Digital processing system for biocompatibility multi-parameter detection

Abstract

The invention discloses a digital processing system for biocompatibility multi-parameter detection, and relates to the technical field of biomedical sensing, and the system executes, through a digital processing unit, physical interface integrity verification, gating decision making through matching of a detection signal and a response characteristic, and multi-parameter detection. Unreliable data is prevented from entering subsequent processing from the source; under the condition that an interface is reliable, multi-dimensional data cross scenario correction is executed, and main sensor signal deviation caused by instantaneous physiological environment fluctuation is intelligently corrected through real-time reading of an auxiliary sensor; and performing prospective compensation of time-dependent attenuation, dynamically selecting the most suitable aging model from the multi-model database, and performing feedback compensation. By establishing a progressive three-level confirmation and compensation architecture from a physical layer, a physiological layer to a time layer, multi-source uncertainty factors are eliminated, and physiological monitoring data with high reliability, high consistency and traceable process can be provided in the whole life cycle of the sensor.

Description

Technical Field

[0001] The present invention relates to the field of biomedical sensing technology, and in particular to a digital processing system for biocompatibility multi-parameter detection. Background Art

[0002] In existing technologies, implantable or wearable devices used for biocompatibility multi-parameter detection, such as continuous glucose monitoring systems, generally have data reliability issues during long-term use. These issues mainly stem from the following aspects:

[0003] First, the physical interface between the sensor and the organism is unstable. For example, even slight displacement or detachment of the sensor probe, or biocontamination due to tissue reactions, can severely distort the original signal. Existing technologies often lack the ability to proactively and in real time diagnose such physical layer failures, potentially leading to unwitting subsequent processing based on erroneous raw data.

[0004] Second, the biological body is a complex, dynamically changing system. Transient physiological environmental fluctuations, such as changes in local tissue pH due to exercise, changes in body temperature, or interference from certain endogenous substances, can affect the sensor's electrochemical response. Existing technologies often use simple filtering or fixed calibration factors, making it difficult to accurately correct for these complex, context-dependent interferences in real time.

[0005] Third, all sensors inevitably experience performance degradation. Over time, sensor materials age, and their sensitivity gradually decreases. Existing compensation methods often rely on fixed aging models calibrated under ideal laboratory conditions. These methods are unable to adapt to the unique and complex operating environment experienced by sensors in each individual. This results in increasing data deviations during the middle and later stages of sensor use. Summary of the Invention

[0006] The present invention provides a digital processing system for biocompatibility multi-parameter detection, aiming to solve the technical problems in existing implantable or wearable biosensor systems, such as low reliability and poor consistency of output data caused by multiple uncertainties such as unstable physical connections, transient physiological environment interference, and sensor performance degradation.

[0007] In view of the above problems, the present invention provides a digital processing system for biocompatibility multi-parameter detection, the system comprising:

[0008] A multimodal sensing interface for contacting a biological body for signal sensing, comprising a primary function sensor and an auxiliary state sensor;

[0009] an analog-to-digital conversion front end for processing and digitizing the signal from the multimodal sensing interface;

[0010] a digital processing unit executing physical interface integrity verification logic, which controls the analog-to-digital conversion front end to apply a preset physical detection signal to the multimodal sensing interface, captures its response characteristics, and makes a gating decision based on a match between the response characteristics and a preset interface reliability model;

[0011] According to the result of the gating decision, if the interface is reliable, the subsequent data processing logic is authorized to execute; if the interface is abnormal, the current data processing cycle is terminated;

[0012] Under the condition that the interface is reliable, executing multi-dimensional data cross-contextualization correction logic, the logic synchronously obtains raw readings of the primary function sensor and the auxiliary status sensor, and corrects the raw readings of the primary function sensor based on a preset cross-parameter impact model and the real-time readings of the auxiliary status sensor to generate contextualized cleansed data;

[0013] After obtaining the contextually cleansed data, a forward-looking compensation logic for time-dependent attenuation is executed. This logic selects a currently applicable attenuation model from a preset multi-model attenuation database, and calculates a forward-looking compensation factor based on the system's cumulative operating time and the currently applicable attenuation model. The forward-looking compensation factor is applied to the contextually cleansed data to generate final output data.

