A blood glucose signal processing method for blood glucose detection

Abstract

The present invention discloses a blood glucose signal processing method for blood glucose detection, which relates to the technical field of blood glucose signal processing. The method comprises: using an optical blood glucose sensor and an electrochemical blood glucose sensor to synchronously and continuously detect the blood glucose of a target user in a predicted detection window, obtaining an optical blood glucose signal detection value sequence and an electrochemical blood glucose signal detection value sequence; obtaining an optical blood glucose signal detection value subsequence set and an electrochemical blood glucose signal detection value subsequence set; performing multimodal interactive fusion analysis to obtain a blood glucose fusion feature; calling the target user's historical blood glucose feature set to authenticate the blood glucose fusion feature and obtain an authentication result. If the authentication result is passed, the blood glucose fusion feature is used as the blood glucose signal processing result. The present invention solves the technical problem in the prior art that the accuracy of blood glucose signal processing is low and it cannot effectively reflect the user's blood glucose status. The technical effect of improving the reliability of blood glucose signal processing is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of blood glucose signal processing, and in particular to a blood glucose signal processing method for blood glucose detection. Background Art

[0002] Diabetes is a common metabolic disease with an increasing incidence, affecting a large population worldwide. Therefore, diabetes management requires the ability to accurately monitor patients' blood sugar levels in real time, allowing for timely adjustments to treatment plans based on blood sugar fluctuations. Traditional blood sugar testing methods rely primarily on periodic blood draws and measurements with a blood glucose meter. While this intermittent method is simple and easy to use, it fails to provide information on dynamic blood sugar changes, limiting the precise treatment of diabetes.

[0003] The existing technology has the technical problem of low accuracy in blood glucose signal processing and inability to effectively reflect the user's blood glucose status. Summary of the Invention

[0004] The present application provides a blood glucose signal processing method for blood glucose detection, which is used to solve the technical problem in the prior art that the blood glucose signal processing accuracy is low and cannot effectively reflect the user's blood glucose status.

[0005] In view of the above problems, the present application provides a blood glucose signal processing method for blood glucose detection, the method comprising:

[0006] Using an optical blood glucose sensor and an electrochemical blood glucose sensor to synchronously and continuously detect the target user's blood glucose level in a predicted detection window, obtaining a sequence of optical blood glucose signal detection values and a sequence of electrochemical blood glucose signal detection values;

[0007] Dividing the optical blood glucose signal detection value sequence and the electrochemical blood glucose signal detection value sequence according to a preset division scale to obtain an optical blood glucose signal detection value subsequence set and an electrochemical blood glucose signal detection value subsequence set;

[0008] performing a multimodal interactive fusion analysis on the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set to obtain a blood glucose fusion feature;

[0009] The target user's historical blood glucose feature set is called to authenticate the blood glucose fusion feature to obtain an authentication result. If the authentication result is passed, the blood glucose fusion feature is used as the blood glucose signal processing result.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The present application uses an optical blood glucose sensor and an electrochemical blood glucose sensor to synchronously and continuously detect the target user's blood glucose in the predicted detection window, obtains an optical blood glucose signal detection value sequence and an electrochemical blood glucose signal detection value sequence, then divides the optical blood glucose signal detection value sequence and the electrochemical blood glucose signal detection value sequence according to a preset division scale, obtains an optical blood glucose signal detection value subsequence set and an electrochemical blood glucose signal detection value subsequence set, and then performs multimodal interactive fusion analysis on the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set to obtain a blood glucose fusion feature, and then calls the target user's historical blood glucose feature set to authenticate the blood glucose fusion feature to obtain an authentication result. If the authentication result is passed, the blood glucose fusion feature is used as the blood glucose signal processing result. The technical effect of improving the quality and reliability of blood glucose signal processing is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Attachment Figure 1 This is a flow chart of a blood glucose signal processing method for blood glucose detection provided by an embodiment of the present invention.

[0013] Attachment Figure 2 This is a flow chart of obtaining blood glucose fusion features in a blood glucose signal processing method for blood glucose detection provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall equally within the scope limited by the appended claims of the application.

[0015] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0016] Examples, such as the attached Figure 1 As shown, the present application provides a blood glucose signal processing method for blood glucose detection, wherein the method comprises:

[0017] S1: Using an optical blood glucose sensor and an electrochemical blood glucose sensor to synchronously and continuously detect the blood glucose level of the target user in the predicted detection window, and obtaining a sequence of optical blood glucose signal detection values and a sequence of electrochemical blood glucose signal detection values;

[0018] In one possible embodiment, the optical blood glucose sensor measures blood glucose levels by detecting properties such as light absorption, scattering, or reflection, typically with non-invasive or minimally invasive, continuous detection capabilities. The electrochemical blood glucose sensor measures blood glucose concentration using the current or voltage signal generated by an electrochemical reaction with glucose, and is characterized by high accuracy. The target user is an individual undergoing blood glucose testing, typically a diabetic patient or someone requiring blood glucose testing.

