A method and system for indentation compensation of CGM sensors

Abstract

This invention relates to the field of blood glucose detection technology and discloses a depression compensation method and system for CGM sensors. The method extracts temporal and physiological features from the acquired blood glucose-related current signals to generate fused feature data. Based on preset temporal dimension rules, it first determines whether the depression is short-term, accurately identifying the temporal features of non-physiological depressions. This enables effective identification of non-physiological signal depressions at night. The dual-dimensional judgment rules achieve accurate differentiation of signal causes. When a short-term depression is determined in the temporal dimension, it is verified by physiological dimension rules. This dual-layer identification logic of initial screening of temporal features and verification of physiological features avoids signal confusion, reduces the false positive rate, prevents the risk of missed hypoglycemia or erroneous intervention, and solves the technical problem that nighttime monitoring easily miscorrects true hypoglycemia signals or misjudges non-physiological depressions as hypoglycemia.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blood glucose detection, in particular to a recess compensation method and system for a CGM sensor. BACKGROUND

[0002] Continuous glucose monitoring (CGM) system is a key device for daily blood glucose management of diabetic patients, which generates continuous glucose monitoring results by collecting the glucose concentration related current signal of subcutaneous interstitial fluid in real time, providing basis for patients to avoid hypoglycemia and hyperglycemia risks and adjust treatment plan, especially during the night sleep period, accurate monitoring is crucial to avoid serious consequences such as hypoglycemic coma.

[0003] However, the change of sleep posture of the subject at night easily leads to pressure or slight displacement of the sensor, causing the blood glucose related current signal to drop for a short time. In addition, when the subject is in a real hypoglycemic state, the non-physiological signal drop is difficult to distinguish from the current signal drop corresponding to physiological hypoglycemia, and the physiological drop is easily confused with the real hypoglycemic signal. If the real hypoglycemic signal is mistakenly determined as a drop caused by pressure on the sensor and corrected, the real hypoglycemic risk will be covered up, resulting in missed reports and delayed intervention; if the non-physiological drop is mistakenly determined as real hypoglycemia, it will cause false alarms and unnecessary hypoglycemic intervention, seriously threatening the health of the patient.

[0004] In summary, the prior art cannot effectively identify the non-physiological signal drop caused by the change of sleep posture at night, and cannot distinguish the cause of the signal drop when the subject is in a real hypoglycemic state, which easily miscorrects the real hypoglycemic signal or misjudges the non-physiological drop as hypoglycemia, resulting in low reliability and safety of night monitoring. SUMMARY

[0005] The present application provides a recess compensation system for a CGM sensor, which solves the technical problem of easily miscorrecting the real hypoglycemic signal or misjudging the non-physiological drop as hypoglycemia during night monitoring.

[0006] In order to solve the above technical problems, the present application provides the following technical solutions:

[0007] A recess compensation system for a CGM sensor, comprising:

[0008] A signal acquisition module for acquiring blood glucose related current signals of a subject at night, and transmitting the blood glucose related current signals to a signal processing module in real time;

[0009] A signal processing module for receiving the blood glucose related current signals, extracting time domain features and physiological correlation features thereof to obtain fusion feature data of blood glucose change rate, and transmitting the fusion feature data to a signal discrimination module;

[0010] a signal discrimination module, configured to receive the fusion feature data, analyze the fusion feature data based on a preset double-dimension judgment rule, determine whether it is a short-term decline through a time-domain dimension rule, and verify whether it is a true hypoglycemia through a physiological dimension rule, to obtain a discrimination result and a corresponding target signal segment, and transmit the discrimination result and the corresponding target signal segment to the depression compensation module;

[0011] a depression compensation module, configured to receive the discrimination result and the target signal segment, extract signal baseline data before the depression in the target signal segment if the discrimination result is a non-physiological depression, combine the signal baseline data with overall blood glucose signal dynamic trend data, and correct the target signal segment by using a linear extrapolation algorithm to obtain a compensated current signal, or retain the original target signal segment as the compensated current signal if it is a true hypoglycemia, and transmit the compensated current signal to the blood glucose estimation module;

[0012] a blood glucose estimation module, configured to receive the compensated current signal, process the compensated current signal based on a preset blood glucose value calculation model, and obtain an accurate night-time blood glucose monitoring result and output the accurate night-time blood glucose monitoring result.

[0013] The basic scheme principle and beneficial effects are as follows: the collected blood glucose related current signal is subjected to time-domain feature extraction and physiological correlation feature extraction to generate fusion feature data, whether it is a short-term decline is determined based on a preset time-domain dimension rule, the time-domain feature of non-physiological depression can be accurately locked, and thus effective identification of night-time non-physiological signal depression can be achieved. The double-dimension judgment rule is used to accurately distinguish the causes of the signal, and the physiological dimension rule is used to verify when the time-domain dimension is determined to be a short-term decline. The double-layer discrimination logic of the time-domain feature preliminary screening and the physiological feature verification can avoid signal confusion, reduce the misjudgment rate, and prevent the occurrence of missed hypoglycemia risk or false intervention.

[0014] Since the reliability and safety of night-time monitoring are low, for non-physiological depression, the signal baseline data before the depression is extracted and combined with the overall blood glucose signal dynamic trend to correct the target signal segment, so as to ensure that the compensated signal conforms to the true blood glucose change trend; for true hypoglycemia signal, the original segment is directly retained. This design of distinguishing processing and accurate compensation achieves the dual goals of accurately compensating non-physiological signals and completely retaining true hypoglycemia signals, avoids the interference of non-physiological depression on the blood glucose curve, ensures the reliability of the monitoring result, prevents the true hypoglycemia signal from being mistakenly corrected, and can avoid the risk of misjudgment and missed judgment.

[0015] Further, the signal acquisition module is further configured to collect physiological feature data of the subject at night, and transmit the physiological feature data to the signal processing module in real time.