[0014] The technical solution provided by this application has at least the following technical effects or advantages:

[0015] Through physical interface integrity verification and gating decision-making mechanisms, the present invention can fundamentally prevent unreliable data generated by sensor physical failures (such as improper wearing) from entering subsequent processing flows, avoiding dangerous decisions based on erroneous data. It is particularly suitable for high-risk scenarios such as closed-loop drug infusion.

[0016] Improved real-time accuracy of data: Through the multi-dimensional data cross-scenario correction stage, the present invention can use auxiliary parameters collected in real time to intelligently compensate for measurement deviations caused by the user's instantaneous physiological state (such as exercise, inflammation), so that the output data can more realistically reflect the user's physiological condition in different scenarios.

[0017] By implementing a proactive compensation phase for time-dependent decay, and specifically a built-in adaptive feedback loop that dynamically selects an aging model based on historical operating conditions, the present invention provides personalized long-term compensation for each sensor. This ensures highly consistent measurement standards from the first day to the last, resolving the core pain point of existing sensors, where data becomes increasingly inaccurate over time.

[0018] The three-level assurance process of this invention is clear and transparent, and it can output diagnostic information including error codes and correction factors. This makes every data point traceable. Users and doctors can not only see the results, but also understand how the results were obtained through multiple levels of verification and correction, greatly enhancing trust in the data and its application value in clinical decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is an architecture diagram of a digital processing system for multi-parameter biocompatibility detection according to the present invention. DETAILED DESCRIPTION

[0020] The present invention relates to a digital processing system for biocompatibility multi-parameter detection, aiming to solve the technical problems in existing implantable or wearable biosensor systems, such as low reliability and poor consistency of output data caused by multiple uncertainties such as unstable physical connections, transient physiological environment interference, and sensor performance degradation.

[0021] To this end, the overall technical solution adopted by this invention is as follows: First, a digital processing unit within the system executes physical interface verification logic to eliminate subsequent data processing errors caused by physical connection anomalies at the source. Second, based on the confirmation of the reliability of the physical connection, the unit executes contextual correction logic to remove interference caused by transient physiological environmental fluctuations. Finally, the unit implements proactive compensation logic to dynamically compensate for long-term performance degradation caused by sensor aging, thereby ensuring that the data output by the system has extremely high confidence and consistency throughout its entire life cycle.

[0022] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0023] like Figure 1 The digital processing system architecture for biocompatibility multi-parameter detection is shown in the figure. In this embodiment, the system is embodied as a smart patch that can continuously monitor blood sugar for 7 days. The system includes:

[0024] An adaptive data assurance system for multi-parameter biocompatibility detection, the system comprising:

[0025] A multimodal sensing interface for contacting a biological body for signal sensing, comprising a primary function sensor and an auxiliary state sensor;

[0026] an analog-to-digital conversion front end for processing and digitizing the signal from the multimodal sensing interface;

[0027] a digital processing unit executing physical interface integrity verification logic, which controls the analog-to-digital conversion front end to apply a preset physical detection signal to the multimodal sensing interface, captures its response characteristics, and makes a gating decision based on a match between the response characteristics and a preset interface reliability model;

[0028] According to the result of the gating decision, if the interface is reliable, the subsequent data processing logic is authorized to execute; if the interface is abnormal, the current data processing cycle is terminated;

[0029] Under the condition that the interface is reliable, executing multi-dimensional data cross-contextualization correction logic, the logic synchronously obtains raw readings of the primary function sensor and the auxiliary status sensor, and corrects the raw readings of the primary function sensor based on a preset cross-parameter impact model and the real-time readings of the auxiliary status sensor to generate contextualized cleansed data;

[0030] After obtaining the contextually cleansed data, a forward-looking compensation logic for time-dependent attenuation is executed. This logic selects a currently applicable attenuation model from a preset multi-model attenuation database, and calculates a forward-looking compensation factor based on the system's cumulative operating time and the currently applicable attenuation model. The forward-looking compensation factor is applied to the contextually cleansed data to generate final output data.