[0019] The predicted detection window refers to the time range for blood glucose data collection and detection within a preset time period, which can provide continuous data support for dynamic blood glucose analysis. The preset detection window can be set by those skilled in the art. The optical blood glucose signal detection value sequence and the electrochemical blood glucose signal detection value sequence are blood glucose data recorded by optical and electrochemical sensors, respectively, organized in time series.

[0020] By using an optical blood glucose sensor and an electrochemical blood glucose sensor to simultaneously start the target user's blood glucose level and continuously collect data within the same time period, errors caused by time offset are reduced and data consistency is ensured. The optical sensor provides continuous blood glucose fluctuation information without being invasive, while the electrochemical sensor supplements the accuracy of the optical signal with high-precision data. This step ensures that the collection of blood glucose data can cover the dynamic changes of the time axis, while taking into account both non-invasiveness and high precision, providing high-quality data input for subsequent processing steps (such as data segmentation and fusion analysis).

[0021] By utilizing the characteristic advantages of optical blood glucose sensors and electrical blood glucose sensors, the technical effect of compensating for the possible defects of a single sensor is achieved, and the goal of reliable blood glucose detection is realized to provide data support for the analysis of blood glucose fluctuation trends of target users.

[0022] S2: dividing the optical blood glucose signal detection value sequence and the electrochemical blood glucose signal detection value sequence according to a preset division scale to obtain an optical blood glucose signal detection value subsequence set and an electrochemical blood glucose signal detection value subsequence set;

[0023] In one embodiment, the preset division scale is a time period pre-set by a person skilled in the art as the interval between two adjacent divisions, such as every 5 minutes or every 10 minutes. The optical blood glucose signal detection value sequence and the electrochemical blood glucose signal detection value sequence are divided according to the preset division scale to obtain the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set.

[0024] Each subsequence in the optical and electrochemical blood glucose signal detection value subsequence sets contains continuous data within a pre-defined time period, facilitating subsequent processing and analysis. This subsequence division provides data support for more refined analysis, facilitating the extraction of localized blood glucose trends, such as fluctuation amplitude and short-term fluctuations, thereby improving the accuracy of blood glucose signal processing.

[0025] S3: performing multimodal interactive fusion analysis on the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set to obtain a blood glucose fusion feature;

[0026] In one possible embodiment, optical blood glucose sensors and electrochemical blood glucose sensors each have different advantages and disadvantages. Optical blood glucose sensors have good continuous detection performance, but are susceptible to external influences and have low detection accuracy. Electrochemical blood glucose sensors, on the other hand, have high detection accuracy but poor continuous detection performance. Therefore, representative signal detection values are first screened for each subsequence within the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set, respectively, to obtain optical blood glucose signal screening value sequences and electrochemical blood glucose signal screening value sequences, thereby achieving data dimensionality reduction and improving signal processing efficiency. Feature extraction is then performed based on the screened data to determine the optical blood glucose screening features and electrochemical blood glucose screening features within a preset detection window, thereby achieving feature extraction of the blood glucose signal. Multimodal interactive fusion analysis is then performed on the optical blood glucose screening features and electrochemical blood glucose screening features. Through the interactive fusion analysis, the optical blood glucose screening features and electrochemical blood glucose screening features are enhanced, and the fused blood glucose features are obtained by averaging. This provides more reliable data support for blood glucose detection and improves the accuracy of blood glucose signal processing.

[0027] S4: calling the target user's historical blood glucose feature set to authenticate the blood glucose fusion feature and obtain an authentication result. If the authentication result is passed, the blood glucose fusion feature is used as the blood glucose signal processing result.

[0028] In one embodiment, the historical blood glucose feature set reflects the blood glucose changes of the target user over a historical period of time. These features typically include the volatility, trend, mutation point, and blood glucose concentration of the blood glucose level, reflecting the blood glucose change pattern of the target user in history. After obtaining the blood glucose fusion feature, the blood glucose fusion feature is authenticated by using the historical blood glucose feature set of the target user to determine whether the blood glucose fusion feature is reliable. If the authentication result is passed, the blood glucose fusion feature is used as the blood glucose signal processing result. Thus, the technical effect of improving the accuracy of blood glucose signal processing is achieved.