[0016] The signal processing module receives the physiological characteristic data, performs feature quantization processing on the physiological characteristic data, and integrates the quantized physiological characteristic data with the fusion feature data to generate joint feature data containing time domain features, physiological correlation features, and quantized physiological features, and transmits the joint feature data to a signal discrimination module;

[0017] The signal discrimination module analyzes the joint feature data based on a preset double-dimension judgment rule: on the basis of a short-term decline determined in the time domain dimension rule, whether the quantized physiological characteristic data proves to be a non-physiological decline.

[0018] The beneficial effect is that since a non-physiological decline and a real hypoglycemic signal are difficult to accurately distinguish only by means of a blood glucose related current signal, the quantized physiological characteristic data is used to prove the non-physiological decline after the short-term decline is determined in the time domain dimension, the double-data-dimension discrimination mode of the blood glucose signal and the physiological characteristic data compensates for the discrimination blind area of the single blood glucose signal, can reduce the confusion rate of the non-physiological decline and the real hypoglycemia, thereby reducing the risk of misjudgment, making the discrimination result more physiological, and improving the accuracy and safety of night monitoring.

[0019] Further, the signal processing module is also used to receive the collection time stamps of the blood glucose related current signal and the physiological characteristic data, and calculate the time difference Δt between the two:

[0020] If the time difference Δt is less than or equal to a preset synchronization threshold, it is determined that the blood glucose related current signal and the physiological characteristic data are synchronous data collected at the same time, and the signal processing module transmits the integrated joint feature data to the signal discrimination module;

[0021] If the time difference Δt is greater than the preset synchronization threshold, it is determined that the two are non-synchronous data, and the signal processing module transmits only the fusion feature data extracted based on the blood glucose related current signal to the signal discrimination module.

[0022] The beneficial effect is that since a non-physiological decline and a real hypoglycemic signal are easily confused, if there is a time delay in the collection of the physiological characteristic data and the blood glucose signal, the data is misaligned, which will increase the probability of misjudgment. By calculating the relationship between the time difference of the two and the preset synchronization threshold, the data validity is screened, the time synchronization verification mechanism excludes the misalignment interference from the data source, prevents time misalignment, and reduces the misjudgment rate caused by different data.

[0023] Further, the signal processing module is also used to obtain the collection time stamp T1 of the blood glucose related current signal and the collection time stamp T2 of the physiological characteristic data transmitted by the signal collection module, and calculate the time delay ΔT = |T1-T2| of the two;

[0024] If ΔT=0, it is determined that the data is synchronous data without delay, the joint feature data is generated and transmitted to the signal discrimination module;

[0025] If ΔT > 0, it is determined that there is time-delayed asynchronous data, and the signal processing module calls the historical data cache library to perform time calibration on the two types of signals respectively based on the change trend of the historical data:

[0026] For the blood glucose related current signal, a linear extrapolation method is used to generate a corrected current signal at the time of (T1+T2) / 2;

[0027] For the physiological characteristic data, a moving average method is used to generate corrected physiological characteristic data at the time of (T1+T2) / 2;

[0028] After correction, time-matched synchronous data is obtained, which is integrated into joint feature data and transmitted to the signal discrimination module.

[0029] The beneficial effect is that relying on the collaborative evidence of multi-source data, part of the physiological characteristic data is discarded, therefore, by calculating the time delay, the asynchronous data is calibrated specifically to ensure that the two types of data are time-matched, which avoids the limitations of data discard and the misplacement interference of asynchronous data, improves the utilization rate of physiological characteristic data, and reduces the misjudgment rate of calibrated data.

[0030] Further, the signal processing module establishes a dynamic time synchronization mechanism through a reference time source, the signal acquisition module records a reference time stamp T1' when acquiring the blood glucose related current signal, and a reference time stamp T2' when acquiring the physiological characteristic data;

[0031] After receiving T1' and T2', the signal processing module calculates the initial time delay ΔT'=|T1'-T2'|, and starts dynamic monitoring: every preset time interval extracts the ΔT' value of a group of synchronous data, constructs a delay monitoring sequence, and calculates the delay mean μ and standard deviation σ through a sliding window algorithm;

[0032] When ΔT' is within the interval [μ-2σ, μ+2σ], it is determined that the time delay is within the normal range; when ΔT' exceeds the interval, it is determined that the time delay is abnormal, triggering a synchronous calibration instruction.

[0033] The beneficial effect is that since signal acquisition is prone to errors caused by local clock drift due to crystal oscillator deviation, by synchronizing with a unified reference time source, the timing dimension of the time stamp is ensured to be consistent; at the same time, the delay monitoring sequence is constructed to calculate the mean and standard deviation to dynamically divide the normal and abnormal delay intervals to trigger recalibration, eliminate clock drift cumulative error, reduce the timing error of the time stamp, and improve the accuracy of delay determination.

[0034] Further, the dual-dimension judgment rule realizes the merging and fusion of the time domain dimension and the physiological dimension through feature weight allocation:

[0035] Set the fusion judgment value S, the calculation formula is S = a x A + b x B + g x C + d x D, wherein A is a time domain feature parameter, B is a duration parameter, C is a recovery trend parameter, and D is a physiological feature parameter, a, b, g, and d are weight coefficients; when S≤the preset fusion judgment value, it is determined that it is not physiological subsidence; when S> the preset fusion judgment value, it is determined that it is real hypoglycemia.

[0036] Beneficial effects are that: due to the phased identification of easy misjudgment caused by single feature threshold rigidity, the quantification fusion of time domain and physiological dimensions is realized through feature weight distribution, the identification logic is upgraded from threshold judgment to multi-feature comprehensive score, and the accuracy and flexibility of signal identification are improved.

