Patent Publication Number: US-2020293882-A1

Title: Near-infrared spectroscopy (nir) based glucose prediction using deep learning

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the priority benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 62/819,493 filed on Mar. 15, 2019, the disclosure of which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The subject matter disclosed herein relates to neural networks. More specifically, the subject matter disclosed herein relates to a recurrent neural network that predicts blood glucose levels based on received near-infrared radiation data. 
     BACKGROUND 
     Regular monitoring and maintaining a blood glucose level may be crucial for preventing complications arising from diabetes. Most commercially available devices for blood glucose measurement are invasive or minimally invasive, and are inconvenient and may be painful. Detection of near-infrared (NIR) radiation may be used as an indicator of blood glucose level, and may be considered as a source of data for a non-invasive approach. 
     SUMMARY 
     An example embodiment provides a recurrent neural network to predict blood glucose level that may include a first long short-term memory (LSTM) and a second LSTM. The first long short-term memory (LSTM) network may include an input to receive near-infrared (NIR) radiation data and an output. The input NIR radiation data may include multichannel NIR radiation data. In one embodiment, the input NIR radiation data may include first overtone NIR radiation data. The second LSTM network may include input to receive the output of the first LSTM network and an output to output blood glucose level data based on the NIR radiation data input to the first LSTM network. In one embodiment, the recurrent neural network may include a denoiser filter coupled to the input of the first LSTM network that receives the NIR radiation data and outputs denoised NIR radiation data to the input of the first LSTM network. In one embodiment, the denoising filter may include a Savitzky-Golay filter. 
     An example embodiment provides a system to predict blood glucose level that may include an input interface, and a recurrent neural network. The input interface may receive multi-channel near-infrared (NIR) radiation data. In one embodiment, the input NIR radiation data may include first overtone NIR radiation data. The recurrent neural network may be coupled to the input interface, and may include a first long short-term memory (LSTM) and a second LSTM. The first long short-term memory (LSTM) network may include an input to receive near-infrared (NIR) radiation data and an output. The second LSTM network may include input to receive the output of the first LSTM network and an output to output blood glucose level data based on the NIR radiation data input to the first LSTM network. In one embodiment, the recurrent neural network may include a denoiser filter coupled to the input of the first LSTM network that receives the NIR radiation data and outputs denoised NIR radiation data to the input of the first LSTM network. In one embodiment, the denoising filter may include a Savitzky-Golay filter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING 
       In the following section, the aspects of the subject matter disclosed herein will be described with reference to exemplary embodiments illustrated in the figure, in which: 
         FIG. 1  depicts a block diagram of a first example embodiment of a system to predict blood glucose level according to the subject matter disclosed herein; 
         FIG. 2  is another block diagram of the first example embodiment of the system according to the subject matter disclosed herein; 
         FIG. 3  depicts block diagram of an example embodiment of an LSTM network according to the subject matter disclosed herein; and 
         FIG. 4  depicts a portion of the system depicted in  FIG. 1  in which a data normalizer and a denoiser are part of the system. 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the disclosure. It will be understood, however, by those skilled in the art that the disclosed aspects may be practiced without these specific details. In other instances, well-known methods, procedures, components and circuits have not been described in detail not to obscure the subject matter disclosed herein. 
     Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment disclosed herein. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” or “according to one embodiment” (or other phrases having similar import) in various places throughout this specification may not be necessarily all referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In this regard, as used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not to be construed as necessarily preferred or advantageous over other embodiments. Additionally, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Also, depending on the context of discussion herein, a singular term may include the corresponding plural forms and a plural term may include the corresponding singular form. Similarly, a hyphenated term (e.g., “two-dimensional,” “pre-determined,” “pixel-specific,” etc.) may be occasionally interchangeably used with a corresponding non-hyphenated version (e.g., “two dimensional,” “predetermined,” “pixel specific,” etc.), and a capitalized entry (e.g., “Counter Clock,” “Row Select,” “PIXOUT,” etc.) may be interchangeably used with a corresponding non-capitalized version (e.g., “counter clock,” “row select,” “pixout,” etc.). Such occasional interchangeable uses shall not be considered inconsistent with each other. 
     Also, depending on the context of discussion herein, a singular term may include the corresponding plural forms and a plural term may include the corresponding singular form. It is further noted that various figures (including component diagrams) shown and discussed herein are for illustrative purpose only, and are not drawn to scale. Similarly, various waveforms and timing diagrams are shown for illustrative purpose only. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, if considered appropriate, reference numerals have been repeated among the figures to indicate corresponding and/or analogous elements. 
     The terminology used herein is for the purpose of describing some example embodiments only and is not intended to be limiting of the claimed subject matter. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The terms “first,” “second,” etc., as used herein, are used as labels for nouns that they precede, and do not imply any type of ordering (e.g., spatial, temporal, logical, etc.) unless explicitly defined as such. Furthermore, the same reference numerals may be used across two or more figures to refer to parts, components, blocks, circuits, units, or modules having the same or similar functionality. Such usage is, however, for simplicity of illustration and ease of discussion only; it does not imply that the construction or architectural details of such components or units are the same across all embodiments or such commonly-referenced parts/modules are the only way to implement some of the example embodiments disclosed herein. 
     It will be understood that when an element or layer is referred to as being on, “connected to” or “coupled to” another element or layer, it can be directly on, connected or coupled to the other element or layer or intervening elements or layers may be present. In contrast, when an element is referred to as being “directly on,” “directly connected to” or “directly coupled to” another element or layer, there are no intervening elements or layers present. Like numerals refer to like elements throughout. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. 