[0029] Further, such as Figure 2 As shown, multimodal interactive fusion analysis is performed on the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set to obtain a blood glucose fusion feature. In this embodiment of the application, step S3 further includes:

[0030] performing concentrated iterative screening of signal detection values within the subsequences of the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set, and sorting the screening results in chronological order of detection to obtain an optical blood glucose signal screening value sequence and an electrochemical blood glucose signal screening value sequence;

[0031] Using a blood glucose signal extraction network layer to extract blood glucose signal features from the optical blood glucose signal screening value sequence and the electrochemical blood glucose signal screening value sequence to obtain optical blood glucose screening features and electrochemical blood glucose screening features;

[0032] A multimodal interactive fusion analysis is performed on the optical blood glucose screening feature and the electrochemical blood glucose screening feature to determine a blood glucose fusion feature.

[0033] In one possible embodiment, for each subsequence of the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set, noise data or abnormal values are removed through centralized iterative screening, and the screening value in each subsequence that best represents the subsequence detection value situation is retained. Then, the screened screening values are rearranged in chronological order to generate an optical signal screening value sequence and an electrochemical signal screening value sequence, thereby ensuring data integrity and timeline consistency.

[0034] In order to improve the efficiency of blood glucose signal processing, a blood glucose signal extraction network layer is used to extract features from the optical blood glucose signal screening value sequence and the electrochemical blood glucose signal screening value sequence. The network layer can capture the time dependence, nonlinear relationship and deep dynamic characteristics of the signal, thereby generating the optical blood glucose screening features and electrochemical blood glucose screening features. The optical blood glucose screening features reflect the blood glucose characteristics of the target user in the preset detection window after the target user's blood glucose is tested with an optical blood glucose sensor, including blood glucose concentration characteristics, blood glucose change rate characteristics, blood glucose fluctuation amplitude characteristics and blood glucose trend characteristics. The electrochemical blood glucose screening features reflect the blood glucose characteristics of the target user in the preset detection window after the target user's blood glucose is tested with an electrochemical blood glucose sensor, including blood glucose concentration characteristics, blood glucose change rate characteristics, blood glucose fluctuation amplitude characteristics and blood glucose trend characteristics.

[0035] Furthermore, by combining the continuity of the optical signal with the accuracy of the electrochemical signal—that is, performing a multimodal interactive fusion analysis of the optical and electrochemical blood glucose screening features—a more representative fusion blood glucose feature is obtained that comprehensively reflects the user's blood glucose dynamics. This completes the entire process from data screening, feature extraction, to fusion analysis, achieving the technical effect of improving the quality and reliability of the fusion blood glucose feature.

[0036] Furthermore, the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set are respectively subjected to concentrated iterative screening of the signal detection values within the subsequences, and the screening results are respectively sorted according to the detection time sequence to obtain the optical blood glucose signal screening value sequence and the electrochemical blood glucose signal screening value sequence. In this embodiment of the application, step S3 further includes:

[0037] randomly extracting a first optical blood glucose signal detection value subsequence from the optical blood glucose signal detection value subsequence set;

[0038] Calculating the mean of the first optical blood glucose signal detection value subsequence to obtain a first optical blood glucose signal detection mean value;

[0039] Using the first optical blood glucose signal detection mean value as an initial screening value, constructing an initial neighborhood in the first optical blood glucose signal detection value subsequence according to a preset centralized screening step;

[0040] Calculating the average of the first optical blood glucose signal detection values in the initial neighborhood to obtain an initial neighborhood average;

[0041] Performing iterative screening authentication based on the difference between the initial neighborhood mean and the initial screening value, and if the iterative screening authentication result is passed, randomly extracting a first optical blood glucose signal detection value from the neighborhood edge of the initial neighborhood as the iterative screening value;

[0042] Iterating in the first optical blood glucose signal detection value subsequence based on the iterative screening value until the iterative screening authentication result is failure, and taking the neighborhood mean of the iterative neighborhood corresponding to the last iteration as the first optical signal screening value;

[0043] Similarly, the optical blood glucose signal detection value subsequence set is traversed to perform centralized screening of signal detection values within the subsequence to obtain Q optical blood glucose signal screening values, and the optical blood glucose signal detection value subsequences corresponding to the Q optical blood glucose signal screening values are arranged in order from front to back according to the detection time of the optical blood glucose signal detection value subsequences to obtain the optical blood glucose signal screening value sequence, where Q is the number of optical blood glucose signal detection value subsequences in the optical blood glucose signal detection value subsequence set;

[0044] The electrochemical blood glucose signal detection value subsequence is subjected to centralized screening of signal detection values within the subsequence to obtain the electrochemical blood glucose signal screening value sequence.

