Non-invasive blood glucose monitor correction method based on joint optimization

Abstract

The invention relates to the technical field of data processing, in particular to a non-invasive blood glucose monitor correction method based on joint optimization, which comprises the following steps: segmenting a blood glucose monitoring data sequence according to periods, and acquiring all wavebands of a blood glucose monitoring data sequence set in each period; the fluctuation difference quantity of the blood glucose monitoring data sequence at each collection moment in each period is obtained; obtaining the difference degree of the blood glucose monitoring data sequence at each collection moment in each period; obtaining an abnormal degree at each collection moment in each period; acquiring the correction amplitude of the blood glucose concentration at each collection moment in each period; acquiring an optimized correction factor of the noninvasive blood glucose monitor according to the correction amplitude of the blood glucose concentration at all the acquisition moments in all the periods in all the acquisition time periods; and performing optimization correction on the noninvasive blood glucose monitor according to the optimization correction factor. According to the invention, the measurement data of the noninvasive blood glucose monitor is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a calibration method for a non-invasive blood glucose monitor based on joint optimization. Background Art

[0002] Non-invasive blood glucose monitors use optical, thermal, electrical or other physical principles to assess blood glucose levels in real time without puncturing the skin to collect blood samples; the measurement accuracy of non-invasive blood glucose monitors is affected by individual physiological differences, changes in the external environment and device stability. Since changes in blood glucose concentration may be very subtle, the measurement data often contain noise and interference, and effective data processing technology is required to extract accurate data; the working principle of non-invasive blood glucose monitors is to use multi-source data to measure blood glucose levels, and then optimize and correct the non-invasive blood glucose monitor by performing anomaly analysis on the multi-source data; the conventional method of correcting data is to simply denoise the measurement data of the non-invasive blood glucose monitor, without considering correction based on the working principle of the non-invasive blood glucose monitor, which results in the optimized and corrected measurement values ​​being inaccurate. Summary of the invention

[0003] In order to solve the above problems, the present invention provides a calibration method of a non-invasive blood glucose monitor based on joint optimization, the method comprising: Obtaining a set of blood glucose monitoring data sequences within a certain collection time period; By analyzing the noise distribution in the blood glucose monitoring data sequence, all the cycles of the blood glucose monitoring data sequence set in each acquisition time period are obtained; the blood glucose monitoring data sequence is segmented according to the cycle, and all the bands of the blood glucose monitoring data sequence set in each cycle are obtained; according to the difference between the corresponding band of the blood glucose monitoring data sequence at each acquisition moment and other bands, the fluctuation difference amount of the blood glucose monitoring data sequence at each acquisition moment in each cycle is obtained; According to the difference in the fluctuation difference of the blood glucose monitoring data sequence between each collection moment and other collection moments, the difference degree of the blood glucose monitoring data sequence at each collection moment in each cycle is obtained; according to the difference in the difference degree between each collection time period and the adjacent collection time periods, the abnormality degree at each collection moment in each cycle is obtained; according to the difference in the abnormality degree between each collection moment and the surrounding collection moments, the correction amplitude of the blood glucose concentration at each collection moment in each cycle is obtained; According to the correction amplitude of blood glucose concentration at all sampling moments in all cycles in all sampling time periods, an optimized correction factor of the non-invasive blood glucose monitor is obtained; and the non-invasive blood glucose monitor is optimized and corrected according to the optimized correction factor.

[0004] Preferably, the method of obtaining all cycles of the blood glucose monitoring data sequence set within each acquisition time period by analyzing the noise distribution in the blood glucose monitoring data sequence includes: Get the Fluctuation curve of blood sugar monitoring data within a collection period; For The blood glucose monitoring data fluctuation curve within the acquisition time period is Fourier transformed, and the inverse of the frequency with the largest amplitude in the spectrum graph is taken as the One cycle of the blood glucose monitoring data fluctuation curve within the collection time period is recorded as the target cycle; According to the maximum amplitude in the spectrum and the frequency of the target cycle, the obtained curve is recorded as the fluctuation curve of the target cycle; The difference curve between the fluctuation curve of the blood glucose monitoring data in the acquisition time period and the fluctuation curve of the target period is recorded as the curve to be analyzed; the curve to be analyzed is decomposed by using the EDM decomposition algorithm to obtain several IMF components and residual terms; the possibility of each IMF component belonging to high-frequency noise is obtained; The IMF component with the highest probability of being high-frequency noise is recorded as the noise component, and the other IMF components and the residual term are added to obtain the curve to be analyzed after the noise is removed; the curve to be analyzed after the noise is removed is subjected to Fourier transform, and the inverse of the frequency corresponding to each peak in the spectrum is taken as the first One cycle of the blood glucose monitoring data fluctuation curve within a collection time period.

[0005] Preferably, the obtaining The blood glucose monitoring data fluctuation curve within a collection time period includes the following specific methods: Construct a four-dimensional space based on the acquisition time, skin reflected light intensity data, skin electrical impedance data, and skin temperature data; The blood glucose monitoring data sequence set within the collection time period is input into the four-dimensional space to obtain the Several blood glucose monitoring data points within a collection time period; in four-dimensional space, the least squares method is used to transform the first The curve fitting is performed for all the blood glucose monitoring data points at the collection time in the collection time period to obtain the Fluctuation curve of blood glucose monitoring data within a collection period.