[0037] Further, when the recess compensation module extracts the signal baseline data before subsidence in the target signal segment, the subsidence time point is determined in advance:

[0038] The blood glucose related current signal in the fusion feature data transmitted by the signal processing module is analyzed by high frequency sampling to generate a continuous waveform sequence of time signal intensity;

[0039] Set a subsidence trigger threshold: when the signal intensity of the continuous M sampling points is greater than the falling amplitude of the previous sampling point by more than a preset instantaneous change threshold, and the signal intensity of the Mth sampling point is greater than the average value of the stable segment before subsidence by more than a first preset percentage, mark the time corresponding to the Mth sampling point as the subsidence starting time point t0;

[0040] Track the waveform sequence to the rising amplitude of the continuous N sampling points, which is greater than the second preset percentage of the total subsidence amplitude, and the signal fluctuation amplitude of the subsequent K sampling points is less than a third percentage, mark the time corresponding to the Nth sampling point as the subsidence ending time point t1, and the subsidence time point range is [t0, t1];

[0041] Extract the signal data in the preset period before the subsidence starting time point t0, and calculate the signal baseline data before subsidence by using a weighted average algorithm, the closer the sampling point to t0, the higher the weight, and the weight coefficient is distributed according to a linear increasing rule.

[0042] Beneficial effects are that: through high frequency sampling and multi-condition trigger mechanism, accurate baseline is provided for recess compensation, overcorrection or insufficient correction caused by baseline distortion is avoided, and the authenticity of the corrected signal is improved.

[0043] A recess compensation method for a CGM sensor, comprising the following steps:

[0044] Step 1: Collect the subject's blood glucose-related current signal at night through the signal acquisition module, and transmit the blood glucose-related current signal to the signal processing module in real time;

[0045] Step 2: The signal processing module receives the blood glucose-related current signal, extracts its time-domain features and physiological correlation features to obtain fused feature data containing the rate of blood glucose change, and then transmits the fused feature data to the signal identification module.

[0046] Step 3: The signal identification module receives the fused feature data and analyzes it based on the preset two-dimensional judgment rules: first, it determines whether it is a short-term depression through the time domain dimension rules, and then verifies whether it is a real hypoglycemia through the physiological dimension rules, and obtains the identification result and the corresponding target signal segment, which is then transmitted to the depression compensation module.

[0047] Step 4: The depression compensation module receives the identification result and the target signal segment. If the identification result is a non-physiological depression, the baseline signal data before the depression in the target signal segment is extracted. Combined with the overall blood glucose signal dynamic trend data, the target signal segment is corrected using a linear extrapolation algorithm to obtain the compensated current signal. If it is a true hypoglycemia, the original target signal segment is retained as the compensated current signal, and the compensated current signal is transmitted to the blood glucose estimation module.

[0048] Step 5: The blood glucose estimation module receives the compensated current signal, processes it based on the preset blood glucose value calculation model, and obtains and outputs accurate nighttime blood glucose monitoring results.

[0049] Furthermore, in step 1, the signal acquisition module also collects the subject's physiological characteristic data at night and transmits the physiological characteristic data to the signal processing module in real time; in step 2, after receiving the physiological characteristic data, the signal processing module performs feature quantization processing on it, and integrates the quantized physiological characteristic data with the fused characteristic data to generate joint feature data containing temporal features, physiological correlation features, and quantified physiological features, which is then transmitted to the signal identification module; in step 3, the signal identification module analyzes the joint feature data based on a preset two-dimensional judgment rule: based on the temporal dimension rule determining it as short-term depression, it uses the quantified physiological characteristic data to corroborate whether it is a non-physiological depression.

[0050] The basic principles and beneficial effects of the scheme are as follows: By generating fused feature data through feature extraction, multi-dimensional evidence is provided for subsequent identification, avoiding the limitations of single-signal judgment; a two-dimensional identification rule is adopted, first determining short-term depressions in the time domain and then physiologically verifying hypoglycemia, preventing signal confusion; differentiated processing is performed based on the identification results: baseline data is extracted and linearly extrapolated for non-physiological depressions, while the original signal is retained for true hypoglycemia, avoiding the risk of false positives and missed detections. This improves the accuracy of non-physiological depression correction, reduces the hypoglycemia false negative rate, and enhances the reliability and safety of nighttime monitoring. Attached Figure Description

[0051] Figure 1 This is a system structure block diagram of Embodiment 1 of a depression compensation system for a CGM sensor;

[0052] Figure 2 This is a flowchart of Embodiment 8 of a depression compensation method for a CGM sensor. Detailed Implementation

[0053] The following detailed description illustrates the specific implementation method:

[0054] Example 1

[0055] This embodiment discloses a depression compensation system for a CGM sensor, as shown in the attached figure. Figure 1 As shown, the signal acquisition module uses an implantable CGM sensor that fits into the subcutaneous tissue of the subject's abdomen. It collects blood glucose-related current signals at night through the principle of electrochemical induction. The sampling frequency is set to 1Hz, which means that one set of current signal data is collected every second. After the acquisition is completed, it is transmitted to the signal processing module in real time via Bluetooth protocol.

[0056] The signal processing module uses a microcontroller. After receiving the blood glucose-related current signal transmitted from the signal acquisition module, it extracts its time-domain and physiological features. First, time-domain features are extracted using a sliding window algorithm (window size set to 5 seconds) to extract three time-domain features: signal drop amplitude, drop duration, and recovery trend. For example, if the current signal acquired at a certain moment drops sharply from 80nA to 50nA, the calculated drop amplitude is 30nA, the drop duration is 10 seconds, and the signal recovers to 75nA within the next 15 seconds, showing a rapid drop followed by a slow recovery. Then, physiological features are extracted. Based on a preset current signal-blood glucose concentration calibration curve, the current signal is converted into blood glucose concentration data through linear fitting, and the rate of blood glucose change is calculated using the difference formula. The blood glucose rate of change is calculated as v = (C2 - C1) / (t2 - t1), where C1 and C2 are the blood glucose concentrations at times t1 and t2, respectively. If the blood glucose concentration is 5.0 mmol / L at t1 = 0 seconds and 4.5 mmol / L at t2 = 10 seconds, then the blood glucose rate of change is calculated as v = -0.05 mmol / (L·s). Finally, fused feature data is generated by integrating the three extracted time-domain features with one physiological correlation feature (blood glucose rate of change) to generate fused feature data. The data format is decrease magnitude - duration - recovery trend - blood glucose rate of change, for example, "30nA - 10s - rapid decrease followed by slow recovery - 0.05 mmol / (L·s)", and is transmitted to the signal identification module through the interface.