[0045] Furthermore, an iterative screening authentication is performed based on the difference between the initial neighborhood mean and the initial screening value. If the iterative screening authentication result is passed, a first optical blood glucose signal detection value is randomly extracted from the neighborhood edge of the initial neighborhood as the iterative screening value. In this embodiment of the application, step S3 further includes:

[0046] Calculate the difference between the initial neighborhood mean and the initial screening value, and determine whether the difference is greater than or equal to a preset difference. If so, the iterative screening authentication result is passed;

[0047] If not, the iterative screening authentication result is failure, and the initial neighborhood mean is used as the first optical signal screening value.

[0048] In one possible embodiment, a subsequence of optical blood glucose signal detection values is randomly selected from a set of optical blood glucose signal detection value subsequences as a first optical blood glucose signal detection value subsequence. The first optical blood glucose signal detection value subsequence is iteratively screened for signal detection values within the subsequence to remove noise and outliers, thereby selecting a representative first optical blood glucose signal detection value subsequence. The mean of the detection values within the first optical blood glucose signal detection value subsequence is traversally calculated to obtain a first optical blood glucose signal detection mean.

[0049] Furthermore, the first optical blood glucose signal detection mean is used as an initial screening value to construct an initial neighborhood. Preferably, the first optical blood glucose signal detection values in the first optical blood glucose signal detection value subsequence whose difference with the first optical blood glucose signal detection mean is within a preset centralized screening step are added to an initially empty set to obtain the initial neighborhood. The preset screening step is the amplitude of a single centralized iterative screening process preset by a person skilled in the art.

[0050] Then, the average of the first optical blood glucose signal detection values in the initial neighborhood is calculated to obtain an initial neighborhood average, wherein the initial neighborhood average reflects the average level of the optical blood glucose signal detection values in the initial neighborhood.

[0051] Preferably, the difference between the initial neighborhood mean and the initial screening value is calculated. The size of the difference reflects the degree of discreteness of the data in the initial neighborhood. The larger the difference, the more discrete the data in the initial neighborhood. The data that can reflect the general situation of the first optical blood glucose signal detection value subsequence is more likely to be outside the initial neighborhood. It is further determined whether the difference is greater than or equal to a preset difference (the minimum difference for centralized iterative screening is pre-set by those skilled in the art). If so, the iterative screening authentication result is passed, indicating that centralized iterative screening needs to be continued at this time; if not, the iterative screening authentication result is failed, indicating that centralized iterative screening should be stopped at this time. The data distribution in the initial neighborhood is relatively concentrated and has high representativeness. At this time, the initial neighborhood mean is used as the first optical signal screening value.

[0052] Preferably, if the iterative screening authentication result is passed, a first optical blood glucose signal detection value is randomly extracted from the neighborhood edge of the initial neighborhood as the iterative screening value. Then, the iterative screening value is used as the new neighborhood center to construct an iterative screening neighborhood. Then, based on the same principle as the iterative screening authentication based on the difference between the initial neighborhood mean and the initial screening value, it is calculated whether the difference between the iterative screening neighborhood mean and the iterative screening value is greater than or equal to the preset difference. If so, a first optical blood glucose signal detection value is randomly extracted from the edge of the iterative screening neighborhood to update the iterative screening value, and then the iteration is continued in the first optical blood glucose signal detection value subsequence according to the updated iterative screening value until the iterative screening authentication result is failed, the iteration is stopped, and the neighborhood mean of the iterative neighborhood corresponding to the last iteration is used as the first optical signal screening value.

[0053] Similarly, based on the same principle, a centralized signal detection value screening is performed on each optical blood glucose signal detection value subsequence set in the optical blood glucose signal detection value subsequence set to obtain Q optical blood glucose signal screening values. The optical blood glucose signal detection value subsequences corresponding to the Q optical blood glucose signal screening values are then arranged in descending order according to the detection time of the optical blood glucose signal detection value subsequences to obtain the optical blood glucose signal screening value sequence, where Q is the number of optical blood glucose signal detection value subsequences in the optical blood glucose signal detection value subsequence set. Furthermore, based on the same principle as for obtaining the optical blood glucose signal screening value sequence, a centralized signal detection value screening is performed on the electrochemical blood glucose signal detection value subsequence to obtain the electrochemical blood glucose signal screening value sequence.

[0054] Through centralized iterative screening, noise or outliers in optical and electrochemical signals can be effectively removed, ensuring the quality and representativeness of the screened values. The refinement of the screening process is controlled by the neighborhood mean and screening stride, making the screened signal sequence smoother and reducing errors caused by data fluctuations. Furthermore, by using the screening certification process to verify the rationality of the signal detection values through step-by-step iteration, it is possible to stably extract statistically significant representative values and improve the reliability of the signal analysis results. This has achieved the technical effect of laying the foundation for the accuracy of blood glucose signal processing.

