CN113729698A - Noninvasive blood glucose detection method and system - Google Patents
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- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
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Abstract
The invention relates to a noninvasive blood glucose detection method, which specifically comprises the following steps: a synchronous monitoring step: acquiring a PPG signal and an ECG signal which are monitored synchronously; obtaining pulse wave transmission time: obtaining pulse wave transmission time according to a rising point of the PPG signal and an ECG signal peak value; a signal processing step: processing the PPG signal and the ECG signal, and removing noise to obtain a processed PPG signal and an processed ECG signal; a characteristic extraction step: the invention also relates to a non-invasive blood sugar detection system, which realizes accurate measurement of blood sugar by taking the pulse wave transmission time, the PPG signal characteristics and the ECG signal characteristics as core parameters and can eliminate the influence of external factors such as skin color, cuticle, vessel wall thickness and the like.
Description
Technical Field
The invention relates to a non-invasive blood sugar detection method and a system.
Background
Diabetes is a chronic disease that is metabolized for life and is not cured, but diabetes can be managed appropriately. About 1.7% of the world's population suffers from diabetes, and this proportion may increase in the near future. There is currently no effective treatment for diabetes, and only by monitoring blood glucose levels periodically to reduce or delay the onset of complications, self-monitoring is considered one of the most direct and feasible options for controlling diabetes.
PPG is a technique for measuring changes in blood volume at a site in the body that is simple and low cost, and is generally used non-invasively for measurements at the skin surface. PPG devices consist of a light source and a detector for emitting light that illuminates tissue and a reflection of the received light. The amount of light absorbed varies periodically according to fluctuations in blood volume in the circulatory system, resulting in a PPG signal containing information related to respiration, circulatory system, blood flow and heartbeat.
Ecg (electrocardiograph) is used to record the time node and intensity of the electrical signal sequence that triggers the heartbeat. By analyzing the ECG images, the physician can better diagnose if our heart rate is normal and if there is a problem with the heart function. The ECG records a sequence of electrical pulses that trigger the beating of the heart.
The strength and waveform structure of the PPG signal and the ECG signal are also related to the skin color, the stratum corneum and the thickness of the blood vessel wall of the subject at that time, so even if the blood glucose concentration of different people is the same, different PPG signals and ECG signals are measured, and therefore, the factors influence the determination of the blood glucose concentration.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a noninvasive blood glucose detection method, which specifically comprises the following steps:
a synchronous monitoring step: acquiring a PPG signal and an ECG signal which are monitored synchronously;
obtaining pulse wave transmission time: obtaining pulse wave transmission time according to a rising point of the PPG signal and an ECG signal peak value;
a signal processing step: processing the PPG signal and the ECG signal, and removing noise to obtain a processed PPG signal and an processed ECG signal;
a characteristic extraction step: performing segmentation processing according to the processed PPG signal and ECG signal and extracting signal characteristics to obtain PPG signal characteristics and ECG signal characteristics;
acquiring blood sugar data: taking the pulse wave transmission time, the PPG signal characteristic and the ECG signal characteristic as blood sugar data to be detected;
and (3) comparison and identification: comparing the blood glucose data to be detected with the blood glucose data in the database, and identifying the corresponding blood glucose concentration;
the database includes a plurality of sets of blood glucose data having a natural time sequence, including pulse wave transit time, PPG signal characteristics, ECG signal characteristics, and corresponding blood glucose concentrations.
A non-invasive blood glucose detection system specifically comprises the following units:
a synchronous monitoring unit: acquiring a PPG signal and an ECG signal which are monitored synchronously;
obtaining pulse wave transmission time unit: obtaining pulse wave transmission time according to a rising point of the PPG signal and an ECG signal peak value;
a signal processing unit: processing the PPG signal and the ECG signal, and removing noise to obtain a processed PPG signal and an processed ECG signal;
a feature extraction unit: performing segmentation processing according to the processed PPG signal and ECG signal and extracting signal characteristics to obtain PPG signal characteristics and ECG signal characteristics;
acquiring a blood glucose data unit: taking the pulse wave transmission time, the PPG signal characteristic and the ECG signal characteristic as blood sugar data to be detected;
a comparison identification unit: comparing the blood glucose data to be detected with the blood glucose data in the database, and identifying the corresponding blood glucose concentration;
the database includes a plurality of sets of blood glucose data having a natural time sequence, including pulse wave transit time, PPG signal characteristics, ECG signal characteristics, and corresponding blood glucose concentrations.