[0057] The signal identification module uses an FPGA chip and pre-stores dual-dimensional judgment rules. After receiving and analyzing the fused feature data, the rules are as follows: Time domain dimension rules: The preset amplitude threshold is 1.5 times the maximum change value of the current signal corresponding to the normal blood glucose fluctuation of the subject in the past 3 days (assuming it is 20nA), i.e., 30nA; the duration of the decline is limited to 5-60 seconds; the signal recovery trend must meet the requirement that the signal recovers to more than 90% of the baseline value before the decline within 30 seconds after the decline. In this embodiment, the signal drop amplitude of 30 nA in the fused feature data is equal to the preset amplitude threshold, and the drop duration of 10 seconds is in the range of 5-60 seconds. However, the recovery trend is a rapid drop followed by a slow recovery, which does not meet the requirement of recovering to more than 90% of the baseline value within 30 seconds. Physiological dimension rule: The preset threshold for the rate of change of blood glucose in physiological hypoglycemia is -0.005 to -0.042 mmol / (L·s). In this embodiment, the rate of change of blood glucose is -0.05 mmol / (L·s), which exceeds the threshold range. Therefore, it is verified as a non-physiological depression. Identification result output: The identification result of "non-physiological depression" is generated, and the corresponding target signal segment is locked, that is, the 10-second current signal data from the start of the signal drop to the time of recovery and stabilization, which is transmitted to the depression compensation module.

[0058] The indentation compensation module uses an ARM Cortex-M7 processor. After receiving the identification results and the target signal segment, it performs the following operations: extracts the signal data (60 sets in total) within 60 seconds before the indentation in the target signal segment, calculates its arithmetic mean as the baseline data of the signal before the indentation, assuming that the average value of the 60 sets of data is 79.5nA, that is, the baseline data is 79.5nA; combines the overall blood glucose signal dynamic trend data (assuming that the overall trend is a slow decrease with a trend slope of -0.02nA / s), and uses a linear extrapolation algorithm to correct the target signal segment, and completes the signal correction for all moments within the target signal segment in sequence to obtain the compensated current signal; and transmits the compensated current signal to the blood glucose estimation module through the UART interface.

[0059] The specific steps for correction using the linear extrapolation algorithm are as follows: Select stable signal segments before and after the target signal segment as references. Assume the target segment is from time t3 to t7, with the preceding stable segment being t1-t2, where signal values ​​I1 = 10nA and I2 = 11nA, and the subsequent stable segment being t8-t9, where signal values ​​I8 = 15nA and I9 = 16nA. Calculate the linear trend slope: Based on the preceding stable segment, calculate the initial slope k1 = (I2-I1) / (t2-t1) = 1nA / time; based on the subsequent stable segment, calculate the recovery slope k2 = (I9-I8) / (t9-t8) = 1nA / time. Since both are consistent, the overall linear trend is determined to be a slope of 1nA / time.

[0060] Based on the endpoint of the previous stationary segment (t2=2, I2=11nA) and the slope 1, the correction values ​​for each time step of the target segment are extrapolated sequentially: I3=11 + 1×(3-2)=12nA, I4=11 + 1×(4-2)=13nA, I5=11 + 1×(5-2)=14nA, I6=11 + 1×(6-2)=15nA

[0061] I7 = 11 + 1 × (7-2) = 16nA. After correction, the compensated current signal is obtained, which makes the target segment and the preceding and following stable segments form a continuous linear trend, eliminating distortion.

[0062] The blood glucose estimation module pre-stores a blood glucose value calculation model, which is a multiple linear regression model obtained by fitting a large amount of clinical data: G = a×I + b×v + c, where G is the blood glucose concentration in mmol / L, I is the compensated current signal in nA, v is the rate of change of blood glucose in mmol / (L·s), and a, b, and c are model coefficients, with a in mmol / (L·nA), b in s, and c in mmol / L. Assume a=0.05, b=-2, and c=3. After receiving the compensated current signal (e.g., 79.4 nA), it is substituted into the model to calculate: G=0.05×79.4 + (-2)×(-0.05) + 3=7.07mmol / L, obtaining an accurate nighttime blood glucose monitoring result, which is then output in real time on the LCD display.

[0063] Example 2

[0064] The only difference from Example 1 is the addition of physiological feature data acquisition and fusion processing, as detailed below:

[0065] In addition to acquiring blood glucose-related current signals, the signal acquisition module also integrates a heart rate sensor and a triaxial accelerometer to simultaneously acquire the subject's physiological characteristic data at night. The heart rate sensor has a sampling frequency of 1Hz and acquires the subject's nighttime heart rate data, ranging from 50 to 100 beats per minute. The triaxial accelerometer has a sampling frequency of 1Hz and acquires the subject's nighttime body movement data. The acceleration vector magnitude is calculated to reflect the body movement amplitude, ranging from 0 to 2g. After acquisition, the physiological characteristic data (heart rate + body movement amplitude) and blood glucose-related current signals are synchronously transmitted to the signal processing module.

[0066] After receiving physiological characteristic data, the signal processing module performs the following processing: It normalizes the heart rate data using the formula H = (H - Hmin) / (Hmax - Hmin), where H is the original heart rate. For example, if Hmin = 50 beats / minute, Hmax = 100 beats / minute, and the original heart rate H = 75 beats / minute, then the heart rate quantization value H = 0.5. It also performs threshold quantization on body movement data, setting the body movement amplitude threshold to 0.05g. When the body movement amplitude is ≥ 0.05g, the body movement quantization value M = 1 (indicating significant body movement); when the body movement amplitude is < 0.05g, M = 0 (indicating...). (If there is no obvious body movement), and the body movement amplitude is 0.8g at a certain moment, then M=1; The quantified physiological characteristic data (H=0.5, M=1) are correlated and integrated with the fusion characteristic data (30nA-10s-rapid decrease followed by slow recovery--0.05mmol / (L·s)) generated in Example 1 to generate joint characteristic data, in the format of decrease amplitude-duration-recovery trend-blood glucose change rate-heart rate quantification value-body movement quantification value, that is, 30nA-10s-rapid decrease followed by slow recovery--0.05mmol / (L·s)-0.5-1, which is transmitted to the signal identification module.

