专利汇可以提供SYSTEM AND APPARATUS FOR NON-INVASIVE MEASUREMENT OF GLUCOSE LEVELS IN BLOOD专利检索,专利查询,专利分析的服务。并且A system for estimating the glucose levels in blood is developed in the present invention. Said system establishes a physiological model of the pulse wave and its energy, which are also correlated with the glucose metabolic function, for generating a fixed length vector containing the values of the previous model combined with other variables related to the user such as, for example, age, sex, height, weight, etc... This fixed length vector is used as an excitation of a function estimation system based on "random forests" for the calculation of the interest variable. The main advantage of this parameter estimation system lays in the fact that it does not apply any restriction a priori on the function to be estimated, and that it is robust in front of heterogeneous data, such as in the case of the present invention.,下面是SYSTEM AND APPARATUS FOR NON-INVASIVE MEASUREMENT OF GLUCOSE LEVELS IN BLOOD专利的具体信息内容。
The present invention develops a system for non-invasive measurement of the glucose levels in blood, independent from the current methods based on blood sample analysis (Oxidasa Glucose molecule reduction) or by means of an absorption spectrum analysis of glucose in blood. With this purpose, a new method is presented based on the function approximation by means of random forests implemented by means of a DSP or FPGA device, whose input is a pre-processed version of the plethysmographic pulse combined with other variables from the patient.
Diabetes Mellitus (DM) comprises a group of metabolic disorders, which share a hyperglycaemic phenotype, (increase of blood glucose levels in patients). Several types of DM exist, which are a result of a complex interaction between genetic factors, and environmental factors and lifestyle (sedentary, diet, etc...). Depending on the causes of DM, factors that contribute to hyperglycemia may include the reduction of insulin hormone secretion, insufficient use of glucose at the metabolic level, or an increased production of glucose by the body.
Disorders associated with DM seriously compromise the body. Also, such disorders are a major economic burden to the healthcare system. In developed countries, DM is the primary cause of kidney failure, non-traumatic amputation of the lower extremities, and blindness in adults. In fact, studies have shown that approximately 1.7% of the world's population suffers from DM, and that this percentage is likely to increase in the medium and long term, thus, DM remaining a major cause of morbidity and mortality.
The protocols published by the World Health Organization (WHO) define the following diagnose criteria of the DM:
Currently, measuring glucose levels involves taking a blood sample during the testing process. Various devices exist for determining glucose levels in diabetic patients, based on the reduction of the reagent glucose oxidase (GOX). Such devices use a small blood sample, obtained with a lancet, and deposited on a small test strip impregnated with GOX. Glucose in the blood reacts with GOX, and hydrogen peroxide (H2O2) is obtained as a result. The amount of hydrogen peroxide causes a change in the impedance of the strip, which correlates with the level of glucose in the blood.
Said systems are highly invasive, because they require patients with diabetes to puncture their fingers up to four times a day to obtain blood samples and monitor their glucose levels.
With the aim to eliminate the hurting related to the puncturing and to minimize the sources of infection, systems exist that utilize spectroscopic techniques to measure glucose levels means of spectroscopy (emission, transmission, and reflection methods). These systems are adversely affected by water in the body, a low glucose concentration, and optical effects produced by the skin, and are thus unreliable. In fact, nowadays there is no known commercial device which uses such techniques.
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However, it is still to be solved the need to establish a robust, reliable, fast and safe system for determining the levels of glucose concentration, which captures the metabolic complexity of the glucose levels in blood without imposing any restrictions a priori in a functional level over the analyte to be estimated.
According to the priori art of the invention previously detailed, the proposed system and apparatus of the present invention is based in the inference of the functional relationship between the shape of the pulse (PPG) and the glucose levels in blood wherein the information is deduces from the dependence between the shape of the pulse and its statistics with the state of the glucose of the patient.
The input information for performing the estimation of the glucose level in blood is processed to ease the work of the function estimator. Since the PPG signal has a variable duration, a treatment is performed to generate a fixed length vector for each measurement. This vector contains information about the shape of the pulse (auto-regression coefficients and mobile average), average distance between pulses, its variance, information about the instantaneous energy, energetic variation and clinical information of the person, such as, for example, sex, age, weight, height, clinical information of the patient (body mass index or similar measurements), etc...
