area below the line within the window):

[0101] The area below the line can usually be calculated by integration. Considering the convenience of operation, especially when using interpolation to form discrete data, when sampling the absorbance fluctuation amplitude at a fixed wavelength interval or the sampling points are roughly evenly distributed, the fluctuation amplitude within the wavelength window can be calculated by integration. The discrete data summation method replaces the integral, that is, the area under the absorbance fluctuation amplitude curve of the j-th fixed wavelength window is equivalently calculated according to the following formula :

[0102] , or it can be expressed as: .

[0103] For example: the area corresponding to the normalized absorbance fluctuation amplitude curve within the wavelength window [600nm, 650nm], where n is the starting wavelength of the fixed wavelength window j (600nm) and N is the wavelength window length (Window Size, 50nm).

[0104] Since the integration of continuous functions and the summation of discrete data are equivalent in the parameter calculation or calculation results involved in the present invention, the above summation formula can also be understood as (equivalently replaced by) the corresponding offline area integration formula.

[0105] ②Other absorbance-related characteristic parameters:

[0106] Extract (calculate) characteristic parameters that can reflect the change in the area under the line within the window, including:

[0107] characteristic parameters (neighbor difference): ;

[0108] characteristic parameters (Relative difference): ;

[0109] characteristic parameters (Interproximal ratio): ;

[0110] characteristic parameters (Comparison):

[0111] ;

[0112] characteristic parameters (Progressive aspect ratio):

[0113] .

[0114] The wavelength window K for calculating the relative difference and the wavelength windows K1, K2, ..., KM for calculating the relative contrast can be selected according to the actual situation, where , is the total number of wavelength windows selected for relative contrast calculation. Typical or representative wavelength windows within the wavelength window can be selected as the wavelength windows used for relative contrast calculation, for example, a wavelength window in the middle, a wavelength window at one end, or other wavelength windows with typical significance based on empirical or statistical results. A portion (one or more) of the wavelength window can be selected as the wavelength windows used for relative contrast calculation, for example, three wavelength windows at both ends and in the middle, another number of equally spaced wavelength windows, or one or more wavelength windows with typical significance based on empirical or statistical results.

[0115] Other absorbance-related characteristic parameters can also be set according to actual conditions.

[0116] Based on the above absorbance related characteristic parameters, the function Indicates the concentration of any one hemoglobin or total hemoglobin.

[0117] The above characteristic parameters and related calculations (data processing) can also be derived using reflectance detection data. By calculating a normalized absorbance fluctuation curve as it varies with wavelength, the above characteristic parameters can be obtained. The wavelength / wavelength range used for detection can be selected based on actual use, for example, selecting several characteristic wavelengths within the range [540nm, 810nm].

[0118] The above characteristic parameters and corresponding calculation (data processing) methods can also be used to obtain the above characteristic parameters by combining test data and calculating the normalized absorbance fluctuation amplitude curve that varies with wavelength. The wavelength range used for detection can be selected according to actual use, for example, selecting several characteristic wavelengths within the range of [540nm, 1400nm].

[0119] 2. Extract pulse wave related characteristic parameters based on detection data

[0120] When other blood components (such as plasma volume, plasma proteins, and platelets) are normal, an increase in hemoglobin concentration typically indicates an increase in the red blood cell count, leading to an increase in the hematocrit (HCT), which in turn increases blood viscosity. Blood viscosity affects hemodynamics and vascular elasticity. Since the pulse wave is the result of the combined effects of cardiac contraction and vascular elasticity, increased viscosity increases blood flow resistance, thereby altering the pulse wave morphology. Therefore, pulse wave morphology can be used as a basis for calculating blood hemoglobin concentration.

[0121] The pulse wave obtained based on the detection data is subjected to pulse rate and amplitude normalization operations to form a normalized pulse wave with normalized frequency and amplitude. Heart rate normalization can be performed by normalizing the collected (measured) pulse wave pulse rate (heart rate) to a standard frequency (standard heart rate), for example, 75 bpm, to reduce the impact of different heart rates among subjects on model construction and blood pressure measurement results. Amplitude normalization can be performed by normalizing the collected (measured) pulse wave amplitude to a standard amplitude (fixed value) to obtain a normalization coefficient k. This normalization coefficient is then used to normalize the entire pulse wave (multiplying it by k) to achieve pulse wave amplitude normalization, thereby facilitating comparability of pulse wave morphology between different individuals / samples.