[0067] The signal identification module performs a two-dimensional judgment based on joint feature data: the time domain dimension judgment is the same as in Example 1, and it is determined to be a short-term depression. Physiological features are used to corroborate this judgment. The quantified physiological feature data is analyzed. Since the body movement quantification value M=1 (there is obvious body movement) and the heart rate quantification value H=0.5 (no significant heart rate abnormality), it is consistent with the physiological features of non-physiological depression caused by changes in sleep posture. Therefore, it is confirmed to be a non-physiological depression. The identification result of non-physiological depression and the corresponding target signal segment are generated and transmitted to the depression compensation module.

[0068] Example 3

[0069] The only difference from Example 2 is the addition of a time synchronization judgment mechanism for multi-source data in the signal processing module, as follows: When collecting blood glucose-related current signals and physiological characteristic data, the collection timestamp is recorded synchronously, and the blood glucose-related current signals, physiological characteristic data, and their corresponding timestamps T1 and T2 are synchronously transmitted to the signal processing module. After receiving the data, the signal processing module extracts the differences between timestamps T1 and T2, for example, Δt = 50ms; the preset synchronization threshold is 100ms. Since Δt = 50ms ≤ 100ms, it is determined to be synchronous data, and the signal processing module transmits the integrated joint feature data to the signal identification module. If Δt = 150ms > 100ms, it is determined to be asynchronous data, and the signal processing module only transmits the fused feature data extracted based on the blood glucose-related current signal to the signal identification module, avoiding misjudgment caused by asynchronous data.

[0070] Example 4

[0071] The only difference from Example 3 is the optimized asynchronous data processing strategy and the addition of a time calibration mechanism, as follows: After acquiring the acquisition timestamp T1 of the blood glucose-related current signal and the acquisition timestamp T2 of the physiological characteristic data, the time delay ΔT = T1 - T2 is calculated. If ΔT = 0, it is determined to be synchronous data without delay, and the joint feature data is directly integrated and transmitted to the signal identification module. If ΔT > 0, it is determined to be asynchronous data with a time delay, and the time calibration process is initiated. The historical data cache library (caching historical data of the last 1 minute) is called, and the two types of signals are calibrated based on the changing trend of the historical data: The corrected current signal at the intermediate time T_mid = (T1 + T2) / 2 is generated using the linear extrapolation method. Assuming that in the historical data, the current signal 5 seconds before T1 is 82nA, the current signal at T1 is 80nA, and the historical changing trend is a linear decrease with a slope k = -0.4nA / s, then the corrected current signal I_cal at T_mid is 79.96nA. The corrected physiological characteristic data at T_mid is generated using the moving average method. Assuming the historical data shows a heart rate of 70 beats / minute 3 seconds before T2, 71 beats / minute 2 seconds before T2, 72 beats / minute 1 second before T2, and 73 beats / minute at T2, and setting the sliding window size to 4, the corrected heart rate H_cal = 71.5 beats / minute is used to calibrate the body movement data in the same way. The calibrated current signal and physiological characteristic data are then integrated into joint feature data and transmitted to the signal discrimination module.

[0072] Example 5

[0073] The only difference from Example 4 is the establishment of a dynamic time synchronization mechanism to improve the accuracy of time delay determination. Specifically, the following steps are taken: a baseline timestamp T1' is recorded synchronously when collecting blood glucose-related current signals, and a baseline timestamp T2' is recorded synchronously when collecting physiological characteristic data; the initial time delay ΔT' = T1' - T2' is calculated; the preset time interval is 10 seconds, and a set of ΔT' values ​​of synchronized data is extracted every 10 seconds to construct a delay monitoring sequence. For example, the ΔT' values ​​of 10 consecutively extracted sets of data are 20ms, 22ms, 19ms, 21ms, 23ms, 20ms, 18ms, 22ms, 19ms, 21ms, 23ms, 20ms, 18ms, 22ms, 23ms, 20ms, 22ms, 23ms, 20ms, 22ms, 23ms, 20ms, 22ms, 23ms, 20ms, 22ms, 23ms, 20ms, 22ms, 23ms, 20ms, 22ms, 23ms, 24ms, 25 ... ms, 22ms, 21ms, 19ms; The mean delay μ and standard deviation σ are calculated using a sliding window algorithm (window size is 10 groups). If the calculated μ=20.5ms and σ=1.5ms, then the normal time delay range is [μ-2σ,μ+2σ]=[20.5-3,20.5+3]=[17.5ms,23.5ms]; If the extracted ΔT'=25ms at a certain moment exceeds the normal range, it is determined to be an abnormal time delay, triggering a synchronization calibration command to control the signal acquisition module to resynchronize the reference time source and correct the timestamp recording deviation.

[0074] Example 6

[0075] The only difference from Example 5 is that the dual-dimensional judgment rule is fused and optimized, and quantitative fusion is achieved by feature weight allocation, as follows: The calculation formula for the preset fusion judgment value S is: S=α×A+β×B+γ×C+δ×D, where α, β, γ, and δ are weight coefficients, which are determined based on historical data statistics and specific performance objectives (such as error minimization and stability maximization), satisfying α+β+γ+δ=1; A is a time-domain feature parameter: the ratio of the signal drop amplitude to a preset amplitude threshold, where the preset amplitude threshold is 30nA. If the signal drop amplitude at a certain moment is 30nA, then A=30 / 30=1; B is a duration parameter: the ratio of the drop duration to 60 minutes. If the drop duration is 10 minutes, then... B = 10 / 60 ≈ 0.167. If the duration exceeds 60 minutes, then B = 1 (typically, the default sampling frequency of mainstream continuous glucose monitoring (CGM) systems is to record blood glucose values ​​once every 5 minutes, i.e., 12 data points per hour and 288 data points per day); C is the recovery trend parameter: the ratio of the recovery rate within 30 minutes after the dip to the dip rate. If the dip rate is 30 nA and the recovery rate within 30 minutes is 25 nA, then C = 25 / 30 ≈ 0.833; D is the physiological characteristic parameter: the ratio of the rate of change of blood glucose to the upper limit of the physiological hypoglycemia rate threshold (0.042 mmol / (L·s)). If the rate of change of blood glucose is 0.05 mmol / (L·s), then D = 0.05 / 0.042 ≈ 1.19;