[0055] Furthermore, step S3 of the embodiment of the present application further includes:

[0056] A plurality of sample optical blood glucose signal screening value sequences and corresponding plurality of sample optical blood glucose screening features are obtained as optical training data, and a framework based on a convolutional neural network is supervised and trained using the optical training data. During the training, a one-to-one mapping relationship between the optical blood glucose signal screening value sequences and the sample optical blood glucose screening features is learned until the training converges, thereby obtaining a trained optical blood glucose signal extraction branch;

[0057] A plurality of sample electrochemical blood glucose signal screening value sequences and corresponding plurality of sample electrochemical blood glucose screening features are obtained as electrochemical training data. The optical training data is used to perform supervised training on a framework built based on a convolutional neural network. During the training, a one-to-one mapping relationship between the electrochemical blood glucose signal screening value sequences and the sample electrochemical blood glucose screening features is learned until the training converges, thereby obtaining a trained electrochemical blood glucose signal extraction branch.

[0058] The optical blood glucose signal extraction branch and the electrochemical blood glucose signal extraction branch are connected in parallel to obtain the blood glucose signal extraction network layer.

[0059] In one possible embodiment, the multiple sample optical blood glucose signal screening value sequences are sequences extracted from multiple sample optical blood glucose signal detection value subsequences after screening, and are used to train and test a neural network model. The multiple sample optical blood glucose screening features refer to feature data corresponding to the sample optical blood glucose signal screening value sequences. The multiple sample optical blood glucose signal screening value sequences and the corresponding multiple sample optical blood glucose screening features are used as optical training data.

[0060] Furthermore, a convolutional neural network (CNN) framework is used to build the model. CNN automatically learns and extracts spatial and temporal features from data through a multi-layer structure including convolutional layers, pooling layers, and fully connected layers. The input of the network is a sequence of optical blood glucose signal screening values, and the output is a blood glucose screening feature corresponding to each sequence. Preferably, the optical blood glucose screening features of the multiple samples are labeled. During the training process, the network is trained by calculating the difference (i.e., error) between the network output and the true label based on the input optical blood glucose signal screening value sequence (training data) and its corresponding optical blood glucose screening feature (label data). The error is backpropagated to each level of the network through the backpropagation algorithm, and the network parameters (such as convolution kernel weights, etc.) are adjusted to minimize the error. In each iteration, the convolutional neural network updates the model parameters through an optimization algorithm (such as gradient descent) to gradually reduce the prediction error. After each round of training, the network gradually adjusts its own weights and biases so that the mapping relationship between the input optical blood glucose signal screening value sequence and the output optical blood glucose screening feature becomes increasingly accurate. After multiple rounds of training, the model error stabilizes, reaching a minimum or convergence state, indicating that the network has learned the optimal mapping from the optical blood glucose signal screening value sequence to the blood glucose screening features. Once training is complete and converged, the resulting model is the trained optical blood glucose signal extraction branch. The optical blood glucose signal extraction branch automatically extracts key features from the optical blood glucose signal sequence and maps them one-to-one with the blood glucose screening features.

[0061] Based on the same construction principle as the optical blood glucose signal extraction branch, multiple sample electrochemical blood glucose signal screening value sequences and corresponding multiple sample electrochemical blood glucose screening features are used as electrochemical training data. The optical training data is used to supervise the training of the framework built based on the convolutional neural network. During training, the one-to-one mapping relationship between the electrochemical blood glucose signal screening value sequences and the sample electrochemical blood glucose screening features is learned until the training converges, obtaining a trained electrochemical blood glucose signal extraction branch. The electrochemical blood glucose signal extraction branch can automatically extract key features from the electrochemical blood glucose signal sequence and map them one-to-one with the blood glucose screening features.

[0062] Furthermore, the trained optical blood glucose signal extraction branch and the electrochemical blood glucose signal extraction branch are combined to form a multi-input network layer (blood glucose signal extraction network layer), which can simultaneously process two different types of signals and extract their blood glucose-related features.

[0063] Furthermore, a multimodal interactive fusion analysis is performed on the optical blood glucose screening feature and the electrochemical blood glucose screening feature to determine a blood glucose fusion feature. Step S3 of the embodiment of the present application further includes:

[0064] Calculating the similarity between the optical blood glucose screening feature and the electrochemical blood glucose screening feature to obtain a screening feature similarity set;

[0065] Normalizing the screened feature similarity set and adding the processing result to the initially empty matrix to obtain a multimodal interaction fusion matrix;

[0066] performing convolution calculation on the optical blood glucose screening feature and the multimodal interaction fusion matrix to obtain an optical blood glucose fusion feature;

[0067] performing convolution calculation on the electrochemical blood glucose screening feature and the multimodal interactive fusion matrix to obtain an electrochemical blood glucose fusion feature;

[0068] The average of the optical blood glucose fusion feature and the electrochemical blood glucose fusion feature is calculated to obtain the blood glucose fusion feature.