The invention realizes accurate measurement of blood sugar by taking the pulse wave transmission time, the PPG signal characteristics and the ECG signal characteristics as core parameters, and can completely eliminate the influence of external factors such as skin color, cuticle, vessel wall thickness and the like if the database is accurate enough. Meanwhile, the invention also realizes the updating of the database by calculating the first-order or multi-order derivative of the differential quantity, so that the database is more accurate.
The above-described and other features, aspects, and advantages of the present application will become more apparent with reference to the following detailed description.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The use of "first," "second," and similar terms in the description and claims of the present application do not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. Also, the use of the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one.
A non-invasive blood glucose detection method comprises the following steps:
a synchronous monitoring step: acquiring a PPG signal and an ECG signal which are monitored synchronously;
obtaining pulse wave transmission time: obtaining pulse wave transmission time according to a rising point of the PPG signal and an ECG signal peak value;
a signal processing step: processing the PPG signal and the ECG signal, and removing noise to obtain a processed PPG signal and an processed ECG signal;
the means for removing noise may be those commonly used in the art.
A characteristic extraction step: performing segmentation processing according to the processed PPG signal and ECG signal and extracting signal characteristics to obtain PPG signal characteristics and ECG signal characteristics;
the characteristic extraction step comprises:
extracting the features of the PPG signal and the ECG signal of each single period from the time domain and the frequency domain by adopting a Gaussian fitting algorithm to obtain signal features,
the signal characteristics include: signal maximum amplitude, rate of rise of a single period from start to peak, rate of fall of a single period from peak to end, average amplitude value of a single period, standard deviation of amplitude, average slope from start to peak, average slope from peak to strait, average slope from strait to diastolic peak, or average slope from peak to diastolic peak.
Gaussian Fitting (Gaussian Fitting) uses a shape as follows:
Gi(x)＝Ai*exp((x-Bi)^2/Ci^2)
the fitting method of the Gaussian function to the data point set is used. The method can be analogized with polynomial fitting, and has the difference that the polynomial fitting is performed by using a power function system and a Gaussian function, and the method has the advantage that the integral is calculated simply and quickly.
The implementation scheme of the mathnet wake matrix operation in the c # is as follows:
double[,]a＝new double[fitDatas.Count,3]；
double[]b＝new double[fitDatas.Count]；
double[]X＝new double[3]{0,0,0}；
for(int i＝0；i<fitDatas.Count；i++)
{
b[i]＝Math.Log(fitDatas[i].Intensity)；
a[i,0]＝1；
a[i,1]＝fitDatas[i].WaveLength；
a[i,2]＝a[i,1]*a[i,1]；
}
//Matrix.Equation(datas.Count,3,a,b,X)；
MathNet.Numerics.LinearAlgebra.Matrix matrixA＝new MathNet.Numerics.LinearAlgebra.Matrix(a)；
MathNet.Numerics.LinearAlgebra.Matrix matrixB＝new MathNet.Numerics.LinearAlgebra.Matrix(b,b.Length)；
MathNet.Numerics.LinearAlgebra.Matrix matrixC＝matrixA.Solve(matrixB)；
X＝matrixC.GetColumnVector(0)；
double S＝-1/X[2]；
double xMax＝X[1]*S/2.0；
double yMax＝Math.Exp(X[0]+xMax*xMax/S)；
acquiring blood sugar data: taking the pulse wave transmission time, the PPG signal characteristic and the ECG signal characteristic as blood sugar data to be detected;
and (3) comparison and identification: comparing the blood glucose data to be detected with the blood glucose data in the database, and identifying the corresponding blood glucose concentration;
the comparison step specifically comprises:
if the blood sugar data to be detected is the first group of blood sugar data in natural time, comparing the blood sugar data to be detected with the blood sugar data in the database one by one in sequence, and calculating the difference between the blood sugar data to be detected and the blood sugar data in the database, wherein the blood sugar data with the minimum difference in the database is used as matching data;
if the blood sugar data to be detected is not the first group of blood sugar data in natural time, calculating the difference between the blood sugar data to be detected and the blood sugar data in the database; finding multiple groups of blood glucose data in a database according to a preset difference threshold, and selecting the blood glucose data which is closer to the natural time of the last matched data as the matched data of the blood glucose data to be detected according to the natural time of the multiple groups of blood glucose data;
the blood glucose concentration in the data is matched as the detected blood glucose concentration.