[0031] In a specific embodiment, the analog-to-digital conversion front end includes a signal excitation and measurement module; when executing the physical interface integrity verification logic, the digital processing unit is configured to instruct the signal excitation and measurement module to apply one of the multi-frequency alternating current signals, voltage step signals or thermal pulse signals as the physical detection signal, and capture the electrochemical impedance spectrum, dielectric spectrum or thermal dissipation curve as the response characteristic.

[0032] Specifically, in this blood glucose monitoring patch system, the digital processing unit (e.g., a microcontroller with an ARM Cortex-M4 core) is configured to instruct the signal excitation and measurement module in the analog-to-digital conversion front end (e.g., an ADuCM355 chip with a built-in potentiostat and impedance spectrum measurement function) connected to it at the beginning of each data acquisition cycle to apply a multi-frequency AC signal between the working electrode and the counter electrode to obtain an electrochemical impedance spectrum. The interface reliability model is a "healthy connection template" stored in the flash memory of the digital processing unit. The digital processing unit is configured to compare the currently measured response characteristics with the template. If the deviation is less than a preset threshold, it is determined that the "interface is reliable", otherwise it is determined that the "interface is abnormal" and the current blood glucose calculation is terminated.

[0033] In a preferred embodiment, the digital processing unit is also configured to insert and execute electrode silence recovery logic after executing the physical interface integrity verification logic and before executing the multi-dimensional data cross-contextualization correction logic. This logic is used to restore the electrochemical state of the sensing interface to a stable baseline before obtaining the original reading after applying a physical detection signal to the sensing interface.

[0034] Specifically, after completing the electrochemical impedance spectroscopy scan, the digital processing unit is configured to control the analog-to-digital conversion front end to stop applying any potential or current to the electrodes for a period of time (e.g., 15 to 30 seconds). This logic ensures that the DC current signal used to measure blood glucose is measured on an undisturbed, stable electrochemical baseline, thereby improving the accuracy of the original reading.

[0035] In another preferred embodiment, when the digital processing unit executes the physical interface integrity verification logic, it also includes a sub-logic for distinguishing the time change scale of the response characteristic based on the matching logic between the response characteristic and the interface reliability model; wherein the digital processing unit is configured to use the changes identified as fast time scales by the sub-logic to determine that the result of the gating decision is an interface abnormality, and to use the changes identified as slow time scales to update the interface reliability model to adapt to the normal aging of the sensor.

[0036] Specifically, the digital processing unit is configured not only to compare the difference between the current response signature and the model, but also to analyze the rate of change of this difference over time. If its internal logic identifies a change on a rapid timescale, it is determined to be an acute physical fault, and the gating decision is "interface anomaly." If a change on a slow timescale is identified, it is determined to be normal aging, and the unit is configured to use the most recent response signature data to update the "healthy connection template" in memory. This configuration enables the system's physical verification capabilities to adapt to normal sensor aging.

[0037] Furthermore, when executing the multi-dimensional data cross-contextualization correction logic, the digital processing unit is configured to:

[0038] synchronously acquiring digital readings of all the primary function sensors and the auxiliary status sensors from the analog-to-digital conversion front end;

[0039] The real-time digital reading of the auxiliary state sensor is used as input and substituted into the cross-parameter influence model to calculate a dynamic correction factor;

[0040] The dynamic correction factor is applied to the raw readings of the primary function sensor to obtain the contextualized cleansed data.

[0041] Specifically, after confirming the physical connection is secure, the digital processing unit is configured to synchronously acquire the digitized readings of each sensor from the analog-to-digital conversion front end. It is configured to first perform background current subtraction, then calculate a dynamic correction factor (k_ph) based on the pH-sensitive electrode readings from an internally stored lookup table that serves as a cross-parameter influence model, and apply this correction factor to the current value to obtain contextualized and purified data.