[0122] The pulse wave used to extract pulse wave-related characteristic parameters may be a normalized pulse wave at a set wavelength (which may be set according to existing technology), or an accumulated average of normalized pulse waves at multiple wavelengths.

[0123] Based on the normalized pulse wave, the following characteristic parameters are extracted (obtained by calculation) as pulse wave related characteristic parameters:

[0124] 1) Time domain characteristic parameters

[0125] Considering that changes in blood viscosity can lead to changes in the shape of the ascending branch (the ascending branch of the pulse wave) and the descending branch (the descending branch of the pulse wave), for example, increased viscosity leads to increased peripheral resistance, resulting in changes in the maximum slope and rise time (10%-90% peak time) of the ascending branch. Increased viscosity leads to increased diastolic blood flow resistance and limited vascular elastic recoil, affecting the attenuation of the descending branch and possibly making the dicrotic wave less obvious or disappearing, the following time domain characteristic parameters are extracted:

[0126] characteristic parameters (maximum slope of ascending branch): , For pulse wave;

[0127] characteristic parameters (Rise time): The time it takes for the pulse wave value to rise from 10% of the peak value (10% of the peak value) to 90% of the peak value (90% of the peak value);

[0128] characteristic parameters (Fall time): The time it takes for the pulse wave value to drop from the peak value to 50% of the peak value (50% of the peak value);

[0129] characteristic parameters (Dicrotic wave slope): The slope of the line connecting the peak value and the trough value of the dicrotic wave;

[0130] characteristic parameters (Dicrotic Amplitude Ratio): ,in is the main wave amplitude (pulse wave amplitude), It is the amplitude of the first dicrotic wave after the main wave;

[0131] characteristic parameters (Ratio of ascending branch time to descending branch time): ,in is the rise time (the time it takes for the rising branch to go from the trough to the peak), is the descending branch time (the time taken by the descending branch to go from the peak to the trough);

[0132] 2) Frequency domain characteristic parameters

[0133] Since changes in blood viscosity will affect the power spectrum density of the pulse wave frequency domain, the normalized power spectrum density curve (distribution function) can be obtained based on the normalized pulse wave. , based on the normalized power spectral density curve, the following frequency domain feature parameters are extracted:

[0134] characteristic parameters (Low-high frequency energy ratio, the ratio of low-frequency component energy to high-frequency component energy): For example, the low-frequency component (frequency range) can be calculated as 0Hz≤f≤2Hz, and the high-frequency component (frequency range) can be calculated as 5Hz≤f≤10Hz. In this case, , ;

[0135] characteristic parameters (Harmonic Component Ratio): The ratio of the third harmonic (amplitude) to the fundamental harmonic (amplitude), which can reflect the blunting of the waveform.

[0136] Other pulse wave related characteristic parameters may also be set according to actual conditions.

[0137] Therefore, we can use the function form Indicates the concentration of any one hemoglobin or total hemoglobin.

[0138] 3. Establishment of hemoglobin concentration calculation model

[0139] Since the pulse wave does not change significantly in a short period of time under normal circumstances, continuous photoplethysmography detection can be performed within a fixed time window to obtain multiple complete measured pulse waves within the window. The frequency and amplitude of these measured pulse waves are normalized to form a normalized measured pulse wave. The normalized measured pulse waves within the time window are accumulated and averaged to obtain a single normalized pulse wave, which is used as the normalized pulse wave for this detection (the detection result of the normalized pulse wave) for subsequent calculations, including constructing an absorbance fluctuation amplitude curve (function) / normalized absorbance fluctuation amplitude curve, and using the characteristic parameters obtained by the calculation as characteristic parameters for this detection and included in a sample data.

[0140] The cumulative averaging method can be to accumulate and superimpose all complete pulse waves within the corresponding time window (normalized with the main wave peak point as the central axis) and divide it by the number of accumulated pulse waves.

[0141] In the data processing of continuous detection, a fixed time length (for example, 15s) can be set as the time window, and the time window can be slid with a set sliding step (for example, 1.2s). Each time window is regarded as a detection, and the characteristic parameters and other detection results of the detection are obtained by calculation.

