Patent Description:
Monitoring of concentration of blood compounds has always been a topic of much interest. The monitoring of concentration of the blood compounds are typically performed invasively wherein the skin of a test (human or animal) subject is pierced to obtain a blood sample for testing. In a non-invasive method, collection of blood sample is not required for prediction of concentration of the blood compound. Also, the non-invasive method provides a painless means of blood compound monitoring especially for those who need to check the concentration of a particular compound several times a day. Some of the typical methods used for monitoring the concentration of blood compounds non-invasively are Mid-Infrared (Mid-IR), Near-Infrared (NIR), and Raman spectroscopy. <CIT> refers to the estimation and subtraction of interference from a NIR spectral measurement, in particular determining targeted orthogonal interference and development of models for removing unwanted spectral variation from a NIR measurement using techniques such as multivariate regression and discrete factor analysis. <CIT> refers to systems for the determination of analytes in body fluids, in particular a system for the non-invasive determination of analytes in body fluids. <CIT> refers to a method and apparatus for non-invasive prediction of hematocrit. <NPL>, is a paper on a method of non-invasive blood glucose estimation from the PPG signal using a SVR model having <NUM> dimensional features as input.

Of the above methods, the NIR spectroscopy is widely used for monitoring concentration of blood compounds. However, the prediction of a particular compound concentration based on NIR spectroscopy data is very challenging when the particular compound's concentration is to be calculated in the presence of other compounds that are not of interest. For example, when monitoring the glucose concentration in the blood, the other compounds in the blood such as water, collagen, keratin, cholesterol, etc. acts as interfering compounds. Another major challenge is to remove drift component from the NIR spectroscopy data that adversely affects the features used for prediction. This in turn affects the prediction accuracy of the blood compound based on the NIR spectroscopy data.

In a related method, drift noise is removed by using an optimal filter. However, the method requires an error covariance matrix which is not possible to compute accurately. In another related method, a baseline scatter removal algorithm is used to compute drifts associated with multiple spectra simultaneously. However, the method requires continuous measurements with the same compound concentration, and therefore it is not suitable for blood composition analysis.

Therefore, there is a need for a method of removing drift from the NIR spectroscopic data for monitoring of concentration of compounds in the blood. Furthermore, there is also a need to obtain a set of global features that could be used for prediction of the concentration of the blood compound using regression.

The dependent claims recite advantageous embodiments of the present invention.

According to an aspect of an example embodiment, there is provided a method of estimating concentration of a blood compound, the method including: removing a baseline drift from Near-Infrared (NIR) spectroscopy data to obtain drift-free spectral features; obtaining a set of global features based on the drift-free spectral features; and estimating the concentration of the blood compound by regression using the set of global features.

The removing the baseline drift from the NIR spectroscopy data, in the invention, includes removing the baseline drift from the NIR spectroscopy data using principal component analysis (PCA).

The removing baseline drift from the NIR spectroscopy data may include: computing a plurality of principal components of the NIR spectroscopy data; obtaining a drift approximation from the plurality of principal components; obtaining a spectral drift approximation from the drift approximation for each spectral feature according to a magnitude of the spectral feature; and removing respective spectral drift approximation from each spectral feature to obtain the drift-free spectral features.

The obtaining the drift approximation from the plurality of principal components may include: selecting a principal component that characterizes the baseline drift, from among the plurality of principal components, based on a change in the principal component over time; and obtaining a polynomial approximation of a predefined degree of the selected principal component as the drift approximation.

The selecting the principal component that characterizes the baseline drift may include selecting a first principal component from among the plurality of principal components as the principal component that characterizes the baseline drift.

The obtaining the polynomial approximation of the predefined degree of the selected principal component as the drift approximation may include obtaining, as the drift approximation, the polynomial approximation that minimizes a least squared error between the polynomial approximation and the baseline drift.

The obtaining the spectral drift approximation from the drift approximation may include: normalizing the drift approximation; and obtaining the spectral drift approximation by scaling the normalized drift approximation by an amplitude-span of the spectral feature.