[0069] Furthermore, step S3 of the embodiment of the present application further includes:

[0070] Obtain a normalization function, wherein the normalization function is:

[0071]

[0072] Among them, Mix[lim(x i ,y i )] is the normalized value corresponding to the i-th screening feature similarity in the screening feature similarity set, e is the base of the natural logarithm, n is the total number of screening feature similarities in the screening feature similarity set, lim(x i ,y i ) is the i-th screening feature similarity in the screening feature similarity set;

[0073] The normalization function is used to perform normalization processing on the screening feature similarity set to obtain the processing result.

[0074] In one possible embodiment, in order to perform multimodal interactive fusion analysis on the optical blood glucose screening feature and the electrochemical blood glucose screening feature, thereby enhancing the optical blood glucose screening feature and the electrochemical blood glucose screening feature. First, the similarity of the corresponding features in the optical blood glucose screening feature and the electrochemical blood glucose screening feature is calculated using the cosine calculation formula to obtain the screening feature similarity set. The screening feature similarity set reflects the degree of similarity between the optical blood glucose screening feature and the electrochemical blood glucose screening feature.

[0075] Then, the screening feature similarity set is normalized using a normalization function to eliminate dimensional differences, and the result is added to the initially empty matrix to obtain the multimodal interaction fusion matrix. The multimodal interaction fusion matrix reflects the interaction relationship between the optical blood glucose screening feature and the multimodal interaction fusion matrix.

[0076] A convolution operation is performed on the optical blood glucose screening feature and the multimodal interaction fusion matrix to extract high-order interaction features between the two and generate an optical blood glucose fusion feature. Preferably, a sample optical blood glucose screening feature set, a sample multimodal interaction fusion matrix, and a sample optical blood glucose fusion feature are obtained as training data, and the training data is used to perform supervised training on a framework constructed based on a convolutional neural network until the training converges, thereby obtaining a trained optical convolutional network layer. The optical blood glucose screening feature and the multimodal interaction fusion matrix are convolutionally analyzed using the optical convolutional network layer to obtain the optical blood glucose fusion feature.

[0077] Similarly, the electrochemical blood glucose screening feature and the multimodal interaction fusion matrix are convolved to extract their interaction features and generate electrochemical blood glucose fusion features. Preferably, the sample electrochemical blood glucose screening feature set, the sample multimodal interaction fusion matrix and the sample electrochemical blood glucose fusion feature are obtained as training data, and the training data are used to perform supervised training on the framework constructed based on the convolutional neural network until the training converges to obtain the trained electrochemical convolutional network layer. The electrochemical convolutional network layer is used to perform convolution analysis on the electrochemical blood glucose screening feature and the multimodal interaction fusion matrix to obtain the electrochemical blood glucose fusion feature. Through convolution calculation, deep interaction relationships are extracted from the input features and the interaction matrix, providing a more accurate and comprehensive feature expression for blood glucose detection and prediction.

[0078] Furthermore, the target user's historical blood glucose feature set is called to authenticate the blood glucose fusion feature to obtain an authentication result. If the authentication result is passed, the blood glucose fusion feature is used as the blood glucose signal processing result. Step S4 of the embodiment of the present application further includes:

[0079] Calculating similarities between the blood glucose fusion feature and the historical blood glucose feature set to obtain a historical similarity set;

[0080] The historical similarity set is authenticated using a preset historical similarity threshold. When the ratio of the number of historical similarities in the historical similarity set that are smaller than the preset historical similarity threshold to the total number of historical similarities in the historical similarity set is smaller than a preset ratio, the authentication result is passed.

[0081] In one possible embodiment, the historical blood glucose feature set is accumulated from the target user's past blood glucose test data, and includes blood glucose features at several historical time points. Each historical blood glucose feature is composed of data from multiple dimensions, representing the user's blood glucose status at a certain moment. The blood glucose fusion feature is the result of processing by fusing optical and electrochemical blood glucose signals, representing the current blood glucose status of the target user. This feature set is usually also multidimensional data.

[0082] The Euclidean distance is used to calculate the distance between the fusion blood glucose feature and each historical blood glucose feature in the historical blood glucose feature set. The smaller the distance, the higher the similarity. The inverse of the calculated result is used as the historical similarity set. The historical similarity set reflects the degree of similarity between the fusion blood glucose feature and the target user's historical blood glucose status.