[0142] When collecting sample data for constructing a hemoglobin concentration calculation model, subjects providing the sample data should be selected according to relevant standards. When performing photoplethysmography (PPE) testing, hemoglobin concentration measurements at the same time should be performed using a standard or sufficiently accurate PPE method / instrument. Characteristic parameters for model construction should be selected, and the relevant characteristic parameters for each measurement should be calculated using the PPE data. These parameters should be used as the model input data (or independent variable). The hemoglobin concentration results obtained at the same time using a standard or sufficiently accurate PPE method / instrument should be used as the model input data (or dependent variable). A corresponding set of model input data (at the same time / same time window) and model input should be used as sample data. A hemoglobin concentration calculation model should be constructed using a number of sample data that conform to the specifications or meet the requirements.

[0143] There are two methods for calculating hemoglobin concentration: one-step method and two-step method.

[0144] 1. Two-step method

[0145] 1) Based on the correlation between the absorbance-related characteristic parameters and the pulse wave-related characteristic parameters and the hemoglobin concentration, a hemoglobin calculation model based on the absorbance-related characteristic parameters and a hemoglobin calculation model based on the pulse wave-related characteristic parameters are constructed respectively.

[0146] The hemoglobin calculation model based on absorbance-related characteristic parameters can be expressed as:

[0147]

[0148] The input data of the model in the sample data used to build the model are absorbance-related characteristic parameters, and the input data of the model are hemoglobin concentration (hemoglobin concentration obtained by standard or sufficiently high-precision hemoglobin concentration detection method / instrument detection). represents the hemoglobin concentration of the model (the hemoglobin concentration predicted based on the absorbance-related characteristic parameters).

[0149] The hemoglobin calculation model based on pulse wave related characteristic parameters can be expressed as:

[0150]

[0151] The input data of the model in the sample data used to build the model are the pulse wave related characteristic parameters, and the input data of the model is the hemoglobin concentration (the hemoglobin concentration is obtained by using a standard or sufficiently high-precision hemoglobin concentration detection method / instrument). represents the hemoglobin concentration of the model (the hemoglobin concentration predicted based on the pulse wave-related characteristic parameters).

[0152] The above two calculation models can be established using their respective corresponding sample data by adopting artificial neural network, nonlinear regression, partial least squares regression, multi-layer model architecture, signal analysis or statistical methods.

[0153] 2) Calculation results based on the above two models and , construct a hemoglobin concentration calculation model based on absorbance-related characteristic parameters and pulse wave-related characteristic parameters:

[0154]

[0155] That is,

[0156]

[0157] in, is the hemoglobin concentration, and The coefficients and constants of the two types of characteristic parameter regression models (corresponding hemoglobin concentration calculation results) are shown respectively.

[0158] The model input data in the sample data used for model construction is and , the model input data is the hemoglobin concentration (the hemoglobin concentration is obtained by using a standard or sufficiently accurate hemoglobin concentration detection method / instrument), where represents the hemoglobin concentration of the model (the hemoglobin concentration predicted based on the absorbance-related features and pulse wave-related feature parameters).

[0159] The optimal regression coefficient can be found by the least squares method or other appropriate methods , thereby establishing a hemoglobin calculation model and realizing non-invasive hemoglobin measurement.

[0160] 2. One-step method

[0161] Based on the correlation between the absorbance-related characteristic parameters and the pulse wave-related characteristic parameters and the hemoglobin concentration, a hemoglobin calculation model based on the absorbance-related characteristic parameters and the pulse wave-related characteristic parameters is constructed.

[0162] The hemoglobin calculation model based on absorbance-related characteristic parameters can be expressed as:

[0163]

[0164] The input data of the model in the sample data used to build the model are absorbance-related characteristic parameters and pulse wave-related characteristic parameters. The input data of the model is hemoglobin concentration (hemoglobin concentration obtained by standard or sufficiently high-precision hemoglobin concentration detection method / instrument). Indicates the hemoglobin concentration.

[0165] The computational model can be established using artificial neural networks, nonlinear regression, partial least squares regression, multi-layer model architecture, signal analysis or statistical methods.

[0166] The construction and updating of relevant calculation models can be implemented using a corresponding computer platform. The terminal (detection device) used for detection can send the detection data to the platform, and the platform will perform relevant operations. The detection device obtains the calculation results of the hemoglobin concentration obtained by the platform's calculation; the calculation model can also be stored locally in the detection device, and the detection device will perform the hemoglobin concentration calculation to generate the calculation results of the hemoglobin concentration.