The normalizing the drift approximation may include dividing the drift approximation by an amplitude-span of the drift approximation to obtain the normalized drift approximation.

The obtaining the set of global features of the invention comprises: obtaining similarity values of each drift-free spectral feature with a compound vector; obtaining a similarity metric for each drift-free spectral feature using the similarity values; ranking the drift-free spectral features based on the similarity metric; and selecting a predefined number of drift-free spectral features as the set of global features.

According to an aspect of another example embodiment, there is provided a blood compound concentration prediction apparatus including at least one processor including: a drift removal unit configured to remove a baseline drift from Near-Infrared (NIR) spectroscopy data to obtain drift-free spectral features; a global feature extraction unit configured to obtain a set of global features based on the drift-free spectral features; and a prediction unit configured to estimate a concentration of a blood compound by regression using the set of global features.

The drift removal unit of the invention removes the baseline drift from the NIR spectroscopy data using principal component analysis (PCA).

The drift removal unit: may compute a plurality of principal components of the NIR spectroscopy data; may obtain a drift approximation from the plurality of principal components; may obtain a spectral drift approximation from the drift approximation for each spectral feature according to a magnitude of the spectral feature; and may remove respective spectral drift approximation from each spectral feature to obtain the drift-free spectral features.

The drift removal unit: may select a principal component that characterizes the baseline drift, from among the plurality of principal components, based on a change in the principal component over time; and may obtain a polynomial approximation of a predefined degree of the selected principal component as the drift approximation.

The drift removal unit may select a first principal component from among the plurality of principal components as the principal component that characterizes the baseline drift.

The drift removal unit may obtain as the drift approximation, a polynomial approximation that minimizes a least squared error between the polynomial approximation and the baseline drift.

The drift removal unit: may normalize the drift approximation; and may obtain the spectral drift approximation by scaling the normalized drift approximation by an amplitude-span of the spectral feature.

The drift removal unit may divide the drift approximation by an amplitude-span of the drift approximation to obtain the normalized drift approximation.

The global feature extraction unit of the invention obtains similarity values of each drift-free spectral feature with a compound vector; obtains a similarity metric for each drift-free spectral feature using the obtained similarity values; ranks the drift-free spectral features as per the similarity metric; and selects a predefined number of drift-free spectral features as the set of global features.

The above and/or other aspects will be more apparent by describing certain exemplary embodiments, with reference to the accompanying drawings, in which:.

Exemplary embodiments are described in greater detail below with reference to the accompanying drawings.

The matters defined in the description, such as detailed construction and elements, are provided to assist in a comprehensive understanding of the exemplary embodiments. However, it is apparent that the exemplary embodiments can be practiced without those specifically defined matters.

The embodiments herein and the various features and advantages details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those of skill in the art to practice the embodiments herein.

An example embodiment provides a method for predicting concentration of a blood compound non-invasively using NIR spectroscopy. The embodiment provides a drift removal algorithm which makes use of information from principal components of the NIR spectroscopy data for the drift removal process. The term "drift" may refer to a baseline drift of a bio-signal, such as a photoplethysmogram (PPG) signal, an electromyography (EMG) signal, or an electrocardiography (ECG) signal. The embodiment further provides extraction of a set of global features for prediction of the concentration of the blood compound using regression. The same is illustrated in <FIG> is a flowchart diagram illustrating a method of predicting concentration of a blood compound of interest non-invasively using Near-Infrared spectroscopy data, according to one example embodiment. The step by step process for predicting the concentration of the blood compound of interest using the present prediction method is explained herein as follows. In operation <NUM>, a plurality of principal components of a NIR spectroscopy data set are computed according to principal component analysis (PCA). In operation <NUM>, a drift approximation from the plurality of principal components is obtained. For example, the drift approximation have a value that minimizes a least-squared error <MAT>, wherein <MAT> denotes the drift approximation and pc denotes a principal component (e.g., a first principal component) selected from the plurality of principal components. In another example, the drift approximation is a polynomial approximation that is obtained based on Remez algorithm. Further, a spectral drift approximation from the drift approximation for each spectral feature according to magnitude of the spectral feature is obtained in operation <NUM>. Then, drift from the spectral features is removed by subtracting respective spectral drift approximation from each spectral feature in operation <NUM>. Upon removing the drift from the spectral features, a set of global features for a plurality of test subjects are obtained in operation <NUM>. Finally, the concentration of the compound is estimated by regression using the obtained set of global features in operation <NUM>.