[0076] Assuming a fusion judgment value of 0.8, α=0.3, β=0.2, γ=0.2, and δ=0.3, a depression of S≤0.8 is considered non-physiological, while a depression of S>0.8 is considered true hypoglycemia. The fusion judgment value S is calculated to be 0.857. Since S>0.8, it is initially determined to be true hypoglycemia. This is further validated by physiological characteristic data. If the physiological characteristic data shows a slow increase in heart rate and small body movement amplitude, consistent with the physiological response of true hypoglycemia, then it is ultimately determined to be true hypoglycemia, and the original target signal segment is retained.

[0077] Example 7

[0078] The only difference from Example 6 is the refinement of the sinking time point determination and baseline data extraction process, specifically as follows: High-frequency sampling analysis is performed on the blood glucose-related current signal, with the sampling frequency set to 10Hz, generating a continuous waveform sequence of time and signal intensity. The sinking trigger threshold parameters are set as follows: M=3, preset instantaneous change threshold=2nA, first preset percentage=15%, N=5, second preset percentage=80%, K=10, and third percentage=5%. The waveform sequence is iterated. When the signal intensity of three consecutive sampling points decreases by more than the preset instantaneous change threshold 2nA compared to the previous sampling point, and the signal intensity of the third sampling point decreases by 14.5% compared to the average value of the stable segment before the sinking, which is close to 15%, the condition is met, and this is marked as the sinking start time point t0.

[0079] Continuing to traverse the waveform sequence, when the cumulative rise of 5 consecutive sampling points exceeds the second preset percentage, and the signal fluctuation amplitude of the subsequent 10 sampling points is less than 5%, 1800ms is marked as the end time point t1 of the dip, with the dip time point range being [t0, t1]. Signal data (300 sampling points) within 30 seconds before the start time point t0 of the dip is extracted. A weighted average algorithm is used to calculate the baseline data, with weight coefficients distributed according to a linear increasing law. The closer the sampling point is to t0, the higher its weight. The weight coefficient of the sampling point in the nearest second is 3, and the weight coefficient of the sampling point in the farthest second is 1. The weight coefficients of the intermediate sampling points increase linearly. Assuming the baseline data obtained through weighted average calculation is 79.2 nA.

[0080] Example 8

[0081] This embodiment discloses a depression compensation method for CGM sensors, as shown in the attached figure. Figure 2As shown, the specific steps are as follows: Blood glucose-related current signals of the subject at night are collected using an implanted CGM sensor at a sampling frequency of 1Hz. The collected current signals are transmitted to the microcontroller in real time via Bluetooth. After receiving the blood glucose-related current signals, temporal and physiological features are extracted. The signal decrease amplitude, duration, and recovery trend are extracted using a 5-second sliding window. Blood glucose concentration is obtained through a calibration curve of "current signal - blood glucose concentration," and the rate of blood glucose change is calculated using differential calculation. Fusion feature data is generated in the format of "decline amplitude - duration - recovery trend - rate of blood glucose change" and transmitted to the FPGA chip. The fusion feature data is analyzed based on a two-dimensional judgment rule. From the temporal dimension, it is determined whether the signal is a short-term dip, and from the physiological dimension, it is verified whether it is true hypoglycemia. The identification result and the corresponding target signal segment are obtained, for example, determining that "the 10-second signal from 23:05:00 to 23:05:10 is a non-physiological dip," and transmitted to the ARM processor. After receiving the identification results and the target signal segment, if it is a non-physiological depression, the signal data of the 60 seconds before t0 is extracted, the baseline data is calculated, and combined with the overall dynamic trend of blood glucose signal, a linear extrapolation algorithm is used to correct the target signal segment to obtain the compensated current signal. If it is a true hypoglycemia, the original target signal segment is retained as the compensated current signal. The compensated current signal is then transmitted to the blood glucose estimation module. Upon receiving the compensated current signal, it is processed based on the preset blood glucose value calculation model G=a×I+b×v+c (a=0.05, b=-2, c=3). For example, if the compensated current signal is 79.4nA and the blood glucose change rate is -0.05mmol / (L·s), the model is substituted to obtain G=7.07mmol / L, generating an accurate nighttime blood glucose monitoring result and outputting it through the LCD display.

[0082] Example 9

[0083] The only difference from Example 8 is the addition of steps for collecting, processing, and fusing physiological characteristic data, as detailed below:

[0084] While collecting blood glucose-related current signals, the MAX30102 heart rate sensor and MPU6050 accelerometer simultaneously collected the subject's nighttime heart rate and body movement data at a sampling frequency of 1Hz. The blood glucose-related current signals and physiological characteristic data (heart rate + body movement) were transmitted to the signal processing module in real time. Upon receiving the data, the signal processing module extracted temporal and physiological correlation features from the blood glucose-related current signals to generate fused feature data. It then quantized the physiological characteristic data, normalizing the heart rate data and thresholding the body movement data. The quantized physiological characteristic data was then correlated and integrated with the fused feature data to generate joint feature data containing temporal features, physiological correlation features, and quantized physiological features, which was transmitted to the signal identification module. The signal identification module analyzed the joint feature data based on a two-dimensional judgment rule: first, it determined whether the depression was short-term using the temporal dimension rule, and then corroborated this by the quantified physiological characteristic data. The identification result and the corresponding target signal segment were generated and transmitted to the depression compensation module. Subsequent signal compensation and blood glucose estimation followed the same steps as in Example 8, ultimately yielding accurate nighttime blood glucose monitoring results, which were then output.