[0167] Figure 6The present invention illustrates a device for measuring blood levels (performing photoplethysmography) on the finger. This continuous, non-invasive hemoglobin measurement device is worn on the fingertip to perform measurements. An external host (including a microprocessor, memory, and interface circuitry / data acquisition circuitry) 1 performs control, data storage, signal processing, and display functions. The fingertip photoelectric probe (photoelectric PPG probe) collects multi-wavelength photoelectric pulse waves and can be a transmissive, reflective, or combined probe. An optical cable 3 transmits the optical signal obtained by the probe to the interface circuitry (data acquisition circuitry) of the main body.

[0168] Figure 7 The present invention illustrates a wrist-based measurement device. This continuous, noninvasive hemoglobin measurement device is worn on the wrist to perform measurements. An external host 1 (including a microprocessor, memory, and interface / data acquisition circuitry) performs control, data storage, signal processing, and display functions. A reflective probe 5, an integrated multi-wavelength light source and photoelectric sensor, is embedded in a wristband 4. When worn on the wrist, the wristband collects multi-wavelength photoelectric pulse waves to measure parameters such as hemoglobin.

[0169] Figure 8 The present invention shows an optical circuit structure of a detection device that can be used to implement the present invention. Appropriate existing technologies can be used to perform detection with the support of software for implementing the detection method of the present invention.

[0170] Unless otherwise specified or when one preferred or optional technical means is a further limitation of another technical means, the preferred and optional technical means disclosed in the present invention can be arbitrarily combined to form several different specific implementation methods.

Claims

1. A continuous non-invasive physiological parameter detection method, characterized in that Based on the photoplethysmography detection data at multiple wavelengths, the absorbance-related characteristic parameters and the pulse wave-related characteristic parameters are extracted. The hemoglobin concentration calculation model based on the absorbance-related characteristic parameters and the pulse wave-related characteristic parameters is used to obtain the hemoglobin concentration detection results.

2. The continuous non-invasive physiological parameter detection method according to claim 1, characterized in that Based on the photoplethysmography data, the absorbance fluctuation amplitude at each wavelength is calculated and fitted into an absorbance fluctuation amplitude curve based on the absorbance fluctuation amplitude at each wavelength. Several wavelength windows of set widths are set, and based on the area under the absorbance fluctuation amplitude curve within the wavelength window, some or all of the following characteristic parameters are extracted as absorbance-related characteristic parameters used in the hemoglobin concentration calculation model: Neighborhood differences: ; Relative difference: ; Interproximal ratio: ; Comparison: ; Progressive aspect ratio: , in, is the interproximal difference, is the relative difference, is the interproximal ratio, For relative comparison, is the progressive aspect ratio, is the area under the absorbance fluctuation curve in the jth wavelength window, is the area under the absorbance fluctuation curve in the j-1th wavelength window, is the area under the absorbance fluctuation curve within the wavelength window selected for calculating the relative difference. The selected The area under the absorbance fluctuation curve within a wavelength window or the sum of the areas under the absorbance fluctuation curve, , It is the sum of the areas under the absorbance fluctuation curves in all wavelength windows before the jth wavelength window.

3. The continuous non-invasive physiological parameter detection method according to claim 2, characterized in that The absorbance fluctuation amplitude curve is normalized before extracting the absorbance-related features, and the absorbance-related characteristic parameters are extracted based on the normalized absorbance fluctuation amplitude curve.

4. The continuous non-invasive physiological parameter detection method according to claim 3, characterized in that The normalization method of the absorbance fluctuation curve is: set the reference wavelength , the following formula is used to normalize the absorbance fluctuation amplitude or absorbance fluctuation amplitude curve at each wavelength: ， in is the wavelength The normalized absorbance fluctuation amplitude under is the wavelength The absorbance fluctuation amplitude under Reference wavelength The absorbance fluctuation amplitude under .

5. The continuous non-invasive physiological parameter detection method according to claim 1, characterized in that Photoplethysmography data at multiple wavelengths is performed using some or all of the following wavelengths: 560nm, 570nm, 660nm, 920nm, 600nm, 620nm, 725nm, 950nm, 805nm, 940nm, 970nm, and greater than 1100nm.