[0006] Preferably, the method of obtaining the possibility that each IMF component belongs to high-frequency noise includes: For any IMF component, a frequency spectrum of the IMF component is obtained by Fourier transform; a frequency corresponding to each peak in the frequency spectrum is obtained by peak detection method and recorded as peak frequency; The cumulative sum of the absolute values ​​of the differences between the mean of all peak frequencies of the IMF component and the mean of all peak frequencies of all other IMF components is taken as the possibility that the IMF component belongs to high-frequency noise.

[0007] Preferably, the method of obtaining the fluctuation difference amount of the blood glucose monitoring data sequence at each collection moment in each cycle according to the difference between the corresponding band of the blood glucose monitoring data sequence at each collection moment and other bands includes the following specific methods: In the In the blood glucose monitoring data fluctuation curve within the collection time period, The band corresponding to the blood glucose monitoring data point at each collection moment is recorded as the target band; all bands except the target band are recorded as reference bands; According to The similarity between the blood glucose monitoring data sequence at the collection time and the blood glucose monitoring data sequence at other collection times is obtained. All similar data sequences at the time of collection; The first The position of the blood glucose monitoring data point in the target band at the time of collection is recorded as the target position; The first The first The first dimension data and The first The absolute value of the difference between the two dimensional data is recorded as the target band and the The reference bands The data difference value of the first dimension data; the target band is The DTW distance between the reference bands is recorded as the DTW distance between the target band and the The sequence difference value between the reference bands; the target band and the The cumulative sum of the data difference values ​​of all dimensional data between the reference bands is recorded as the target band and the The overall data difference value between the reference bands; the product of the overall data difference value and the sequence difference value is recorded as the target band and the The corrected difference value between the target band and all reference bands is recorded as the cumulative sum of the corrected difference values ​​between the target band and all reference bands. The fluctuation difference factor of the blood glucose monitoring data sequence at the collection time is The ratio between the number of all similar data sequences at the collection time is taken as the The fluctuation difference of the blood glucose monitoring data sequence at each collection moment.

[0008] Preferably, the obtaining All similar data sequences at the collection time, including the specific methods: Preset a similarity threshold parameter , The blood glucose monitoring data sequence at the collection time is The cosine similarity between the blood glucose monitoring data sequences at the target position in the reference band is greater than or equal to the similarity threshold parameter , will The blood glucose monitoring data sequence at the target position in the reference band is used as the first Similar data sequences at the same time.

[0009] Preferably, the method of obtaining the difference of the blood glucose monitoring data sequence at each collection moment in each cycle according to the difference of the fluctuation difference of the blood glucose monitoring data sequence between each collection moment and other collection moments includes the following specific methods: In the During the collection period, The sum of the absolute values ​​of the differences between the fluctuation difference of the blood sugar monitoring data sequence at the first collection time and the fluctuation difference of the blood sugar monitoring data sequence at other collection times is taken as the first The difference of blood glucose monitoring data series at each collection moment.

[0010] Preferably, the method of obtaining the abnormality degree at each collection moment in each cycle according to the difference between each collection time period and the adjacent collection time period includes the following specific methods: Preset a time range neighborhood parameter , will Before the collection period The collection period and The collection time periods are all taken as A reference collection time period for a collection time period; The first The first The first The difference between the blood glucose monitoring data sequence at the left adjacent collection time of the collection time and the The first The first The absolute value of the difference between the difference of the blood glucose monitoring data sequence at the right adjacent collection time of the collection time is recorded as The adjacent abnormal difference of the reference acquisition time period; The cumulative sum of the adjacent abnormal differences of all reference collection time periods in the collection time period is recorded as The anomaly correction factor of the first acquisition period is The product of the differences between the blood glucose monitoring data sequences at the collection time is taken as the The abnormality degree of the blood glucose monitoring data sequence at each collection moment.

[0011] Preferably, the method of obtaining the correction amplitude of the blood glucose concentration at each collection moment in each cycle according to the difference in the degree of abnormality between each collection moment and the surrounding collection moments includes the following specific methods: Preset a collection time neighborhood parameter , in During the collection period, before the collection time The collection time and The time sequence composed of the collection moments is recorded as Neighborhood reference time sequence of the acquisition time; The first The mean of the patient blood glucose concentration data at all collection moments in the neighborhood reference time sequence of the collection moment is recorded as the first mean; Within the collection time period The absolute value of the difference between the patient's blood glucose concentration data at the time of collection and the first mean is recorded as the correction factor; The normalized value of the product of the abnormal degree of the blood glucose monitoring data sequence at the collection time and the correction factor is taken as the The correction range of blood glucose concentration at each collection time.