[0083] Furthermore, a preset historical similarity threshold (e.g., 0.6) is set by those skilled in the art to determine whether the current fusion glucose signature is sufficiently similar to the historical glucose signature. A similarity above the threshold indicates that the current glucose state is very similar to the historical glucose state. A similarity below the threshold indicates that the glucose state has changed significantly, which may indicate an error in glucose signal processing.

[0084] When the ratio of the number of similarities in the historical similarity set that is less than the preset historical similarity threshold to the total number of historical similarities in the historical similarity set is less than a preset ratio, the authentication result is passed. Preferably, the ratio calculation formula can be: ratio = number of similarities in the historical similarity set that is less than the preset historical similarity threshold / total number of historical similarity sets. The ratio represents the degree of match between the current blood glucose fusion feature and the historical blood glucose features.

[0085] If the ratio is less than a preset value (e.g., 0.6), the authentication result is passed, indicating that the current blood glucose fusion signature matches the historical blood glucose signature sufficiently well and meets the processing requirements. If the ratio is greater than the preset value, the authentication fails, indicating that the current blood glucose fusion signature differs significantly from the historical blood glucose signature and requires further processing.

[0086] In summary, the embodiments of the present application have at least the following technical effects:

[0087] 1. This application utilizes dual detection of optical blood glucose sensors and electrochemical blood glucose sensors, which can complement each other and reduce the errors that may be caused by a single sensor. By performing multimodal interactive fusion analysis, useful information from multi-source data can be better extracted, achieving the technical effect of further improving the accuracy of blood glucose signal processing.

[0088] 2. This application authenticates the blood glucose fusion feature by invoking the target user's historical blood glucose feature set, ensuring consistency between the current blood glucose signal and historical data. This process effectively avoids erroneous analysis caused by unexpected events or abnormal fluctuations, ensuring accurate reflection of blood glucose signals and making blood glucose processing results more reliable.

[0089] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0091] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A blood glucose signal processing method for blood glucose detection, characterized in that: The method comprises: Using an optical blood glucose sensor and an electrochemical blood glucose sensor to synchronously and continuously detect the target user's blood glucose level in a predicted detection window, obtaining a sequence of optical blood glucose signal detection values and a sequence of electrochemical blood glucose signal detection values; Dividing the optical blood glucose signal detection value sequence and the electrochemical blood glucose signal detection value sequence according to a preset division scale to obtain an optical blood glucose signal detection value subsequence set and an electrochemical blood glucose signal detection value subsequence set; performing a multimodal interactive fusion analysis on the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set to obtain a blood glucose fusion feature; Calling the target user's historical blood glucose feature set to authenticate the blood glucose fusion feature and obtain an authentication result. If the authentication result is passed, the blood glucose fusion feature is used as the blood glucose signal processing result; The multimodal interactive fusion analysis is performed on the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set to obtain the blood glucose fusion feature, including: performing concentrated iterative screening of signal detection values within the subsequences of the optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set, and sorting the screening results in chronological order of detection to obtain an optical blood glucose signal screening value sequence and an electrochemical blood glucose signal screening value sequence; Using a blood glucose signal extraction network layer to extract blood glucose signal features from the optical blood glucose signal screening value sequence and the electrochemical blood glucose signal screening value sequence to obtain optical blood glucose screening features and electrochemical blood glucose screening features; performing a multimodal interactive fusion analysis on the optical blood glucose screening feature and the electrochemical blood glucose screening feature to determine a blood glucose fusion feature; The optical blood glucose signal detection value subsequence set and the electrochemical blood glucose signal detection value subsequence set are respectively subjected to concentrated iterative screening of signal detection values within the subsequence, and the screening results are respectively sorted in order of detection time to obtain the optical blood glucose signal screening value sequence and the electrochemical blood glucose signal screening value sequence, including: randomly extracting a first optical blood glucose signal detection value subsequence from the optical blood glucose signal detection value subsequence set; Calculating the mean of the first optical blood glucose signal detection value subsequence to obtain a first optical blood glucose signal detection mean value; Using the first optical blood glucose signal detection mean value as an initial screening value, constructing an initial neighborhood in the first optical blood glucose signal detection value subsequence according to a preset centralized screening step; Calculating the average of the first optical blood glucose signal detection values in the initial neighborhood to obtain an initial neighborhood average; Performing iterative screening authentication based on the difference between the initial neighborhood mean and the initial screening value, and if the iterative screening authentication result is passed, randomly extracting a first optical blood glucose signal detection value from the neighborhood edge of the initial neighborhood as the iterative screening value; Iterating in the first optical blood glucose signal detection value subsequence based on the iterative screening value until the iterative screening authentication result is failure, and taking the neighborhood mean of the iterative neighborhood corresponding to the last iteration as the first optical signal screening value; Similarly, the optical blood glucose signal detection value subsequence set is traversed to perform centralized screening of signal detection values within the subsequence to obtain Q optical blood glucose signal screening values, and the optical blood glucose signal detection value subsequences corresponding to the Q optical blood glucose signal screening values are arranged in order from front to back according to the detection time of the optical blood glucose signal detection value subsequences to obtain the optical blood glucose signal screening value sequence, where Q is the number of optical blood glucose signal detection value subsequences in the optical blood glucose signal detection value subsequence set; The electrochemical blood glucose signal detection value subsequence is subjected to centralized screening of signal detection values within the subsequence to obtain the electrochemical blood glucose signal screening value sequence.