6. The continuous non-invasive physiological parameter detection method according to claim 1, characterized in that A pulse wave is obtained based on photoplethysmography detection data, and amplitude normalization and heart rate normalization are performed on the pulse wave to obtain a normalized pulse wave. Based on the normalized pulse wave, some or all of the following characteristic parameters are extracted as pulse wave related characteristic parameters used in a hemoglobin concentration calculation model: maximum slope of the ascending branch, rise time, fall time, dicrotic wave slope, dicrotic amplitude ratio, ascending and descending branch time ratio, low and high frequency energy ratio, and harmonic component ratio.

7. The continuous non-invasive physiological parameter detection method according to claim 6, characterized in that In the case of normalized pulse waves at several wavelengths, the normalized pulse wave at a set wavelength is used as the normalized pulse wave for extracting pulse wave-related characteristic parameters, or the average value of the normalized pulse waves at each relevant wavelength is used as the normalized pulse wave for extracting pulse wave-related characteristic parameters.

8. The continuous non-invasive physiological parameter detection method according to any one of claims 1 to 7, characterized in that A hemoglobin concentration calculation model based on absorbance-related characteristic parameters and pulse wave-related characteristic parameters is constructed by the following method: the absorbance-related characteristic parameters and pulse wave-related characteristic parameters are extracted based on photoplethysmography detection data at multiple wavelengths; and based on the extracted absorbance-related characteristic parameters and pulse wave-related characteristic parameters and the corresponding hemoglobin concentration detection data, a hemoglobin concentration calculation model based on the absorbance-related characteristic parameters and pulse wave-related characteristic parameters is constructed using a one-step method or a two-step method: 1) One-step method: using absorbance-related characteristic parameters and pulse wave-related characteristic parameters as model input data and corresponding hemoglobin concentration detection data as model output data, constructing one-step method sample data, and using the one-step method sample data to construct a hemoglobin concentration calculation model based on the absorbance-related characteristic parameters and pulse wave-related characteristic parameters; 2) Two-step method: Step 1: Using absorbance-related characteristic parameters as model input data and corresponding hemoglobin concentration detection data as model output data, constructing sample data of a first calculation model for hemoglobin concentration, and using the first calculation model sample data for hemoglobin concentration to construct a first calculation model for hemoglobin concentration; using pulse wave-related characteristic parameters as model input data and corresponding hemoglobin concentration detection data as model output data, constructing sample data of a second calculation model for hemoglobin concentration, and using the second calculation model sample data for hemoglobin concentration to construct a second calculation model for hemoglobin concentration; Step 2: Based on the first calculation model for hemoglobin concentration, absorbance-related characteristic parameters are used to calculate the hemoglobin concentration calculated value of the first calculation model; based on the second calculation model for hemoglobin concentration, pulse wave-related characteristic parameters are used to calculate the hemoglobin concentration calculated value of the second calculation model; the hemoglobin concentration calculated value of the first calculation model and the hemoglobin concentration calculated value of the second calculation model are used as model input data; corresponding hemoglobin concentration detection data are used as model output data to construct sample data of the hemoglobin concentration calculation model based on the first calculation model for hemoglobin concentration and the second calculation model for hemoglobin concentration; the sample data of the hemoglobin concentration calculation model based on the first calculation model for hemoglobin concentration and the second calculation model for hemoglobin concentration are used to construct the hemoglobin concentration calculation model based on the first calculation model for hemoglobin concentration and the second calculation model for hemoglobin concentration, and use this as the hemoglobin concentration calculation model based on absorbance-related characteristic parameters and pulse wave-related characteristic parameters.

9. Continuous non-invasive physiological parameter detection device, characterized in that include: The probe adopts a multi-wavelength photoplethysmography probe for performing multi-wavelength photoplethysmography detection and forming an optical signal for multi-wavelength photoplethysmography detection; A host computer is provided with a signal sampling circuit, a data processing circuit, a human-computer interaction device and a storage device, wherein the signal sampling circuit is used for collecting data of optical signals of multi-wavelength photoplethysmography detection to form multi-wavelength photoplethysmography detection data; the data processing circuit uses any one of the continuous non-invasive physiological parameter detection methods according to claims 1-8 to perform data processing to obtain hemoglobin concentration detection results; the human-computer interaction device is used for human-computer interaction and is provided with a display screen for displaying hemoglobin detection results; and the storage device is used for data storage and model storage.

10. The continuous non-invasive physiological parameter detection device according to claim 9, characterized in that The probe is a transmission probe, a reflection probe, or a transmission-reflection combined probe; the probe is a probe for detecting the finger part, or a probe for detecting the arm part.

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