[0012] Preferably, the method of obtaining the optimized correction factor of the non-invasive blood glucose monitor according to the correction amplitude of the blood glucose concentration at all acquisition moments in all acquisition time periods in all cycles includes the following specific methods: The first Within the collection time period The patient's blood glucose concentration data at the time of collection is Within the collection time period The difference between the mean values ​​of the patient blood glucose concentration data at all collection moments in the neighborhood reference time sequence of the collection moment is recorded as the first difference; The first difference and the The product of the correction amplitude of blood glucose concentration at the time of collection is taken as the Correction value at each acquisition moment; If the first difference is greater than or equal to 0, The difference between the patient's blood glucose concentration data at the time of collection and the corrected value is taken as the Corrected patient blood glucose concentration data at each collection moment; If the first difference is less than 0, The sum of the patient's blood glucose concentration data at the time of collection and the correction value is taken as the Corrected patient blood glucose concentration data at each collection moment; The ratio between the mean of the corrected patient blood glucose concentration data at all collection moments in all cycles of all collection time periods and the mean of the patient blood glucose concentration data at all collection moments in all cycles of all collection time periods is used as the optimization correction factor of the non-invasive blood glucose monitor.

[0013] The beneficial effects of the technical solution of the present invention are as follows: the present invention obtains the difference degree of the blood glucose monitoring data sequence at each collection moment in each cycle according to the difference in the fluctuation difference amount of the blood glucose monitoring data sequence between each collection moment and other collection moments; According to the difference between the difference degree between each collection time period and the adjacent collection time periods, the abnormality degree at each collection moment in each cycle is obtained; according to the difference in the abnormality degree between each collection moment and the surrounding collection moments, the correction amplitude of the blood glucose concentration at each collection moment in each cycle is obtained; according to the correction amplitude of the blood glucose concentration at all collection moments in all cycles of all collection time periods, the optimized correction factor of the non-invasive blood glucose monitor is obtained; according to the optimized correction factor, the non-invasive blood glucose monitor is optimized and corrected, so as to achieve the optimization and correction of the non-invasive blood glucose monitor through the working principle of the non-invasive blood glucose monitor, so that the measurement data of the non-invasive blood glucose monitor is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A flowchart of the steps of the calibration method of the non-invasive blood glucose monitor based on joint optimization of the present invention; Figure 2 The present invention is a characteristic relationship flow chart of the calibration method of the non-invasive blood glucose monitor based on joint optimization. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the calibration method of the non-invasive blood glucose monitor based on joint optimization proposed by the present invention, its specific implementation method, structure, characteristics and effects are described in detail as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0017] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0018] The specific scheme of the calibration method of the non-invasive blood glucose monitor based on joint optimization provided by the present invention is described in detail below with reference to the accompanying drawings.

[0019] See also Figure 1 , which shows a flowchart of a calibration method for a non-invasive blood glucose monitor based on joint optimization provided by an embodiment of the present invention, the method comprising the following steps: Step S001: Acquire a set of blood glucose monitoring data sequences within a certain collection time period.

[0020] It should be noted that non-invasive blood glucose monitors use different non-invasive technologies to measure blood glucose levels. Their working principles are: estimating blood glucose levels by emitting near-infrared light to the skin and analyzing changes in the intensity of the reflected light; estimating blood glucose levels by measuring changes in the electrical impedance of the skin and tissues; estimating blood glucose levels by measuring changes in skin temperature.

[0021] Specifically, it is necessary to first collect a set of blood glucose monitoring data sequences within a certain collection time period. The specific process is as follows: An infrared light source is used to emit near-infrared light onto the skin, and an optical receiver is installed near the surface of the patient's skin to receive the near-infrared light reflected from the skin, and the intensity data of the received reflected light is measured by the sensor; two electrodes are placed on the patient's skin, one electrode is used to apply a low-frequency alternating current, and the other electrode is used to measure the voltage, and the electrical impedance data is calculated according to Ohm's law; the patient's skin temperature data is measured by an infrared thermometer; the patient's blood glucose concentration data is measured by a non-invasive blood glucose monitor; Take every 30 minutes as a collection time period, and collect data for a total of 5 hours; for any collection time period, take every 1 minute as a sampling moment, and collect the patient's skin reflected light intensity data, skin electrical impedance data, and skin temperature data of three dimensional data types in turn each time, for a total of 30 minutes; the patient's skin reflected light intensity data, skin electrical impedance data, and skin temperature data of three dimensional data at each sampling moment are used as the blood glucose monitoring data sequence set within the collection time period.

[0022] So far, a set of blood glucose monitoring data sequences within several collection time periods is obtained through the above method.

[0023] Step S002: By analyzing the noise distribution in the blood glucose monitoring data sequence, all cycles of the blood glucose monitoring data sequence set within each acquisition time period are obtained; the blood glucose monitoring data sequence is segmented according to the cycle, and all bands of the blood glucose monitoring data sequence set in each cycle are obtained; according to the difference between the corresponding band of the blood glucose monitoring data sequence at each acquisition moment and other bands, the fluctuation difference amount of the blood glucose monitoring data sequence at each acquisition moment in each cycle is obtained.

[0024] 1. Obtain all cycles of the blood glucose monitoring data sequence set within each collection time period.

[0025] It should be noted that since the intervals between collection times within the collection time period are constant and the patient's heartbeat, breathing rhythm and other physiological processes are all periodic changes, under normal circumstances, the data fluctuations of the blood glucose monitoring data within each collection time period show periodic characteristics; since the collected blood glucose monitoring data may contain high-frequency noise, resulting in the inability to accurately obtain other cycles, it is necessary to remove the high-frequency noise to obtain more accurate blood glucose monitoring data.