<FIG> is a block diagram illustrating a blood compound concentration prediction apparatus, according to one example embodiment. The blood compound concentration prediction apparatus <NUM> may be embedded in an electronic apparatus. Examples of the electronic apparatus may include a cellular phone, a smartphone, a tablet PC, a laptop computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, an MP3 player, a digital camera, a wearable device, and the like, and examples of the wearable device may include a watch-type device, a wristband-type device, a ring-type device, a waist belt-type device, a necklace-type device, an ankle band-type device, a thigh band-type device, a forearm band-type device, and the like. However, the electronic device is not limited to the above examples, and the wearable device is neither limited thereto.

According to one example embodiment, the blood compound concentration prediction apparatus <NUM> may include a drift removal unit <NUM>, global feature extraction unit <NUM> and a prediction unit <NUM>. The drift removal unit <NUM>, the global feature extraction unit <NUM> and the prediction unit <NUM> may be implemented by one or more processors. The drift removal unit <NUM> computes and removes drift from the NIR spectroscopy data. In detail, the NIR spectroscopy data is obtained as follows.

At first, the value of a blood compound is obtained using a standard invasive procedure (e.g., a blood pressure measurement using a cuff). Then, a non-invasive spectral scan is performed on a person/test subject using near-Infrared spectrometer to obtain raw NIR spectra. The raw NIR spectra is labelled as a blood compound value which was obtained from the invasive procedure, and is stored in the blood compound concentration prediction apparatus <NUM>. The obtained raw NIR spectra are preprocessed further to obtain compound spectra. The compound spectra and the associated compound values may be arranged into the form of the matrix X using data obtained in consecutive measurements, which would be referred as data matrix in the rest of the document.

Here, c = [c<NUM> c<NUM>. cN]T is a compound vector. The matrix S is the NIR spectroscopy data. The NIR spectroscopy data is affected by the drift which in turn affects the prediction accuracy of the compound of interest. Each column of the matrix S is the absorption spectra associated with the wavelength λ and may be represented by the vector sλ. It may be noted that the absorption spectra sλ in some embodiments can be interchangeably referred to as "spectral feature" or "feature".

The absorption spectra sλ could be written as <MAT>.

Here, <MAT> is the true absorption spectra and fλ is the drift affecting the true absorption spectra.

The drift removal unit <NUM> obtains an estimate of the drift component <MAT> and subtracts it from sλ to obtain the drift-free spectra <MAT> which is expressed as: <MAT>.

In one example embodiment, the drift removal unit <NUM> removes drift using principal component analysis (PCA). The drift removal unit <NUM> performs the PCA operation for obtaining the ith principal component <MAT> which is described as <MAT> <MAT>.

Here, the variables follow the standard notations. If the drift component on the data set is significant enough to impact the predictions based on the set, it is likely to manifest in the first principal component of the data set. Else, the drift would manifest in say ith principal component. Also, since all the principal components are uncorrelated, it is a reasonable assumption that if the drift component is captured in the ith principal component, it is unlikely that it would significantly manifest in any other principal components. Let the ith Principal component in which drift is manifested be denoted by pc. In an example embodiment, the first principal component may be selected from a plurality of principal components to remove the drift component when the change in the value of the first principal component over time is greater than a predetermined value.

<FIG> illustrate a graphical representation of a first principal component and corresponding linear approximation of NIR spectroscopy data of different test subjects labelled S11, S61, and S71 according to one example embodiment. As shown in <FIG>, there is a predominance of the drift component. The slope of the linear approximation gives the rate of change of the drift with respect to the time.