     The terms “first,” “second,” etc., as used herein, are used as labels for nouns that they precede, and do not imply any type of ordering (e.g., spatial, temporal, logical, etc.) unless explicitly defined as such. Furthermore, the same reference numerals may be used across two or more figures to refer to parts, components, blocks, circuits, units, or modules having the same or similar functionality. Such usage is, however, for simplicity of illustration and ease of discussion only; it does not imply that the construction or architectural details of such components or units are the same across all embodiments or such commonly-referenced parts/modules are the only way to implement some of the example embodiments disclosed herein. 
     Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this subject matter belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. 
     As used herein, the term “module” refers to any combination of software, firmware and/or hardware configured to provide the functionality described herein in connection with a module. The software may be embodied as a software package, code and/or instruction set or instructions, and the term “hardware,” as used in any implementation described herein, may include, for example, singly or in any combination, hardwired circuitry, programmable circuitry, state machine circuitry, and/or firmware that stores instructions executed by programmable circuitry. The modules may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, but not limited to, an integrated circuit (IC), system on-chip (SoC) and so forth. The various components and/or functional blocks disclosed herein may be embodied as modules that may include software, firmware and/or hardware that provide functionality described herein in connection with the various components and/or functional blocks. 
     Predicting blood glucose level based on NIR radiation data may be formulated as a regression problem in which NIR data (a vector time series) may be used as an input and blood glucose level (scalar time series) is a predicted output. The NIR data may be collected as multiband and/or multichannel data. At each time stamp, one NIR data input vector corresponds to a predicted output blood glucose level. 
     Training data (NIR data paired with a corresponding blood glucose level) may be collected for training a recurrent neural network. Typically, training data may be hard to collect and may be limited in quantity. Previously, a partial least squares (PLS) regression has been used to fit a neural network to training data. PLS may work well with clean data (no noise); however, PLS may perform poorly with real-world data that contains noise. Additionally, a PLS regression does not explicitly take into account information contained in data that was previously regressed, thereby making a trend-based prediction, such as a blood glucose level prediction, less reliable. 
     The subject matter disclosed herein provides a deep learning-based recurrent solution for a glucose-level prediction that uses a regressor network configuration that includes two stacked long short-term memory (LSTM) networks. In one embodiment, an autoencoder may be used at the input of the regression network for denoising purposes. In another embodiment, a denoising filter may be used in addition to or alternatively to an autoencoder. Additionally, the training data may be augmented by interpolation of the training data to provide an enhanced set of training data that may be used with the regressor network configuration disclosed hereon. 
       FIG. 1  depicts a block diagram of a first example embodiment of a system  100  to predict blood glucose level according to the subject matter disclosed herein. In one embodiment, the system  100  may be configured as a recurrent neural network that includes a first long short-term memory (LSTM) network  101  and a second LSTM network  102 . The first LSTM network  101  receives NIR radiation data X, which may be multiband and/or multichannel NIR data. The output of the first LSTM network  101  is input to the second LSTM network  102 . A portion of the cell state of the LSTM network  101  is retained in the LSTM network  101  at  103 . Similarly, a portion of the cell state of the LSTM network  102  is retained in the LSTM network  102  at  104 . The output of the second LSTM network  102  is input of a dropout  105 . The dropout  105  may function as a regularizer. During training, the dropout  105  may set a random subset of the activations to zero to help the network generalize better. The output of the dropout  105  is input to a fully-connected layer FC  106  that may be used to perform regression of the glucose levels. The output Y of the FC  104  is a single-dimensional prediction (i.e., a number) of a blood glucose level corresponding to the input NIR radiation data. In one embodiment, the dimensionality of the input NIR radiation data X is 131, the dimensionality of the first layer is less than 41, and the dimensionality of the second layer is less than 20. 
       FIG. 2  is another block diagram of the first example embodiment of the system  100  according to the subject matter disclosed herein.  FIG. 2  is the system  100  depicted as being unrolled in the time domain. The left-most system  100  shows the system  100  at time t. The input to the system  100  is a vector X t  and the output is Y t . The system  100  in the center of  FIG. 2  shows the system  100  at time t+1. The input to the system  100  is X t+1  and the output is Y t+1 . The right-most system  100  of  FIG. 2  shows the system  100  at time t+2. The input to the system  100  is X t+2  and the output is Y t+2 . At  103 , a portion of the cell state of the LSTM network  101  at time t is retained in the LSTM network  101  at time t+1, and portion of the cell state of the LSTM network  101  at time t+1 is retained in the LSTM network  101  at time t+2. Similarly, at  104 , a portion of the cell state of the LSTM network  102  at time t is retained in the LSTM network  102  at time t+1, and portion of the cell state of the LSTM network  102  at time t+1 is retained in the LSTM network  102  at time t+2. 
       FIG. 3  depicts block diagram of an example embodiment of an LSTM network  300  according to the subject matter disclosed herein. The first and second LSTM networks  101  and  102  may be configured as the example embodiment of the LSTM network  300 . The LSTM network  300  may include three gate layers  301 - 303  and a tanh layer  304 . 
     The gate layer  301  functions as a forget gate layer to decide what information in the cell state C that will be forgotten. The forget gate layer  301  may include a sigmoid layer that receives the previous output h t−1  of the LSTM network  300  and a current input X t . The forget gate layer  301  outputs f t , which includes a value f t  between 0 and 1 for each value in a previous cell state C t−1 , in which 0 represents a complete forget action of the value of the previous cell state and 1 represents a complete retention of the value of the previous cell state. The values of the output f t  and the previous cell state C t−1  are multiplied by a multplier  305 . The output f t  may be determined as 
         f   t =σ g ( w   f   x   t   +U   f   h   t−1   +b   f ),  (1)
 