[0085] The above are merely embodiments of the present invention. The invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are able to access all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A recess compensation system for a CGM sensor, characterized by, The application relates to a blood glucose monitoring system and method, comprising: a signal acquisition module for acquiring blood glucose related current signals of a subject at night and transmitting the blood glucose related current signals to a signal processing module in real time; a signal processing module for receiving the blood glucose related current signals, extracting time domain features and physiological correlation features of the blood glucose related current signals, obtaining fusion feature data of a blood glucose change rate, and transmitting the fusion feature data to a signal discrimination module; a signal discrimination module for receiving the fusion feature data, analyzing the fusion feature data based on a preset double-dimension judgment rule, determining whether the fusion feature data is short-term depression through a time domain dimension rule, verifying whether the fusion feature data is real hypoglycemia through a physiological dimension rule, obtaining a discrimination result and a corresponding target signal segment, and transmitting the discrimination result and the corresponding target signal segment to a depression compensation module; the short-term depression refers to a signal fluctuation with a signal drop duration of 5-60 seconds, a signal drop amplitude exceeding 1.5 times of a maximum change value of a current signal corresponding to normal blood glucose fluctuation of the subject at night in the recent three days, and a signal recovery to more than 90% of a baseline value before the depression within 30 seconds after the depression; a depression compensation module for receiving the discrimination result and the target signal segment, extracting signal baseline data before the depression in the target signal segment if the discrimination result is non-physiological depression, combining overall blood glucose signal dynamic trend data, correcting the target signal segment by using a linear extrapolation algorithm, and obtaining a compensated current signal; if the discrimination result is real hypoglycemia, retaining the original target signal segment as the compensated current signal, and transmitting the compensated current signal to a blood glucose estimation module; a blood glucose estimation module for receiving the compensated current signal, processing the compensated current signal based on a preset blood glucose value calculation model, obtaining an accurate night blood glucose monitoring result, and outputting the accurate night blood glucose monitoring result; the double-dimension judgment rule realizes the combined fusion of the time domain dimension and the physiological dimension through feature weight distribution: a fusion judgment value S is set, and a calculation formula of the fusion judgment value S is S = alpha * A + beta * B + gamma * C + delta * D, wherein A is a time domain feature parameter, specifically a ratio of a signal drop amplitude to a preset amplitude threshold; B is a duration parameter, specifically a ratio of a signal drop duration to 60 seconds, and B=1 if the duration exceeds 60 seconds; C is a recovery trend parameter, specifically a ratio of a signal recovery amplitude within 30 seconds after the depression to the depression amplitude; D is a physiological feature parameter, specifically a ratio of a blood glucose change rate to an upper limit of a physiological hypoglycemia rate threshold; alpha, beta, gamma and delta are weight coefficients; when S is less than or equal to a preset fusion judgment value, the discrimination result is determined to be non-physiological depression; and when S is greater than the preset fusion judgment value, the discrimination result is determined to be real hypoglycemia; when the depression compensation module extracts the signal baseline data before the depression in the target signal segment, a depression time point is determined in advance: high-frequency sampling analysis is conducted on the blood glucose related current signals in the fusion feature data transmitted by the signal processing module, and a continuous waveform sequence of time signal intensity is generated; Setting a collapse trigger threshold: when the signal strength of the consecutive M sampling points is greater than the preset instantaneous change threshold, and the signal strength of the M sampling point is greater than the mean value of the stable segment before the collapse by a first preset percentage, the time corresponding to the M sampling point is marked as the collapse starting time point t0; Tracking the waveform sequence to the signal strength of the consecutive N sampling points, the cumulative rise amplitude of the signal strength is greater than the second preset percentage of the total amplitude of the collapse, and the signal fluctuation amplitude of the subsequent K sampling points is less than the third percentage, the time corresponding to the N sampling point is marked as the collapse ending time point t1, and the collapse time point range is [t0, t1]; Extracting the signal data in the preset period before the collapse starting time point t0, and calculating the signal baseline data before the collapse by using a weighted average algorithm, the closer the sampling point to t0, the higher the weight, and the weight coefficient is distributed according to the linear increasing law.

2. The recess compensation system for a CGM sensor of claim 1, wherein, The signal acquisition module is also used to collect the physiological characteristic data of the subject at night, and transmit the physiological characteristic data to the signal processing module in real time; The signal processing module receives the physiological characteristic data, performs feature quantization processing on the physiological characteristic data, and integrates the quantized physiological characteristic data with the fusion feature data to generate joint feature data containing time domain features, physiological correlation features and quantized physiological features, and transmits the joint feature data to the signal discrimination module; The signal discrimination module analyzes the joint feature data based on a preset two-dimensional judgment rule: on the basis of the short-term collapse determined by the time domain dimension rule, whether it is a non-physiological collapse is verified according to the quantized physiological characteristic data.

3. The recess compensation system for a CGM sensor of claim 2, wherein, The signal processing module is also used to receive the collection time stamps of the blood glucose related current signal and the physiological characteristic data, and calculate the time difference Δt between the two: If the time difference Δt is less than or equal to the preset synchronization threshold, it is determined that the blood glucose related current signal and the physiological characteristic data are synchronous data collected at the same time, and the signal processing module transmits the integrated joint feature data to the signal discrimination module; If the time difference Δt is greater than the preset synchronization threshold, it is determined that the two are non-synchronous data, and the signal processing module only transmits the fusion feature data extracted based on the blood glucose related current signal to the signal discrimination module.