<FIG> illustrates the first principal component of <NUM>-decimation of the NIR spectroscopy data of test subject S11, according to one example embodiment. The NIR spectroscopy data is expressed a:s <MAT>.

The d-decimation of S is defined as <MAT>.

The set Sd is obtained by including every dth row of the matrix S. As shown in <FIG>, the first principal component of Sd is characterized by a linear approximation whose slope is approximately <NUM> times the slope of the original matrix S in <FIG>. This demonstrates that the drift component is captured in the first principal component in this example.

<FIG> is a flowchart diagram illustrating a method of removing drift using principal component analysis, according to one example embodiment. According to this example embodiment, the drift removal unit <NUM> removes drift using principal component analysis. The step by step process performed by the drift removal unit <NUM> is explained herein as follows. In operation <NUM>, a principal component pc that characterizes drift is selected from a plurality of principal components. In operation <NUM>, the polynomial approximation pc' of the selected principal component pc is obtained as a drift approximation. In one example embodiment, drift approximation pc' may be obtained as pc' that minimizes the least squared error <MAT>. Further, in operation <NUM>, for each sλ, the drift approximation is scaled as per the magnitude of sλ to obtain spectral drift approximation <MAT>. The spectral drift approximation <MAT> is obtained as <MAT>.

Here, ds is the amplitude span of sλ given by ds = (max(sλ) - min(sλ)) and dp is the amplitude span of <MAT> given by <MAT>.

Finally, in operation <NUM>, the drift removal is performed by subtracting the spectral drift approximation <MAT> from the respective sλ for every λ. This is represented as <MAT>.

The <MAT> is also referred to as drift-free spectral feature or simply drift-free feature.

<FIG> is a flowchart diagram illustrating a method of extracting global features for prediction of concentration of compound, according to one example embodiment. In this example embodiment, one or more operations performed by a global feature extraction unit <NUM> for extracting global features are explained herein as follows. In operation <NUM>, a similarity value of each drift-free spectral feature with the respective compound vector is obtained. Let there be P test subjects denoted as <MAT>, k = <NUM>,<NUM>,. Let the corresponding drift-free spectral features for the subject <MAT> be denoted as <MAT> and the compound's concentration be denoted by ck. In one example embodiment, the similarity value may be obtained as the correlation of the drift-free spectral feature <MAT> with the compound vector ck, which may be computed as <MAT>.

In operation <NUM>, similarity metric for each drift-free spectral feature is obtained using similarity values obtained across all test subjects. In one example embodiment, the similarity metric may be computed as <MAT>.

In operation <NUM>, the drift-free spectral features are ranked as per the similarity metric. In operation <NUM>, a K number of drift-free spectral features are selected in order of the ranking for prediction of the compound concentration using regression. The number K may have a predetermined value, and/or may be decided based on the performance of particular regression method employed for prediction. The K number of drift-free features are referred to as "global features" in rest of the document.

Now, based on the obtained global features, the prediction unit <NUM> predicts or estimates concentration of the blood compound using regression from the drift free spectroscopy data.

<FIG> is a block diagram illustrating a blood compound concentration prediction apparatus, according to another example embodiment. The blood compound concentration prediction apparatus <NUM> may be embedded in an electronic apparatus. Examples of the electronic apparatus may include a cellular phone, a smartphone, a tablet PC, a laptop computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, an MP3 player, a digital camera, a wearable device, and the like, and examples of the wearable device may include a watch-type device, a wristband-type device, a ring-type device, a waist belt-type device, a necklace-type device, an ankle band-type device, a thigh band-type device, a forearm band-type device, and the like. However, the electronic device is not limited to the above examples, and the wearable device is neither limited thereto.

Referring to <FIG>, the blood compound concentration prediction apparatus <NUM> includes a processor <NUM>, an input interface <NUM>, a storage <NUM>, a communication interface <NUM>, and an output interface <NUM>. Here, the processor <NUM> may perform the operations of the drift removal unit <NUM>, the global feature extraction unit <NUM>, and the prediction unit <NUM> described above with reference to <FIG>, such that detailed description thereof will be omitted.