The database comprises a plurality of groups of blood glucose data with natural time sequence, wherein one group of blood glucose data comprises pulse wave transmission time, PPG signal characteristics, ECG signal characteristics and corresponding blood glucose concentration.
The difference is calculated by the following formula:
T＝ΔA×α1+ΔB×α2+ΔC×α3
wherein T is the difference;
Δ a is the pulse wave transit time difference;
Δ B is the sum of the absolute differences between the PPG signal features;
Δ C is the sum of the absolute differences between the ECG signal features;
α 1, α 2, α 3 are weight coefficients. The weighting factor is generally given a greater weight to the pulse wave propagation time difference, for example, a factor of 0.8.
The noninvasive blood glucose detection method also comprises the following database updating step:
and calculating multiple groups of differential quantities arranged according to natural time according to the multiple groups of the blood sugar data to be detected and the matched blood sugar data obtained in the comparison step, calculating a first-order or multi-order derivative of the differential quantities, and if the first-order or multi-order derivative is higher than a certain threshold value, putting the multiple groups of the blood sugar data to be detected into a database and giving the natural time according to the sequence.
The invention also relates to a non-invasive blood sugar detection system, which specifically comprises the following units:
a synchronous monitoring unit: acquiring a PPG signal and an ECG signal which are monitored synchronously;
obtaining pulse wave transmission time unit: obtaining pulse wave transmission time according to a rising point of the PPG signal and an ECG signal peak value;
a signal processing unit: processing the PPG signal and the ECG signal, and removing noise to obtain a processed PPG signal and an processed ECG signal;
a feature extraction unit: performing segmentation processing according to the processed PPG signal and ECG signal and extracting signal characteristics to obtain PPG signal characteristics and ECG signal characteristics;
acquiring a blood glucose data unit: taking the pulse wave transmission time, the PPG signal characteristic and the ECG signal characteristic as blood sugar data to be detected;
a comparison identification unit: comparing the blood glucose data to be detected with the blood glucose data in the database, and identifying the corresponding blood glucose concentration;
the database comprises a plurality of groups of blood glucose data with natural time sequence, wherein one group of blood glucose data comprises pulse wave transmission time, PPG signal characteristics, ECG signal characteristics and corresponding blood glucose concentration.
The comparison unit specifically comprises:
if the blood sugar data to be detected is the first group of blood sugar data in natural time, comparing the blood sugar data to be detected with the blood sugar data in the database one by one in sequence, and calculating the difference between the blood sugar data to be detected and the blood sugar data in the database, wherein the blood sugar data with the minimum difference in the database is used as matching data;
if the blood sugar data to be detected is not the first group of blood sugar data in natural time, calculating the difference between the blood sugar data to be detected and the blood sugar data in the database; finding multiple groups of blood glucose data in a database according to a preset difference threshold, and selecting the blood glucose data which is closer to the natural time of the last matched data as the matched data of the blood glucose data to be detected according to the natural time of the multiple groups of blood glucose data;
the blood glucose concentration in the data is matched as the detected blood glucose concentration.