4. The recess compensation system for a CGM sensor of claim 3, wherein, The signal processing module is also used to obtain the collection time stamp T1 of the blood glucose related current signal and the collection time stamp T2 of the physiological characteristic data transmitted by the signal acquisition module, and calculate the time delay ΔT = |T1-T2| between the two; If ΔT=0, it is determined to be synchronous data without delay, the joint feature data is generated and transmitted to the signal discrimination module; If ΔT>0, it is determined to be non-synchronous data with time delay, and the signal processing module calls a historical data cache library to perform time calibration on the two types of signals based on the change trend of the historical data: For the blood glucose related current signal, a linear extrapolation method is used to generate a corrected current signal at the time of (T1+T2) / 2; For the physiological characteristic data, a moving average method is used to generate corrected physiological characteristic data at the time of (T1+T2) / 2. The time-matched synchronous data obtained after the correction is transmitted to a signal discrimination module as combined feature data.

5. The recess compensation system for a CGM sensor of claim 4, wherein, The signal processing module establishes a dynamic time synchronization mechanism through a reference time source, and the signal acquisition module records a reference time stamp T1' when collecting the blood glucose related current signal and records a reference time stamp T2' when collecting the physiological feature data; After receiving T1' and T2', the signal processing module calculates an initial time delay ΔT'=|T1'-T2'|, and simultaneously starts dynamic monitoring: every preset time interval, the ΔT' value of a group of synchronous data is extracted to construct a delay monitoring sequence, and the delay mean μ and standard deviation σ are calculated through a sliding window algorithm; When ΔT' is within the interval [μ-2σ, μ+2σ], it is determined that the time delay is within the normal range; when ΔT' is outside the interval, it is determined that the time delay is abnormal, triggering a synchronization calibration instruction.

6. A recess compensation method for a CGM sensor, characterized by, The method comprises the following steps: Step 1: collecting the blood glucose related current signal of the subject at night through the signal acquisition module, and transmitting the blood glucose related current signal to the signal processing module in real time; Step 2: the signal processing module receives the blood glucose related current signal, extracts the time domain features and physiological correlation features thereof, obtains the fusion feature data containing the blood glucose change rate, and then transmits the fusion feature data to the signal discrimination module; Step 3: the signal discrimination module receives the fusion feature data, analyzes it based on the preset two-dimensional judgment rule: first, determine whether it is a short-term depression through the time domain dimension rule, and then verify whether it is a true hypoglycemia through the physiological dimension rule, to obtain the discrimination result and the corresponding target signal segment, and transmit them to the depression compensation module; the short-term depression refers to a signal fluctuation with a duration of 5-60 seconds, a signal drop amplitude exceeding 1.5 times of the maximum change value of the current signal corresponding to the normal blood glucose fluctuation of the subject in the last 3 nights, and a signal recovery to more than 90% of the baseline value before the depression within 30 seconds after the depression; Step 4: the depression compensation module receives the discrimination result and the target signal segment, and if the discrimination result is non-physiological depression, extracts the signal baseline data before the depression in the target signal segment, combines the overall blood glucose signal dynamic trend data, and uses a linear extrapolation algorithm to correct the target signal segment to obtain the compensated current signal; If it is a true hypoglycemia, the original target signal segment is retained as the compensated current signal, and the compensated current signal is transmitted to the blood glucose estimation module; Step 5: the blood glucose estimation module receives the compensated current signal, processes it based on the preset blood glucose value calculation model, obtains an accurate night blood glucose monitoring result, and outputs it; The two-dimensional judgment rule realizes the combined fusion of the time domain dimension and the physiological dimension through feature weight allocation: A fusion judgment value S is set, and the calculation formula is S = α × A + β × B + γ × C + δ × D, wherein A is a time domain characteristic parameter, specifically a ratio of a signal drop amplitude to a preset amplitude threshold; B is a duration parameter, specifically a ratio of a signal drop duration to 60 seconds, and B = 1 if the duration exceeds 60 seconds; C is a recovery trend parameter, specifically a ratio of a signal rise amplitude within 30 seconds after a drop to the drop amplitude; D is a physiological characteristic parameter, specifically a ratio of a blood glucose change rate to an upper limit of a physiological hypoglycemia rate threshold; α, β, γ and δ are weight coefficients; when S ≤ a preset fusion judgment value, it is determined to be non-physiological sag; and when S > the preset fusion judgment value, it is determined to be true hypoglycemia. When the sag compensation module extracts the signal baseline data before the sag in the target signal segment, the sag time point is determined in advance: The blood glucose related current signal in the fusion feature data transmitted by the signal processing module is analyzed by high frequency sampling, and a continuous waveform sequence of time signal intensity is generated; A sag trigger threshold is set: when the signal intensity of the continuous M sampling points is greater than the drop amplitude of the previous sampling point by more than a preset instantaneous change threshold, and the signal intensity of the Mth sampling point is greater than the mean value of the stable segment before the sag by more than a first preset percentage, the time corresponding to the Mth sampling point is marked as the sag starting time point t0; When the waveform sequence is tracked to the rise amplitude of the continuous N sampling points, the cumulative value of the rise amplitude exceeds a second preset percentage of the total sag, and the signal fluctuation amplitude of the subsequent K sampling points is less than a third percentage, the time corresponding to the Nth sampling point is marked as the sag ending time point t1, and the sag time point range is [t0, t1]; The signal data in a preset period before the sag starting time point t0 is extracted, and the signal baseline data before the sag is calculated by using a weighted average algorithm, and the closer the sampling point is to t0, the higher the weight is, and the weight coefficients are distributed according to a linear increasing rule.

7. The sag compensation method for a CGM sensor according to claim 6, characterized in that, In step 1, the signal acquisition module also acquires physiological characteristic data of the subject at night, and transmits the physiological characteristic data to the signal processing module in real time; In step 2, after receiving the physiological characteristic data, the signal processing module performs feature quantization processing on the physiological characteristic data, integrates the quantized physiological characteristic data with the fusion feature data, generates joint feature data containing time domain features, physiological correlation features and quantized physiological features, and transmits the joint feature data to the signal discrimination module; In step 3, the signal discrimination module analyzes the joint feature data based on a preset two-dimensional judgment rule: on the basis of the rule of short-term sag in the time domain dimension, whether it is non-physiological sag is verified according to the quantized physiological characteristic data.

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