The system for the inference of functions works blindly in the sense that no functional restriction is imposed to the relationship between the pulse and the glucose level in blood. Since the functional form which relates the PPG with the glucose levels is unknown, a system for infering said function has been chosen which is robust with irrelevant input variables such as clinical information and parameters derived from the shape of the PPG wave. Furthermore, said technique is related with other parameters as previously described in the prior art of the present invention. The preferred system for estimating functions in the present invention are the "random forests", in comparison to other machine learning systems and pattern recognition such as, for example, decision and regression trees (CART), Splines, classifier comitees, Support Vector Machines and Neuronal networks. The random forests are based in the parallel generation of a set of decision trees, which estimate the function with a random selection of variables in each node not performing the prune of the nodes, and training each tree with a random subset of the training database in such a way that each tree has a different generalization systematic error. Therefore, when performing an average of the estimations of each tree, the systematic error are compensated and the estimation variance decreases.
The implementation of the present invention comprises two different phases. First phase is the training of the system, which is performed only once and, therefore, it does not require any calibration/personalization afterwards. This phase consists of the obtaining of a database with information about several patient's parameters including, sex, weight, age, etc. combined with a recording of the plethysmographic wave. This information is used in the estimation of the parameters of the decision trees and are stored in the system.
The second phase consists of loading the information of the set of trees obtained in the training phase and recording the plethysmographic wave of the patient in the measurement moment with other variables such as, for example, sex, weight, age, etc... In this phase, the system reads the information of the plethysmographic pulse, performs its processing and generates a fixed length vector with the information that describes the signal. The additional information of the person is added to this vector and a set of random forests is applied, which calculate several intermediate functions of the variable of interest. Afterwards, the glucose level in blood is calculated from said intermediate functions.
The present invention consists of a system for monitoring glucose in blood (
The vector of the obtained stochastic model is linked with a digital system (3) which approximates functions based on "random forests", its main function being estimating the glucose concentration with several functions related with that, with the object of decreasing the estimation error in the post-processing step (4). The main function of the system (4) is to estimate the final values of the glucose concentration by means of the averaging of the functions of the previous step (3) to decrease the systematic error (bias) and the variance of the estimations performed with said concentration. Systems (2, 3, and 4) are implemented by means of a FPGA or DSP device.
The system (1) for obtaining the PPG wave implements a simple technique, non invasive and low cost for detecting the volume changes in the micro-vascular network of a tissue. The most basic implementation of said system requires few opto-electronic components including:
The PPG is normally used in a non-invasive way and it operates in the infrared or near infrared (NIR) wavelengths. The most known PPG waveform is the peripheral pulse (
The PPG wave comprises a physiological pulsed wave (AC component) attributed to the blood volume changes synchronized with each heartbeat. Said component is superimposed with another fundamental component (DC component) related to the respiratory rhythm, the central nervous system's activity, thermo-regulation and metabolic function. The fundamental frequency of the AC component is found around 1Hz depending on the cardiac rhythm (
The interaction between the light and the biological tissues is complex and includes optical processes like the scattering, absorption, reflection, transmission and fluorescence. The selected wavelength for the system (1) is highly important because of the following:
The PPG pulse (
As it has previously been described in the prior art of the present invention, the propagation of the pressure pulse PP along the circulatory tree also has to be taken into account. Said PP changes its shape while it moves towards the periphery of the circulatory tree suffering amplifications/attenuations and alterations of its shape and temporary characteristics. These changes are caused by the reflections suffered by the PP because of the narrowing of the arteries in the periphery. The PP pulse propagation in the arteries is further complicated by a phase distortion depending on the frequency.
Because of this, the representations of PP by means of ARMA stochastic models (Auto-Regressive Moving Average) and by means of the Teager-Kaiser coupled to an AR system (2) have been considered.
As shown in
The pulse-oxymeter of the system (1) uses the PPG to obtain information about the oxygen saturation (SpO2) in the arteries of the patient. As previously described, the SpO2 can be obtained by illuminating the tissues (normally a finger or an ear's lobe) in the red and NIR wavelengths. Normally, the SpO2 devices use the commutation between both wavelengths to determine said parameter. The amplitudes of both wavelengths are sensitive to the changes in SpO2 because of the absorption difference of HbO2 and Hb in those wavelengths. The SpO2 may be obtained from the ratio between amplitudes, the PPG and the AC and DC components.
In pulse oximetry, the intensity of the light (T) transmitted through the tissue is commonly referred to as a DC signal. The intensity is a function of the optical properties of tissue (i.e. the absorption coefficient µa and the scattering coefficient µ's). Arterial pulsation produces periodic variations in the concentrations of oxy and deoxy hemoglobin, which may result in periodic variations in the absorption coefficient.