[0042] Finally, the digital processing unit, when executing the look-ahead compensation logic for the time-dependent decay, is configured to:

[0043] Selecting the currently applicable attenuation model from the multi-model attenuation database;

[0044] Substituting the accumulated operating time of the system into the currently applicable attenuation model to calculate the current sensitivity attenuation ratio of the sensor;

[0045] Obtaining the forward-looking compensation factor according to the sensitivity attenuation ratio;

[0046] The forward-looking compensation factor is applied to the contextually cleansed data to complete data normalization and generate the final output data.

[0047] In one embodiment, when selecting the currently applicable attenuation model from the multi-model attenuation database, the digital processing unit is configured to:

[0048] Determining the current working state of the system based on the historical data of the auxiliary state sensor collected by the multi-dimensional data cross-contextualization correction logic;

[0049] According to the working state, an attenuation model matching the working state is dynamically selected from the multi-model attenuation database as the currently applicable attenuation model.

[0050] Specifically, the digital processing unit's flash memory stores a multi-model attenuation database. The unit is configured to periodically analyze historical auxiliary sensor data (such as average pH values ​​and EIS trends) stored in its memory over a period of time to determine the current operating status and select the most suitable attenuation model from the database. After selecting a model, the unit is configured to calculate a forward-looking compensation factor (k_aging) based on the accumulated operating time provided by an internal clock and apply it to the contextualized, cleansed data.

[0051] In one embodiment, when the digital processing unit dynamically selects from the multi-model attenuation database based on the working state, it is also configured to: if the currently identified working state has no directly matching attenuation model in the database, generate an aging model for use by weighted interpolation of two or more closest aging models.

[0052] Specifically, for example, when the digital processing unit's judgment logic determines that the current operating state is between "normal" and "mild inflammation," it is configured to execute a weighted interpolation algorithm. Based on the degree of deviation from the current state, the parameters of the "normal attenuation model" and the "accelerated inflammation attenuation model" are linearly combined to generate a customized compensation model specific to the current cycle. This configuration enables continuous, smooth, and adaptive compensation with higher accuracy.

[0053] In one embodiment, the digital processing unit is further configured to:

[0054] The final output data and the diagnostic information generated during the execution of the processing logic are combined into a complete data frame for output;

[0055] The diagnostic information includes an error code indicating a physical layer fault, a dynamic correction factor applied by the multi-dimensional data cross-contextualization correction logic, and the proactive compensation factor applied by the proactive compensation logic for time-dependent decay.

[0056] Specifically, after completing all calculations, the digital processing unit is configured to construct a structured data frame. This frame contains not only the final blood glucose concentration value and timestamp, but also the status code, pH correction coefficient (k_ph), and aging compensation coefficient (k_aging) generated during the processing. This configuration makes the system's output data traceable, greatly enhancing data transparency and clinical application value.

[0057] In summary, the adaptive data assurance system provided by the present invention systematically solves the multi-source uncertainty problem in biosensor data processing through the collaborative work of its internal functional units and a set of logically rigorous algorithm processes executed by the digital processing unit, and realizes the provision of highly reliable, highly consistent and process-traceable physiological monitoring data throughout the entire life cycle, with significant technical effects and application value.

[0058] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. A digital processing system for multi-parameter biocompatibility detection, characterized in that: The system comprises: A multimodal sensing interface for contacting a biological body for signal sensing, comprising a primary function sensor and an auxiliary state sensor; an analog-to-digital conversion front end for processing and digitizing the signal from the multimodal sensing interface; a digital processing unit executing physical interface integrity verification logic, which controls the analog-to-digital conversion front end to apply a preset physical detection signal to the multimodal sensing interface, captures its response characteristics, and makes a gating decision based on a match between the response characteristics and a preset interface reliability model; According to the result of the gating decision, if the interface is reliable, the subsequent data processing logic is authorized to execute; if the interface is abnormal, the current data processing cycle is terminated; Under the condition that the interface is reliable, executing multi-dimensional data cross-contextualization correction logic, the logic synchronously obtains raw readings of the primary function sensor and the auxiliary status sensor, and corrects the raw readings of the primary function sensor based on a preset cross-parameter impact model and the real-time readings of the auxiliary status sensor to generate contextualized cleansed data; After obtaining the contextually cleansed data, a forward-looking compensation logic for time-dependent attenuation is executed. This logic selects a currently applicable attenuation model from a preset multi-model attenuation database, and calculates a forward-looking compensation factor based on the system's cumulative operating time and the currently applicable attenuation model. The forward-looking compensation factor is applied to the contextually cleansed data to generate final output data.