[0026] Preferably, in some implementations of the embodiments of the present invention, by analyzing the noise distribution in the blood glucose monitoring data sequence, the specific method for obtaining all cycles of the blood glucose monitoring data sequence set in each acquisition time period is: Get the Fluctuation curve of blood sugar monitoring data within a collection period; For The blood glucose monitoring data fluctuation curve within the acquisition time period is Fourier transformed, and the inverse of the frequency with the largest amplitude in the spectrum graph is taken as the One cycle of the blood glucose monitoring data fluctuation curve within the collection time period is recorded as the target cycle; According to the maximum amplitude in the spectrum diagram and the frequency of the target cycle, the obtained curve is recorded as the fluctuation curve of the target cycle; The difference curve between the fluctuation curve of the blood glucose monitoring data in the acquisition time period and the fluctuation curve of the target period is recorded as the curve to be analyzed; the curve to be analyzed is decomposed by using the EDM decomposition algorithm to obtain several IMF components and residual terms; the possibility of each IMF component belonging to high-frequency noise is obtained; The IMF component with the highest probability of being high-frequency noise is recorded as the noise component, and the other IMF components and the residual term are added to obtain the curve to be analyzed after the noise is removed; the curve to be analyzed after the noise is removed is subjected to Fourier transform, and the inverse of the frequency corresponding to each peak in the spectrum is taken as the first One cycle of the blood glucose monitoring data fluctuation curve within a collection time period.

[0027] It should be noted that high-frequency noise only appears in part of the original signal, unlike periodic signals that exist in the entire original signal, and the EDM decomposition algorithm can decompose the high-frequency noise. Therefore, the high-frequency noise signal can be determined by analyzing all frequency distributions of different IMF components in the spectrum diagram; the EDM decomposition algorithm is a prior art and will not be described in detail in this embodiment.

[0028] Preferably, in some implementations of the embodiments of the present invention, obtaining the The specific method of the blood sugar monitoring data fluctuation curve within a collection time period is: Construct a four-dimensional space based on the acquisition time, skin reflected light intensity data, skin electrical impedance data, and skin temperature data; The blood glucose monitoring data sequence set within the collection time period is input into the four-dimensional space to obtain the Several blood glucose monitoring data points within a collection time period; in four-dimensional space, the least squares method is used to transform the first The curve fitting is performed for all the blood glucose monitoring data points at the collection time in the collection time period to obtain the Fluctuation curve of blood glucose monitoring data within a collection period.

[0029] Among them, the least square method is an existing technology and will not be described in detail in this embodiment.

[0030] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining the possibility that each IMF component belongs to high-frequency noise is: For any IMF component, a frequency spectrum of the IMF component is obtained by Fourier transform; a frequency corresponding to each peak in the frequency spectrum is obtained by peak detection method and recorded as peak frequency; The cumulative sum of the absolute values ​​of the differences between the mean of all peak frequencies of the IMF component and the mean of all peak frequencies of all other IMF components is taken as the possibility that the IMF component belongs to high-frequency noise; The specific formula is: In the formula, Indicates the possibility that the IMF component belongs to high-frequency noise; represents the total number of all IMF components; Indicates The mean of all peak frequencies of the IMF components; represents the mean of all peak frequencies of the IMF components; Indicates taking the absolute value.

[0031] It should be noted that the larger the absolute value of the difference between the mean of all peak frequencies of the IMF component and the mean of all peak frequencies of all other IMF components, the greater the waveform difference between the IMF component and all other IMF components, and the greater the possibility that the IMF component belongs to high-frequency noise; Fourier transform is a prior art and will not be described in detail in this embodiment.

[0032] At this point, all cycles of the blood glucose monitoring data sequence set within each collection time period are obtained.

[0033] 2. Obtain the fluctuation difference of the blood glucose monitoring data sequence at each collection moment in each cycle.

[0034] It should be noted that the greater the difference in the fluctuation difference of the blood glucose monitoring data sequence between each collection moment and other collection moments, the greater the fluctuation difference of the blood glucose monitoring data sequence at each collection moment, and the greater the degree of abnormality of the blood glucose monitoring data sequence at each collection moment.

[0035] Preferably, for Any cycle of the blood glucose monitoring data sequence set within the collection time period The blood glucose monitoring data fluctuation curve within the collection time period is segmented according to the cycle to obtain the first All bands of the blood glucose monitoring data fluctuation curve within a collection time period.