     in which σ 9  is the sigmoid function of the input gate unit, W f  is the size of the first hidden layer (i.e., LSTM network  101 ) times the input feature size d, U f  is the size of the first hidden layer squared, and b f  is a constant associated with the forget gate unit. In one embodiment, the size of the first hidden layer may be 41. 
     The gate layer  302  functions as an input gate layer to decide what new information will be stored in the current cell state C t . The input gate layer  302  may include a sigmoid layer that receives the previous output h t−1  of the LSTM network  300  and the current input X t , and outputs a vector i t  as 
         i   t =σ g ( W   i   x   t   +U   i   h   t−1   +b   i ),  (2)
 
     in which σ g  is the sigmoid function of the input gate unit, W i  is the size of the first hidden layer (i.e., LSTM network  101 ) times the input feature size d=131, U f  is the size of the first hidden layer squared, and b 1  is a bias value. 
     The layer  304  receives as an input the previous output h t−1  of the LSTM network  300  and the current input X t . The layer  304  outputs an intermediate cell state vector c t  as 
         c   t =σ c ( W   c   x   t   +U   c   h   t−1   +b   c ),  (3)
 
     in which σ c  is a sigmoid function W c  is a learnable parameter matrix of weight that having a size of the first hidden layer (i.e., LSTM network  101 ) times the input feature size d=131, U c  is the size of the first hidden layer squared, and b c  is a bias value constant. 
     The output i t  of the input gate layer  302  and the intermediate cell state vector c t  output from the layer  304  are multiplied by a multiplier  306 . The output of the multiplier  305  and the output of the multiplier  306  are added by a summer  307  to form the current cell state C t  of the LSTM network  300  as 
         C   t   =f   t   ·c   t−1   +i   t ·σ c ( W   c   x   t   +U   c   h   t−1   +b   c )  (4)
 
     The gate layer  303  functions as an output gate to determine what output h t  will be output from the LSTM network  300 . The output gate layer  303  receives as an input the previous output h t−1  of the LSTM  300  and the current input X t , and outputs o t  as 
         o   t =σ g ( W   o   x   t   +U   o   h   t−1   +b   o ),  (5)
 
     in which σ g  is the sigmoid function of the input gate unit, W o  is the size of the first hidden layer (i.e., LSTM network  101 ) times the input feature size d=131, U o  is the size of the first hidden layer squared, and b o  is a bias value constant. 
     A tanh function is applied to the cell state C t . The output o t  of the output gate layer  303  and the output of the tanh function are multiplied by a multiplier  308  to form the output h t , which is an encoded representation that is similar to an output feature map of an activation function, as 
         h   t   =o   t *tanh( C   t ).  (6)
 
     To train the system  100 , training data may be used having NIR data paired with a corresponding blood glucose level. Preprocessing of the training data may be used to provide better quality data for fitting the model of system  100 , which is purely data driven. Additionally, the raw NIR data may be denoised to prevent the system  100  from overfitting the noise. In one embodiment, a Savitzky-Golay filter may be used to smooth the NIR data. In yet another embodiment, normalization may be provided to provide better data distribution and provide an easier gradient descent for training purposes. A stochastic gradient descent (SGD) may be used during backpropagation to optimize an L2 loss function.  FIG. 4  depicts a portion of the system  100  in which a data normalizer  107  and a denoiser  108  are part of the system  100 . The training data included 131 channels, and may include first overtone data. The output of the first LSTM network  101  is h1 channels, and the output of the second LSTM network  102  is h2 channels. 
     To further improve the performance and capability of generalization of model by increasing the training dataset size and variety. In one embodiment, the input training data may be augmented by interpolation of the NIR data and blood glucose level pairs. 
     As will be recognized by those skilled in the art, the innovative concepts described herein can be modified and varied over a wide range of applications. Accordingly, the scope of claimed subject matter should not be limited to any of the specific exemplary teachings discussed above, but is instead defined by the following claims.