2. A digital processing system for biocompatibility multi-parameter detection according to claim 1, characterized in that: The analog-to-digital conversion front end includes a signal excitation and measurement module; when executing the physical interface integrity verification logic, the digital processing unit is configured to instruct the signal excitation and measurement module to apply one of a multi-frequency alternating current signal, a voltage step signal, or a thermal pulse signal as the physical detection signal, and to capture an electrochemical impedance spectrum, a dielectric spectrum, or a thermal dissipation curve as the response characteristic.

3. A digital processing system for biocompatibility multi-parameter detection according to claim 1, characterized in that: When executing the multi-dimensional data cross-contextualization correction logic, the digital processing unit is configured to: synchronously acquiring digital readings of all the primary function sensors and the auxiliary status sensors from the analog-to-digital conversion front end; The real-time digital reading of the auxiliary state sensor is used as input and substituted into the cross-parameter influence model to calculate a dynamic correction factor; The dynamic correction factor is applied to the raw readings of the primary function sensor to obtain the contextualized cleansed data.

4. A digital processing system for biocompatibility multi-parameter detection according to claim 1, characterized in that: The digital processing unit, when executing the look-ahead compensation logic for the time-dependent decay, is configured to: Selecting the currently applicable attenuation model from the multi-model attenuation database; Substituting the accumulated operating time of the system into the currently applicable attenuation model to calculate the current sensitivity attenuation ratio of the sensor; Obtaining the forward-looking compensation factor according to the sensitivity attenuation ratio; The forward-looking compensation factor is applied to the contextually cleansed data to complete data normalization and generate the final output data.

5. A digital processing system for biocompatibility multi-parameter detection according to claim 4, characterized in that: When selecting the currently applicable attenuation model from the multi-model attenuation database, the digital processing unit is configured to: Determining the current working state of the system based on the historical data of the auxiliary state sensor collected by the multi-dimensional data cross-contextualization correction logic; According to the working state, an attenuation model matching the working state is dynamically selected from the multi-model attenuation database as the currently applicable attenuation model.

6. A digital processing system for biocompatibility multi-parameter detection according to claim 1, characterized in that: The digital processing unit is further configured to: The final output data and the diagnostic information generated during the execution of the processing logic are combined into a complete data frame for output; The diagnostic information includes an error code indicating a physical layer fault, a dynamic correction factor applied by the multi-dimensional data cross-contextualization correction logic, and the proactive compensation factor applied by the proactive compensation logic for time-dependent decay.

7. A digital processing system for biocompatibility multi-parameter detection according to claim 1, characterized in that: The digital processing unit is also configured to insert and execute electrode silence recovery logic after executing the physical interface integrity verification logic and before executing the multi-dimensional data cross-contextualization correction logic. This logic is used to restore the electrochemical state of the sensing interface to a stable baseline before obtaining the original reading after applying a physical detection signal to the sensing interface.

8. A digital processing system for biocompatibility multi-parameter detection according to claim 1, characterized in that: When executing the physical interface integrity verification logic, the digital processing unit also includes a sub-logic for distinguishing the time change scale of the response feature based on the matching logic between the response feature and the interface reliability model; wherein the digital processing unit is configured to use the changes identified by the sub-logic as fast time scales to determine that the result of the gating decision is an interface abnormality, and to use the changes identified as slow time scales to update the interface reliability model to adapt to the normal aging of the sensor.

9. A digital processing system for biocompatibility multi-parameter detection according to claim 5, characterized in that: When dynamically selecting from the multi-model attenuation database based on the working state, the digital processing unit is further configured to: if the currently identified working state has no directly matching attenuation model in the database, generate an aging model for use by weighted interpolation of two or more closest aging models.

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