[0036] Preferably, in some implementations of the embodiments of the present invention, according to the difference between the corresponding band of the blood glucose monitoring data sequence at each collection moment and other bands, the specific method for obtaining the fluctuation difference amount of the blood glucose monitoring data sequence at each collection moment in each cycle is: In the In the blood glucose monitoring data fluctuation curve within the collection time period, The band corresponding to the blood glucose monitoring data point at each collection moment is recorded as the target band; all bands except the target band are recorded as reference bands; According to The similarity between the blood glucose monitoring data sequence at the collection time and the blood glucose monitoring data sequence at other collection times is obtained. All similar data sequences at the time of collection; The first The position of the blood glucose monitoring data point in the target band at the time of collection is recorded as the target position; The first The first The first dimension data and The first The absolute value of the difference between the two dimensional data is recorded as the target band and the The reference bands The data difference value of the first dimension data; the target band is The DTW distance between the reference bands is recorded as the DTW distance between the target band and the The sequence difference value between the reference bands; the target band and the The cumulative sum of the data difference values ​​of all dimensional data between the reference bands is recorded as the target band and the The overall data difference value between the reference bands; the product of the overall data difference value and the sequence difference value is recorded as the target band and the The corrected difference value between the target band and all reference bands is recorded as the cumulative sum of the corrected difference values ​​between the target band and all reference bands. The fluctuation difference factor of the blood glucose monitoring data sequence at the collection time is The ratio between the number of all similar data sequences at the collection time is taken as the The fluctuation difference of the blood glucose monitoring data sequence at each collection moment; The specific formula is: In the formula, Indicates The collection time period is the first The fluctuation difference of the blood glucose monitoring data sequence at each collection moment; Indicates the first The number of all reference bands of the blood glucose monitoring data fluctuation curve within a collection time period; Represents the data type quantity of all dimensional data in the blood glucose monitoring data sequence at each collection moment; Indicates Within the collection time period The first Dimensional data; Indicates Within the collection time period The first Dimensional data; Indicates Within the collection time period The corresponding band of the blood glucose monitoring data point at the first collection moment is DTW distance between reference bands; Indicates Within the collection time period The number of all similar data sequences at the collection time; Indicates taking the absolute value.

[0037] It should be noted that Indicates the target band and The reference bands The data difference value of the dimension data; Indicates the target band and The overall data difference value between the reference bands; Indicates the target band and The corrected difference between the reference bands; Indicates The fluctuation difference factor of the blood glucose monitoring data sequence at each collection moment; The larger the Within the collection time period The blood glucose monitoring data sequence at the collection time is The greater the difference between the blood glucose monitoring data sequences at the same position in other reference bands within the acquisition time period, the The greater the possibility of abnormality of the blood glucose monitoring data sequence at the time of collection; the smaller the fluctuation difference factor, the greater the possibility of abnormality of the blood glucose monitoring data sequence at the time of collection; The more the blood glucose monitoring data sequences in the first collection period conform to the periodic distribution, the The less likely the blood sugar monitoring data within a collection period is to be abnormal; The larger the The smaller the fluctuation difference of the data points in the corresponding band of the blood glucose monitoring data points at the first collection moment, the The smaller the abnormality of the blood glucose monitoring data sequence at each collection moment is likely to be.

[0038] Preferably, in some implementations of the embodiments of the present invention, obtaining the The specific method for all similar data sequences at the collection time is: Preset a similarity threshold parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation; Jordi The blood glucose monitoring data sequence at the collection time is The cosine similarity between the blood glucose monitoring data sequences at the target position in the reference band is greater than or equal to the similarity threshold parameter , will The blood glucose monitoring data sequence at the target position in the reference band is used as the first Similar data sequences at the same time.

[0039] At this point, the fluctuation difference of the blood glucose monitoring data sequence at each collection moment in each cycle is obtained through the above method.

[0040] Step S003: According to the difference in the fluctuation difference of the blood glucose monitoring data sequence between each collection moment and other collection moments, the difference degree of the blood glucose monitoring data sequence at each collection moment in each cycle is obtained; according to the difference in the difference between each collection time period and the adjacent collection time periods, the abnormality degree at each collection moment in each cycle is obtained; according to the difference in the abnormality degree between each collection moment and the surrounding collection moments, the correction amplitude of the blood glucose concentration at each collection moment in each cycle is obtained.

[0041] 1. The difference of blood glucose monitoring data sequence at each collection time in each cycle.

[0042] It should be noted that under normal circumstances, the blood glucose monitoring data in each collection time period fluctuates uniformly and periodically. When an abnormality occurs, because the collection time interval is short, the abnormal collection time will not exist alone, and abnormalities will occur in the linked collection time. Therefore, the larger the abnormality, the greater the difference between the adjacent sampling times.

[0043] Preferably, in some implementations of the embodiments of the present invention, according to the difference in the fluctuation difference of the blood glucose monitoring data sequence between each collection moment and other collection moments, the specific method for obtaining the difference degree of the blood glucose monitoring data sequence at each collection moment in each cycle is: In the During the collection period, The sum of the absolute values ​​of the differences between the fluctuation difference of the blood sugar monitoring data sequence at the first collection time and the fluctuation difference of the blood sugar monitoring data sequence at other collection times is taken as the first The difference of blood glucose monitoring data sequence at each collection time; The specific formula is: In the formula, Indicates The collection time period is the first The difference of blood glucose monitoring data sequence at each collection time; Indicates The number of all collection moments in a collection time period; Indicates The collection time period is the first The fluctuation difference of the blood glucose monitoring data sequence at each collection moment; Indicates The collection time period is the first The fluctuation difference of the blood glucose monitoring data sequence at each collection moment; Indicates taking the absolute value.

[0044] At this point, the difference of the blood glucose monitoring data sequence at each collection moment in each cycle is obtained.