2. A blood glucose signal processing method for blood glucose detection according to claim 1, characterized in that: Performing iterative screening authentication based on the difference between the initial neighborhood mean and the initial screening value, and if the iterative screening authentication result is passed, randomly extracting a first optical blood glucose signal detection value from the neighborhood edge of the initial neighborhood as the iterative screening value, including: Calculate the difference between the initial neighborhood mean and the initial screening value, and determine whether the difference is greater than or equal to a preset difference. If so, the iterative screening authentication result is passed; If not, the iterative screening authentication result is failure, and the initial neighborhood mean is used as the first optical signal screening value.

3. The blood glucose signal processing method for blood glucose detection according to claim 1, wherein: include: A plurality of sample optical blood glucose signal screening value sequences and corresponding plurality of sample optical blood glucose screening features are obtained as optical training data, and a framework based on a convolutional neural network is supervised and trained using the optical training data. During the training, a one-to-one mapping relationship between the optical blood glucose signal screening value sequences and the sample optical blood glucose screening features is learned until the training converges, thereby obtaining a trained optical blood glucose signal extraction branch; Acquire multiple sample electrochemical blood glucose signal screening value sequences and corresponding multiple sample electrochemical blood glucose screening features as electrochemical training data, use the electrochemical training data to perform supervised training on a framework built based on a convolutional neural network, and learn a one-to-one mapping relationship between the electrochemical blood glucose signal screening value sequences and the sample electrochemical blood glucose screening features during training until the training converges, thereby obtaining a trained electrochemical blood glucose signal extraction branch; The optical blood glucose signal extraction branch and the electrochemical blood glucose signal extraction branch are connected in parallel to obtain the blood glucose signal extraction network layer.

4. A blood glucose signal processing method for blood glucose detection according to claim 1, characterized in that: Performing a multimodal interactive fusion analysis on the optical blood glucose screening feature and the electrochemical blood glucose screening feature to determine a blood glucose fusion feature includes: Calculating the similarity between the optical blood glucose screening feature and the electrochemical blood glucose screening feature to obtain a screening feature similarity set; Normalizing the screened feature similarity set and adding the processing result to the initially empty matrix to obtain a multimodal interaction fusion matrix; performing convolution calculation on the optical blood glucose screening feature and the multimodal interaction fusion matrix to obtain an optical blood glucose fusion feature; performing convolution calculation on the electrochemical blood glucose screening feature and the multimodal interactive fusion matrix to obtain an electrochemical blood glucose fusion feature; The average of the optical blood glucose fusion feature and the electrochemical blood glucose fusion feature is calculated to obtain the blood glucose fusion feature.

5. A blood glucose signal processing method for blood glucose detection according to claim 4, characterized in that: include: Obtain a normalization function, wherein the normalization function is: ； in, is the normalized value corresponding to the similarity of the i-th screening feature in the screening feature similarity set, is the base of natural logarithms, is the total number of filtered feature similarities in the filtered feature similarity set, is the i-th screening feature similarity in the screening feature similarity set; The normalization function is used to perform normalization processing on the screening feature similarity set to obtain the processing result.

6. A blood glucose signal processing method for blood glucose detection according to claim 1, characterized in that: The target user's historical blood glucose feature set is called to authenticate the blood glucose fusion feature to obtain an authentication result. If the authentication result is passed, the blood glucose fusion feature is used as a blood glucose signal processing result, including: Calculating similarities between the blood glucose fusion feature and the historical blood glucose feature set to obtain a historical similarity set; The historical similarity set is authenticated using a preset historical similarity threshold. When the ratio of the number of historical similarities in the historical similarity set that are smaller than the preset historical similarity threshold to the total number of historical similarities in the historical similarity set is smaller than a preset ratio, the authentication result is passed.

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