The intensity variations of the AC component of the PPG may be expressed in the following way:
This physiological waveform is proportional to the variation of light intensity, which, in its turn, is a function of the absorption and scattering coefficients (µa and µ's, respectively). The component Δµa, may be written as a linear variation of the concentrations of oxy and deoxy hemoglobin (Δcox and Δcdeox) as follows:
Being εox and εdeox the extinction coefficients (i.e. fraction of light lost as a result of scattering and absorption per unit distance in a particular environment). Based on these equations, the arterial oxygen saturation (SpO2) may be determined by:
The expression of SpO2 as a function of the AC component may be obtained by the direct application of equations (I) and (III) at selected wave-lenghts (red and NIR).
wherein,
Normalizing the AC component with the DC to compensate the low frequency effects which are unrelated to the synchronous changes in the blood, the following is obtained:
Including this parameter in (IV) yields:
being:
Wherein ΔT(NIR) and ΔT(R) correspond to equation (I), evaluated at R and NIR wavelengths.
Although the equation (VI) is an exact solution for SpO2, k cannot be evaluated since it doesn't have T(µa,µ's). However, k and R are functions of the optical properties of the tissue, being possible to represent k as a function of R. More specifically, it may be possible to express k as a linear regression with the following form:
15 This linear regression implies a calibration factor empirically derived but assuming a flat wave with intensity P, its absorption coefficient may be defined as:
where dP represents the differential change in the intensity of a light beam passing through an infinitesimal dz in a homogeneous medium with an absorption 20 coefficient of µa. Therefore, integrating over z the Beer-Lambert law is obtained.
Assuming that T≈P, equation (VII) is thus reduced to k=1, which is the preferred approximation in the pulse-oximetry measurement performed in the present invention.
The PPG signal obtained by the system (1) is used as the system's excitation (2) (
Several parameters exist, which are basic in the form and in the propagation of the pressure pulse (PP). Said parameters are related with the cardiac output, heart rate, cardiac synchrony, breathing rate, and the metabolic function. I has also been previously detailed the close relationship between the PP and the PPG. Therefore, since the previously detailed parameters are important in the shape and propagation of the PP, the parameters listed above may also influence the PPG signal.
According to the above, the preferred embodiment of the present invention uses a stochastic ARMA(q,p) modeling (auto-regressive moving average model of order q (MA) and p(AR)).
By definition, the time series PPG(n), PPG(n-1),..., PPG(n-M) represents the realization of an AR process of order p=M if it satisfies the following finite difference equation (FDE):
Wherein the coefficients [a1, a2,...,aM] are the AR parameters and w(n) is a white process. The term akPPG(n-k) is the inner product of the ak coefficient and PPG(n-k), wherein k=1, .., M. The equation (X) may be rewritten as the following:
wherein vk = -ak.
From the above equation, it follows that the current pulse value PPG(n) equals a finite linear combination of the above values (PPG(n-k)) plus a prediction error term w(n). Therefore, rewriting the equation (X) as a linear convolution, it is obtained:
It can be defined that a0= 1 without loss of generality, and thus, the Z-transform of 10 the predictive filter may be given by:
Defining PPG z as the Z-transform of the PPG pulse, then:
where
In the MA (moving average) component case of order q=K of the PPG(n) pulse, it can be described as the response of a linear discrete filter (filter with all zeros) excited by a Gaussian white noise. Thus, the MA response of said filter written as an EDF may be:
wherein [b1, b2,..., bk] are the constants called MA parameters and e(n) is a white noise process of zero mean and variance σ2. Therefore, relating equations (XII) and (XVI) we obtain the following:
Being e(n) the error terms of the ARMA(q,p) model. Taking the Z transform of the above equation in (XVII) the following is obtained:
Since the first terms of the AR and MA vectors may be equal to 1 without loss of generality, the expression of the ARMA(q,p) model (5) in the system (2) may be given by:
5 Being A(z) and B(z) the AR and MA components of PPG(n) respectively. The preferred embodiment of the present invention uses an ARMA model of order q=1 and p=5, although any order of p and q in a range between [4,12] can be used
Once the ARMA (q,p) is calculated, by means of the Wold decomposition and the Levinson-Durbin recursion, the input signal is filtered with an H(z) inverse filter (6). Also, the statistics of the residual error e(n) are calculated with the subsystem (7).
The information obtained from these subsystems is stored in the output vector V(n) of a fixed dimension
The pre-processing system (2) of the present invention also comprises a subsystem (8) which calculates the Teager-Kaiser operator and models its output by means of an AR process of p order which is equivalent to the previously described.
In this case, without a loss of generality, the PPG pulse may be considered as a signal modulated in both amplitude and frequency, or an AM-FM signal, being the type of:
Being a(t) and w(t) the instantaneous amplitude and frequency of the PPG. The Teager-Kaiser operator of a determined signal is defined by:
wherein
This operator applied to the AM-FM modulated signal of equation (XX) results in the instantaneous energy of the source that produces the oscillation of the PPG. That is:
wherein the approximation error is negligible if the instantaneous amplitude a(t) and the instantaneous frequency w(t) do not vary too fast with respect to the average value of w(t); as is the case of the PPG pulse for the estimation of glucose levels in blood.