The input interface <NUM> may receive NIR spectroscopy data, and may receive input of various operation signals from a user. In the embodiment, the input interface <NUM> may include a keypad, a dome switch, a touch pad (static pressure/capacitance), a jog wheel, a jog switch, a hardware (H/W) button, and the like. Particularly, the touch pad, which forms a layer structure with a display, may be called a touch screen.

The storage <NUM> may store programs or commands for operation of the blood compound concentration prediction apparatus <NUM>, and may store data input to and output from the blood compound concentration prediction apparatus <NUM> and data processed by the blood compound concentration prediction apparatus <NUM>, and the like.

The storage <NUM> may include at least one storage medium of a flash memory type memory, a hard disk type memory, a multimedia card micro type memory, a card type memory (e.g., an SD memory, an XD memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, and an optical disk, and the like. Further, the blood compound concentration prediction apparatus <NUM> may operate an external storage medium, such as web storage and the like, which performs a storage function of the storage <NUM> on the Internet.

The communication interface <NUM> may communicate with an external device. For example, the communication interface <NUM> may transmit, to the external device, the data input to the blood compound concentration prediction apparatus <NUM>, data stored in and processed by the blood compound concentration prediction apparatus <NUM>, and the like, or may receive, from the external device, various data useful for estimating a blood compound concentration.

In this case, the external device may be medical equipment using the data input to the blood compound concentration prediction apparatus <NUM>, data stored in and processed by the blood compound concentration prediction apparatus <NUM>, and the like, a printer to print out results, or a display device. In addition, the external device may be a digital TV, a desktop computer, a cellular phone, a smartphone, a tablet PC, a laptop computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, an MP3 player, a digital camera, a wearable device, and the like, but is not limited thereto.

The communication interface <NUM> may communicate with external devices by using Bluetooth communication, Bluetooth Low Energy (BLE) communication, Near Field Communication (NFC), WLAN communication, Zigbee communication, Infrared Data Association (IrDA) communication, Wi-Fi Direct (WFD) communication, Ultra Wideband (UWB) communication, Ant+ communication, WIFI communication, Radio Frequency Identification (RFID) communication, <NUM> communication, <NUM> communication, <NUM> communication, and the like. However, this is merely exemplary and communication is not limited thereto.

The output interface <NUM> may output the data input to the blood compound concentration prediction apparatus <NUM>, data stored in and processed by the blood compound concentration prediction apparatus <NUM>, and the like. In the embodiment, the output interface <NUM> may output the data input to the blood compound concentration prediction apparatus <NUM>, data stored in and processed by the blood compound concentration prediction apparatus <NUM>, and the like, by using at least one of an acoustic method, a visual method, and a tactile method. To this end, the output interface <NUM> may include a display, a speaker, a vibrator, and the like.

Claim 1:
A method of estimating concentration of a blood compound, the method comprising
removing a baseline drift from Near-Infrared (NIR) spectroscopy data obtained in consecutive measurements on a test subject to obtain drift-free spectral features as vectors;
obtaining a set of global features for a plurality of test subjects based on the drift-free spectral features; and
estimating the concentration of the blood compound by regression using the set of global features;
wherein the removing the baseline drift from the NIR spectroscopy data comprises removing the baseline drift from the NIR spectroscopy data using principal component analysis (PCA);
characterized in that
the obtaining the set of global features comprises:
(<NUM>) obtaining similarity values of each drift-free spectral feature with a compound vector containing the concentrations of the blood compound in the consecutive measurements by calculating a correlation of the drift-free spectral feature as vector with the compound vector, respectively;
(<NUM>) obtaining a similarity metric for each drift-free spectral feature using the similarity values obtained across all test subjects;
(<NUM>) ranking the drift-free spectral features using the similarity metric; and
(<NUM>) selecting a predefined number of drift-free spectral features in order of the ranking as the set of global features.