The feature extraction unit includes:
performing feature extraction on the PPG signal and the ECG signal of each single period from two aspects of time domain and frequency domain by adopting a Gaussian fitting algorithm to obtain signal features, wherein the signal features comprise: signal maximum amplitude, rate of rise of a single period from start to peak, rate of fall of a single period from peak to end, average amplitude value of a single period, standard deviation of amplitude, average slope from start to peak, average slope from peak to strait, average slope from strait to diastolic peak, or average slope from peak to diastolic peak.
The non-invasive blood glucose detecting system further comprises a database updating unit:
and calculating multiple groups of differential quantities arranged according to natural time according to multiple groups of the blood sugar data to be detected and the matched blood sugar data obtained in the comparison unit, calculating a first-order or multi-order derivative of the differential quantities, and if the first-order or multi-order derivative is higher than a certain threshold value, putting the multiple groups of the blood sugar data to be detected into a database and giving the natural time according to the sequence.
The invention realizes accurate measurement of blood sugar by taking the pulse wave transmission time, the PPG signal characteristics and the ECG signal characteristics as core parameters, and can completely eliminate the influence of external factors such as skin color, cuticle, vessel wall thickness and the like if the database is accurate enough. Meanwhile, the invention also realizes the updating of the database by calculating the first-order or multi-order derivative of the differential quantity, so that the database is more accurate.
While there have been shown and described what are at present considered the fundamental principles and essential features of the invention and its advantages, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and the description is given here only for clarity, and those skilled in the art should integrate the description, and the embodiments may be combined appropriately to form other embodiments understood by those skilled in the art.
Claims (10)
1. A non-invasive blood glucose detection method is characterized by comprising the following steps:
a synchronous monitoring step: acquiring a PPG signal and an ECG signal which are monitored synchronously;
obtaining pulse wave transmission time: obtaining pulse wave transmission time according to a rising point of the PPG signal and an ECG signal peak value;
a signal processing step: processing the PPG signal and the ECG signal, and removing noise to obtain a processed PPG signal and an processed ECG signal;
a characteristic extraction step: performing segmentation processing according to the processed PPG signal and ECG signal and extracting signal characteristics to obtain PPG signal characteristics and ECG signal characteristics;
acquiring blood sugar data: taking the pulse wave transmission time, the PPG signal characteristic and the ECG signal characteristic as blood sugar data to be detected;
and (3) comparison and identification: comparing the blood glucose data to be detected with the blood glucose data in the database, and identifying the corresponding blood glucose concentration;
the database includes a plurality of sets of blood glucose data having a natural time sequence, including pulse wave transit time, PPG signal characteristics, ECG signal characteristics, and corresponding blood glucose concentrations.
2. The method of claim 1, wherein the step of comparing comprises:
if the blood sugar data to be detected is the first group of blood sugar data in natural time, comparing the blood sugar data to be detected with the blood sugar data in the database one by one in sequence, and calculating the difference between the blood sugar data to be detected and the blood sugar data in the database, wherein the blood sugar data with the minimum difference in the database is used as matching data;
if the blood sugar data to be detected is not the first group of blood sugar data in natural time, calculating the difference between the blood sugar data to be detected and the blood sugar data in the database; finding multiple groups of blood glucose data in a database according to a preset difference threshold, and selecting the blood glucose data which is closer to the natural time of the last matched data as the matched data of the blood glucose data to be detected according to the natural time of the multiple groups of blood glucose data;
the blood glucose concentration in the matched data is taken as the detected blood glucose concentration.
3. The method of claim 2, wherein the step of extracting features comprises:
extracting the features of the PPG signal and the ECG signal of each single period from the time domain and the frequency domain by adopting a Gaussian fitting algorithm to obtain signal features,
the signal features include: signal maximum amplitude, rate of rise of a single period from start to peak, rate of fall of a single period from peak to end, average amplitude value of a single period, standard deviation of amplitude, average slope from start to peak, average slope from peak to strait, average slope from strait to diastolic peak, or average slope from peak to diastolic peak.