[0045] 2. Obtain the degree of abnormality at each collection moment in each cycle.

[0046] It should be noted that since the abnormal collection moment does not exist alone, the collection moments at adjacent positions in adjacent collection time periods will be linked to abnormalities. Therefore, the difference degree is corrected according to the difference in the difference degree at adjacent collection moments in adjacent collection time periods to obtain the degree of abnormality at the collection moment.

[0047] Preferably, in some implementations of the embodiments of the present invention, according to the difference between each acquisition time period and the difference between the adjacent acquisition time periods, the specific method for obtaining the abnormality degree at each acquisition moment in each cycle is: Preset a time range neighborhood parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation; The first Before the collection period The collection period and The collection time periods are all taken as A reference collection time period for a collection time period; The first The first The first The difference between the blood glucose monitoring data sequence at the left adjacent collection time of the collection time and the The first The first The absolute value of the difference between the difference of the blood glucose monitoring data sequence at the right adjacent collection time of the collection time is recorded as The adjacent abnormal difference of the reference acquisition time period; The cumulative sum of the adjacent abnormal differences of all reference collection time periods in the collection time period is recorded as The anomaly correction factor of the first acquisition period is The product of the differences between the blood glucose monitoring data sequences at the collection time is taken as the The abnormality of the blood glucose monitoring data sequence at each collection moment; The specific formula is: In the formula, Indicates The collection time period is the first The abnormality of the blood glucose monitoring data sequence at each collection moment; Indicates The collection time period is the first The difference of blood glucose monitoring data sequence at each collection time; Indicates The number of all reference collection time periods for a collection time period; Indicates The first The reference acquisition time period is the first The difference between the blood glucose monitoring data sequences at the left adjacent collection time of the collection time; Indicates The first The reference acquisition time period is the first The difference between the blood glucose monitoring data sequences at the right adjacent collection time of the collection time; Indicates taking the absolute value.

[0048] It should be noted that Represents the anomaly correction factor.

[0049] At this point, the degree of abnormality at each acquisition moment in each cycle is obtained.

[0050] 3. The correction range of blood glucose concentration at each collection moment in each cycle.

[0051] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining the correction amplitude of the blood glucose concentration at each collection moment in each cycle according to the difference in the degree of abnormality between each collection moment and the surrounding collection moments is: Preset a collection time neighborhood parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation; In the During the collection period, before the collection time The collection time and The time sequence composed of the collection moments is recorded as Neighborhood reference time sequence of the acquisition time; The first The mean of the patient blood glucose concentration data at all collection moments in the neighborhood reference time sequence of the collection moment is recorded as the first mean; Within the collection time period The absolute value of the difference between the patient's blood glucose concentration data at the time of collection and the first mean is recorded as the correction factor; The normalized value of the product of the abnormal degree of the blood glucose monitoring data sequence at the collection time and the correction factor is taken as the The correction range of blood glucose concentration at each collection time; In the formula, Indicates The collection time period is the first The correction range of blood glucose concentration at each collection time; Indicates The collection time period is the first The abnormality of the blood glucose monitoring data sequence at each collection moment; Indicates Within the collection time period The patient's blood glucose concentration data at each collection moment; Indicates Within the collection time period The number of all acquisition moments in the neighborhood reference moment sequence of the acquisition moment; Indicates Within the collection time period The first in the neighborhood reference time sequence of the collection time The patient's blood glucose concentration data at each collection moment; Indicates taking the absolute value; represents the linear normalization function.

[0052] It should be noted that Represents the correction factor.

[0053] So far, the correction range of the blood glucose concentration at each collection time in each cycle is obtained through the above method.

[0054] Step S004: Obtain an optimized correction factor of the non-invasive blood glucose monitor according to the correction amplitude of the blood glucose concentration at all acquisition moments in all acquisition time periods in all cycles; and optimize and correct the non-invasive blood glucose monitor according to the optimized correction factor.

[0055] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining the optimized correction factor of the non-invasive blood glucose monitor according to the correction amplitude of the blood glucose concentration at all acquisition moments in all acquisition time periods in all cycles is: The first Within the collection time period The patient's blood glucose concentration data at the time of collection is Within the collection time period The difference between the mean values ​​of the patient blood glucose concentration data at all collection moments in the neighborhood reference time sequence of the collection moment is recorded as the first difference; The first difference and the The product of the correction amplitude of blood glucose concentration at the time of collection is taken as the Correction value at each acquisition moment; If the first difference is greater than or equal to 0, The difference between the patient's blood glucose concentration data at the time of collection and the corrected value is taken as the Corrected patient blood glucose concentration data at each collection moment; If the first difference is less than 0, The sum of the patient's blood glucose concentration data at the time of collection and the correction value is taken as the Corrected patient blood glucose concentration data at each collection moment; The ratio between the mean of the corrected patient blood glucose concentration data at all collection moments in all cycles of all collection time periods and the mean of the patient blood glucose concentration data at all collection moments in all cycles of all collection time periods is used as the optimization correction factor of the non-invasive blood glucose monitor.