The AR process of order p of Ψ[PPG(t)] is implemented with a filter (9) equivalent to that of
Once the stochastic models based on an ARMA(q,p) model (5, 6 and 7) and the ARMA(q,p) model over the Teager-Kaiser operator (8 and 9), the present invention calculates the heart rate (HR) and cardiac synchrony (for example, heart rate variability), from the PPG signal by means of subsystem (10). The preferred embodiment of the present invention calculates the heart rate (HR) over time windows of the PPG which may vary between 2 seconds and 5 minutes.
The pre-processing system (2) comprises also a subsystem (11), which calculates the zero crossings of the PPG signal input, as well as the variances of these zero crossings. The preferred embodiment of the present invention calculates the heart rate over time windows of the PPG which may vary between 2 seconds and 5 minutes.
Finally, the pre-processing system (2) comprises a subsystem (12) for the generation of variables related with the patient under study which include:
All the obtained data in the subsystems comprised in the system (2) are stored in the output fixed length vector V(n).
Once the features vector of fixed length V(n) is obtained, an estimation of the SBP, DBP and MAP may be performed by means of the function approximation system (3) based in "random forests". The function estimation system presented in this invention has the advantage of not requiring any calibration once the 'random forest' has been correctly trained.
In a specific way, a random forest is a classifier which consists of a set of classifiers with a tree structure {h(V, Θk),k =1,...} wherein Θk are independent random vectors and identically distributed (i.i.d.), wherein each vector deposits a single vote for the most popular class of the input V. This approximation presents a clear advantage related to the reliability over other classifiers based on a single tree, in addition to not imposing any functional restrictions on the relationship between the pulse and glucose levels in the blood.
The random forests used in the present invention are generated by growing decision trees based on the random vector Θk, such that the predictor h(V,Θ) takes numeric values. This random vector Θk associated to each tree provides a random distribution on each node and, at the same time, it also provides information on the random sampling of the training base, resulting in different data subsets for each tree. Based on the result, the generalization error of the classifier used may be provided by the following:
Since the generalization error of a random forest is smaller than the generalization error of a single decision tree, defining
yields
Each tree has a different generalization error and p represents the correlation between the residuals defined by equation (XXIV). Thus, a lower correlation between residuals may result in better estimates. In the present invention, this minimum correlation is determined by the random sampling process of the feature vector at each node of the tree that is being trained in the subsystem (2). To further decrease the generalization error, the present invention estimates both the parameter of interest (glucose levels in blood) and linear combinations of the previously discussed parameters (height, weight, age, gender, etc.).
The random forests consist of a set of CART-type decision trees (Classification and Regression Trees), altered to introduce systematic errors (XXV) on each one and afterwards, by means of a bootstrap system, a systematic variations (these two processes are modeled by the parameter Θ in the analysis of the predictor h(V, Θ). The systematic error in each embodiment is introduced by two mechanisms:
The result of the above process is that each tree has a different systematic error.
Also, for each of these modifications, each tree is trained with "bootstrap" type samples (for example, a sample of input data is taken, leading to a fraction of the input data missing while another fraction of the data is duplicated). The effect of the bootstrap samples is that variability is introduced, which when performing the average of the estimations, it is compensated.
The overall result of the above features is system (4), in the systematic error and variability in the error can be easily compensated resulting more precise than other type of function estimators (XXV). In this system, the base classifier is a tree, which decides on the basis of levels, which makes it robust when presented with input distributions which include outliers or heterogeneous type data (as in the present invention).
The preferred embodiment of the system (4) consists of taking random samples of two elements of 47 in a node level (which can be implemented in variations between 2 and 47) and a bootstrap size of 100, also with the possibility of varying between sizes of 25 and 500.
The hand-held device according to the invention may incorporate a display for displaying data and commands for controlling the operation of the device. It also comprises at least an acoustic, mechanical, and/or optical probe, whose signals are interpreted by a post-processing system by means of a CPU centralized by means of DSP, FPGA or micro controllers. It also comprises working memories for storing the data and operative processes of the system.
The manual device of the invention may also comprise manual control buttons, according to the prior art, to activate and control it, plus batteries and/or access to an external power source.
Finally, the results obtained by the present invention may be transmitted to a PC to be analyzed, via serial port or USB or a network connection, for example by means of WiFi or Bluetooth.
It has to be noted that any alterations of the details or shape of the invention are comprised within the essence of the invention.
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