4. The method of claim 2The non-invasive blood glucose detecting method is characterized in that the difference is calculated by the following formula:
wherein T is the difference;
Δ a is the pulse wave transit time difference;
Δ B is the sum of the absolute differences between the PPG signal features;
Δ C is the sum of the absolute differences between the ECG signal features;
α 1, α 2, α 3 are weight coefficients.
5. The method of claim 1, further comprising the step of updating the database:
and calculating multiple groups of differential quantities arranged according to natural time according to the multiple groups of the blood sugar data to be detected and the matched blood sugar data obtained in the comparison step, calculating a first-order or multi-order derivative of the differential quantities, and if the first-order or multi-order derivative is higher than a certain threshold value, putting the multiple groups of the blood sugar data to be detected into a database and giving the natural time according to the sequence.
6. A non-invasive blood glucose detection system, comprising the following units:
a synchronous monitoring unit: acquiring a PPG signal and an ECG signal which are monitored synchronously;
obtaining pulse wave transmission time unit: obtaining pulse wave transmission time according to a rising point of the PPG signal and an ECG signal peak value;
a signal processing unit: processing the PPG signal and the ECG signal, and removing noise to obtain a processed PPG signal and an processed ECG signal;
a feature extraction unit: performing segmentation processing according to the processed PPG signal and ECG signal and extracting signal characteristics to obtain PPG signal characteristics and ECG signal characteristics;
acquiring a blood glucose data unit: taking the pulse wave transmission time, the PPG signal characteristic and the ECG signal characteristic as blood sugar data to be detected;
a comparison identification unit: comparing the blood glucose data to be detected with the blood glucose data in the database, and identifying the corresponding blood glucose concentration;
the database includes a plurality of sets of blood glucose data having a natural time sequence, including pulse wave transit time, PPG signal characteristics, ECG signal characteristics, and corresponding blood glucose concentrations.
7. The system of claim 6, wherein the comparing unit comprises:
if the blood sugar data to be detected is the first group of blood sugar data in natural time, comparing the blood sugar data to be detected with the blood sugar data in the database one by one in sequence, and calculating the difference between the blood sugar data to be detected and the blood sugar data in the database, wherein the blood sugar data with the minimum difference in the database is used as matching data;
if the blood sugar data to be detected is not the first group of blood sugar data in natural time, calculating the difference between the blood sugar data to be detected and the blood sugar data in the database; finding multiple groups of blood glucose data in a database according to a preset difference threshold, and selecting the blood glucose data which is closer to the natural time of the last matched data as the matched data of the blood glucose data to be detected according to the natural time of the multiple groups of blood glucose data;
the blood glucose concentration in the matched data is taken as the detected blood glucose concentration.
8. The system of claim 7, wherein the feature extraction unit comprises:
extracting the features of the PPG signal and the ECG signal of each single period from the time domain and the frequency domain by adopting a Gaussian fitting algorithm to obtain signal features,
the signal features include: signal maximum amplitude, rate of rise of a single period from start to peak, rate of fall of a single period from peak to end, average amplitude value of a single period, standard deviation of amplitude, average slope from start to peak, average slope from peak to strait, average slope from strait to diastolic peak, or average slope from peak to diastolic peak.
9. The system of claim 7, wherein the differential is calculated by the following formula:
wherein T is the difference;
Δ a is the pulse wave transit time difference;
Δ B is the sum of the absolute differences between the PPG signal features;
Δ C is the sum of the absolute differences between the ECG signal features;
α 1, α 2, α 3 are weight coefficients.
The non-invasive blood glucose detection method further comprises a database updating unit.
10. The system of claim 6, wherein a plurality of residual amounts arranged according to the natural time are calculated according to the plurality of sets of the blood glucose data to be tested and the matching blood glucose data obtained from the comparing unit, and a first or a second derivative of the residual amounts is calculated, and if the first or the second derivative is higher than a threshold, the plurality of sets of the blood glucose data to be tested are placed in the database and sequentially assigned with the natural time.
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