[0056] Preferably, the specific method for optimizing and correcting the non-invasive blood glucose monitor according to the optimized correction factor is: For each measurement value of the non-invasive blood glucose monitor, the sum of the optimized correction factor of the non-invasive blood glucose monitor and 1 is recorded as the correction value; the product of the measurement value and the correction value is taken as the optimized corrected measurement value of the non-invasive blood glucose monitor.

[0057] See also Figure 2 , which shows a characteristic relationship flow chart of a calibration method of a non-invasive blood glucose monitor based on joint optimization; At this point, this embodiment is completed.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A calibration method for a non-invasive blood glucose monitor based on joint optimization, characterized in that: The method comprises the following steps: Obtaining a set of blood glucose monitoring data sequences within a certain collection time period; By analyzing the noise distribution in the blood glucose monitoring data sequence, all the cycles of the blood glucose monitoring data sequence set in each acquisition time period are obtained; the blood glucose monitoring data sequence is segmented according to the cycle, and all the bands of the blood glucose monitoring data sequence set in each cycle are obtained; according to the difference between the corresponding band of the blood glucose monitoring data sequence at each acquisition moment and other bands, the fluctuation difference amount of the blood glucose monitoring data sequence at each acquisition moment in each cycle is obtained; According to the difference in the fluctuation difference of the blood glucose monitoring data sequence between each collection moment and other collection moments, the difference degree of the blood glucose monitoring data sequence at each collection moment in each cycle is obtained; according to the difference in the difference degree between each collection time period and the adjacent collection time periods, the abnormality degree at each collection moment in each cycle is obtained; according to the difference in the abnormality degree between each collection moment and the surrounding collection moments, the correction amplitude of the blood glucose concentration at each collection moment in each cycle is obtained; According to the correction amplitude of blood glucose concentration at all sampling moments in all cycles in all sampling time periods, an optimized correction factor of the non-invasive blood glucose monitor is obtained; and the non-invasive blood glucose monitor is optimized and corrected according to the optimized correction factor.

2. The calibration method of the non-invasive blood glucose monitor based on joint optimization according to claim 1, characterized in that: The specific method of obtaining all cycles of the blood glucose monitoring data sequence set within each acquisition time period by analyzing the noise distribution in the blood glucose monitoring data sequence includes: Get the Fluctuation curve of blood sugar monitoring data within a collection period; For The blood glucose monitoring data fluctuation curve within the acquisition time period is Fourier transformed, and the inverse of the frequency with the largest amplitude in the spectrum graph is taken as the One cycle of the blood glucose monitoring data fluctuation curve within the collection time period is recorded as the target cycle; According to the maximum amplitude in the spectrum and the frequency of the target cycle, the curve obtained is recorded as the fluctuation curve of the target cycle; The difference curve between the fluctuation curve of the blood glucose monitoring data in the acquisition time period and the fluctuation curve of the target period is recorded as the curve to be analyzed; the curve to be analyzed is decomposed by using the EDM decomposition algorithm to obtain several IMF components and residual terms; the possibility of each IMF component belonging to high-frequency noise is obtained; The IMF component with the highest probability of being high-frequency noise is recorded as the noise component, and the other IMF components and the residual term are added to obtain the curve to be analyzed after the noise is removed; the curve to be analyzed after the noise is removed is subjected to Fourier transform, and the inverse of the frequency corresponding to each peak in the spectrum is taken as the first One cycle of the blood glucose monitoring data fluctuation curve within a collection time period.

3. The calibration method of the non-invasive blood glucose monitor based on joint optimization according to claim 2, characterized in that: The acquisition The blood glucose monitoring data fluctuation curve within a collection time period includes the following specific methods: Construct a four-dimensional space based on the acquisition time, skin reflected light intensity data, skin electrical impedance data, and skin temperature data; The blood glucose monitoring data sequence set within the collection time period is input into the four-dimensional space to obtain the A number of blood glucose monitoring data points at collection times within a collection time period; In four-dimensional space, the least squares method is used to transform The curve fitting is performed for all the blood glucose monitoring data points at the collection time in the collection time period to obtain the Fluctuation curve of blood glucose monitoring data within a collection period.

4. The calibration method of the non-invasive blood glucose monitor based on joint optimization according to claim 2, characterized in that: The specific method of obtaining the possibility that each IMF component belongs to high-frequency noise includes: For any IMF component, a frequency spectrum of the IMF component is obtained by Fourier transform; a frequency corresponding to each peak in the frequency spectrum is obtained by peak detection method and recorded as peak frequency; The cumulative sum of the absolute values ​​of the differences between the mean of all peak frequencies of the IMF component and the mean of all peak frequencies of all other IMF components is taken as the possibility that the IMF component belongs to high-frequency noise.

5. The calibration method of the non-invasive blood glucose monitor based on joint optimization according to claim 2, characterized in that: The method of obtaining the fluctuation difference of the blood glucose monitoring data sequence at each collection moment in each cycle according to the difference between the corresponding band and other bands of the blood glucose monitoring data sequence at each collection moment includes: In the In the blood glucose monitoring data fluctuation curve within the collection time period, The band corresponding to the blood glucose monitoring data point at each collection moment is recorded as the target band; all bands except the target band are recorded as reference bands; According to The similarity between the blood glucose monitoring data sequence at the collection time and the blood glucose monitoring data sequence at other collection times is obtained. All similar data sequences at the time of collection; The first The position of the blood glucose monitoring data point in the target band at the time of collection is recorded as the target position; The first The first The first dimension data and The first The absolute value of the difference between the two dimensional data is recorded as the target band and the The reference bands The data difference value of the first dimension data; the target band is The DTW distance between the reference bands is recorded as the DTW distance between the target band and the The sequence difference value between the reference bands; the target band and the The cumulative sum of the data difference values ​​of all dimensional data between the reference bands is recorded as the target band and the The overall data difference value between the reference bands; the product of the overall data difference value and the sequence difference value is recorded as the target band and the The corrected difference between the reference bands; The cumulative sum of the corrected difference values ​​between the target band and all reference bands is recorded as The fluctuation difference factor of the blood glucose monitoring data sequence at each collection moment; The volatility difference factor and The ratio between the number of all similar data sequences at the collection time is taken as the The fluctuation difference of the blood glucose monitoring data sequence at each collection moment.

6. The calibration method of the non-invasive blood glucose monitor based on joint optimization according to claim 5, characterized in that: The acquisition All similar data sequences at the collection time, including the specific methods: Preset a similarity threshold parameter , The blood glucose monitoring data sequence at the collection time is The cosine similarity between the blood glucose monitoring data sequences at the target position in the reference band is greater than or equal to the similarity threshold parameter , will The blood glucose monitoring data sequence at the target position in the reference band is used as the first Similar data sequences at the same time.

7. The calibration method of the non-invasive blood glucose monitor based on joint optimization according to claim 1, characterized in that: The specific method of obtaining the difference degree of the blood glucose monitoring data sequence at each collection moment in each cycle according to the difference in the fluctuation difference amount of the blood glucose monitoring data sequence between each collection moment and other collection moments is as follows: In the During the collection period, The sum of the absolute values ​​of the differences between the fluctuation difference of the blood sugar monitoring data sequence at the first collection time and the fluctuation difference of the blood sugar monitoring data sequence at other collection times is taken as the first The difference of blood glucose monitoring data series at each collection moment.

8. The calibration method of the non-invasive blood glucose monitor based on joint optimization according to claim 1, characterized in that: The specific method of obtaining the abnormality degree at each collection moment in each cycle according to the difference between each collection time period and the adjacent collection time period is as follows: Preset a time range neighborhood parameter , will Before the collection period The collection period and The collection time periods are all taken as A reference collection time period for a collection time period; The first The first The first The difference between the blood glucose monitoring data sequence at the left adjacent collection time of the collection time and the The first The first The absolute value of the difference between the difference of the blood glucose monitoring data sequence at the right adjacent collection time of the collection time is recorded as The adjacent abnormal difference of the reference acquisition time period; The cumulative sum of the adjacent abnormal differences of all reference collection time periods in the collection time period is recorded as Anomaly correction factor for each acquisition period; The anomaly correction factor and The product of the differences between the blood glucose monitoring data sequences at the collection time is taken as the The abnormality degree of the blood glucose monitoring data sequence at each collection moment.

9. The calibration method of the non-invasive blood glucose monitor based on joint optimization according to claim 1, characterized in that: The method of obtaining the correction amplitude of the blood glucose concentration at each collection moment in each cycle according to the difference in the abnormality between each collection moment and the surrounding collection moments includes: Preset a collection time neighborhood parameter , in During the collection period, before the collection time The collection time and The time sequence composed of the collection moments is recorded as Neighborhood reference time sequence of the acquisition time; The first The mean of the patient blood glucose concentration data at all collection moments in the neighborhood reference time sequence of the collection moment is recorded as the first mean; Within the collection time period The absolute value of the difference between the patient's blood glucose concentration data at the time of collection and the first mean value is recorded as the correction factor; The first The normalized value of the product of the abnormal degree of the blood glucose monitoring data sequence at the collection time and the correction factor is taken as the The correction range of blood glucose concentration at each collection time.

10. The calibration method of the non-invasive blood glucose monitor based on joint optimization according to claim 9, characterized in that: The method of obtaining the optimized correction factor of the non-invasive blood glucose monitor according to the correction amplitude of the blood glucose concentration at all acquisition moments in all acquisition time periods in all cycles includes the following specific methods: The first Within the collection time period The patient's blood glucose concentration data at the time of collection is Within the collection time period The difference between the mean values ​​of the patient blood glucose concentration data at all collection moments in the neighborhood reference time sequence of the collection moment is recorded as the first difference; The first difference and the The product of the correction amplitude of blood glucose concentration at the time of collection is taken as the Correction value at each acquisition moment; If the first difference is greater than or equal to 0, The difference between the patient's blood glucose concentration data at the time of collection and the corrected value is taken as the Corrected patient blood glucose concentration data at each collection moment; If the first difference is less than 0, The sum of the patient's blood glucose concentration data at the time of collection and the correction value is taken as the Corrected patient blood glucose concentration data at each collection moment; The ratio between the mean of the corrected patient blood glucose concentration data at all collection moments in all cycles of all collection time periods and the mean of the patient blood glucose concentration data at all collection moments in all cycles of all collection time periods is used as the optimization correction factor of the non-invasive blood glucose monitor.

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