A digital processing system for multi-parameter detection of biocompatibility

Abstract

This invention discloses a digital processing system for biocompatibility multi-parameter detection, relating to the field of biomedical sensing technology. The system, through its digital processing unit, performs the following: physical interface integrity verification by matching the detection signal with response characteristics to perform gating decisions, preventing unreliable data from entering subsequent processing; under reliable interface conditions, it performs multi-dimensional data cross-contextual correction, intelligently correcting the main sensor signal deviation caused by instantaneous physiological environmental fluctuations using real-time readings from auxiliary sensors; and it performs prospective compensation for time-dependent decay by dynamically selecting the most suitable aging model from a multi-model database and performing feedback compensation. By establishing a progressive three-level assurance and compensation architecture from the physical layer, physiological layer to the time layer, this invention eliminates multi-source uncertainties and achieves highly reliable, highly consistent, and traceable physiological monitoring data throughout the sensor's entire lifecycle.

Description

Technical Field

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

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

[0003] First, the physical interface between the sensor and the organism is unstable. For example, slight displacement or detachment of the sensor probe, or biological contamination due to tissue reaction, can cause severe distortion of the raw signal. Existing technologies typically lack the ability to proactively and in real-time diagnose such physical layer faults, leading the system to potentially perform subsequent processing based on erroneous raw data without the system's knowledge.

[0004] Second, a living organism is a complex and dynamically changing system. Instantaneous fluctuations in the physiological environment, such as changes in local tissue fluid pH due to exercise, changes in body temperature, or interference from certain endogenous substances, can all affect the electrochemical response of sensors. Existing technologies often employ simple filtering or fixed calibration factors, making it difficult to accurately correct for these complex and contextualized interferences in real time.

[0005] Third, all sensors inevitably experience performance degradation. As usage time increases, sensor materials age, and their sensitivity gradually decreases. Existing compensation methods mostly employ fixed aging models calibrated under ideal laboratory conditions, which cannot adapt to the unique and complex working environment experienced by each individual sensor. This leads to increasingly larger data deviations in the later stages of sensor use. Summary of the Invention

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

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

[0008] A multimodal sensing interface for contact with living organisms to sense signals, including a primary functional sensor and an auxiliary status sensor;

[0009] An analog-to-digital conversion front end is used to process signals from the multimodal sensing interface and digitize them;

[0010] The digital processing unit executes physical interface integrity verification logic. This logic 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 the matching result of the response characteristics and the preset interface reliability model.

[0011] Based on the gating decision, if the interface is reliable, the subsequent data processing logic is authorized to be executed; if the interface is abnormal, the current data processing cycle is terminated.

[0012] Under the condition that the interface is reliable, a multi-dimensional data cross-contextual correction logic is executed. This logic synchronously acquires the original readings of the main function sensor and the auxiliary state sensor, and corrects the original readings of the main function sensor based on the preset cross-parameter influence model and the real-time readings of the auxiliary state sensor to generate contextualized data.

[0013] After obtaining the context-filtered data, a forward-looking compensation logic for time-dependent decay is executed. This logic selects the currently applicable decay model from a preset multi-model decay database, calculates a forward-looking compensation factor based on the system's cumulative running time and the currently applicable decay model, and applies the forward-looking compensation factor to the context-filtered data to generate the final output data.

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

[0015] By using physical interface integrity verification and gating decision-making mechanisms, this invention can prevent unreliable data caused by sensor physical failures (such as improper wearing) from entering the subsequent processing flow, thus avoiding dangerous decisions based on erroneous data. It is especially suitable for high-risk scenarios such as closed-loop drug infusion.

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

[0017] Through a forward-looking compensation phase for time-dependent decay, and particularly its built-in adaptive feedback loop that dynamically selects the aging model based on historical operating conditions, this invention provides personalized long-term compensation for each sensor. This ensures that the measurement standards of the device remain highly consistent from day one to day one, solving the core problem in existing technologies where sensor data becomes increasingly inaccurate over time.

[0018] The three-level verification process of this invention is clear and transparent, and can output diagnostic information including error codes and correction factors. This makes every data point verifiable, allowing users and doctors not only to see the results but also to understand how those results were obtained through layers of verification and correction, greatly enhancing the trust in the data and its application value in clinical decision-making. Attached Figure Description

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

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

[0021] Therefore, the overall technical solution adopted in this invention is as follows: First, a digital processing unit within the system executes physical interface verification logic to prevent subsequent data processing errors caused by abnormal physical connections from the source. Second, based on confirming the reliability of the physical connection, this unit executes contextual correction logic to eliminate interference caused by instantaneous physiological environmental fluctuations. Finally, this unit executes forward-looking 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 lifecycle.

[0022] The above technical solutions will now be described in detail with reference to the accompanying drawings and specific embodiments to provide a better understanding of them. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments used only to explain the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Furthermore, it should be noted that, for ease of description, only the parts related to the present invention are shown in the drawings, not all of them.

[0023] like Figure 1 The diagram illustrates a digital processing system architecture for biocompatibility multi-parameter detection. In this embodiment, the system is specifically implemented as a smart patch capable of continuously monitoring blood glucose 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 contact with living organisms to sense signals, including a primary functional sensor and an auxiliary status sensor;

[0026] An analog-to-digital conversion front end is used to process signals from the multimodal sensing interface and digitize them;

[0027] The digital processing unit executes physical interface integrity verification logic. This logic 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 the matching result of the response characteristics and the preset interface reliability model.

[0028] Based on the gating decision, if the interface is reliable, the subsequent data processing logic is authorized to be executed; if the interface is abnormal, the current data processing cycle is terminated.

[0029] Under the condition that the interface is reliable, a multi-dimensional data cross-contextual correction logic is executed. This logic synchronously acquires the original readings of the main function sensor and the auxiliary state sensor, and corrects the original readings of the main function sensor based on the preset cross-parameter influence model and the real-time readings of the auxiliary state sensor to generate contextualized data.

[0030] After obtaining the context-filtered data, a forward-looking compensation logic for time-dependent decay is executed. This logic selects the currently applicable decay model from a preset multi-model decay database, calculates a forward-looking compensation factor based on the system's cumulative running time and the currently applicable decay model, and applies the forward-looking compensation factor to the context-filtered data to generate the final output data.

[0031] In one specific embodiment, the analog-to-digital conversion front end includes a signal excitation and measurement module; when the digital processing unit executes the physical interface integrity verification logic, it is configured to instruct the signal excitation and measurement module to apply one of a multi-frequency AC 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 characteristics.

[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 at the beginning of each data acquisition cycle to instruct the signal excitation and measurement module within its connected analog-to-digital converter front-end (e.g., an ADuCM355 chip with built-in potentiostat and impedance spectroscopy measurement functions) to apply a multi-frequency alternating current signal between the working electrode and the counter electrode to acquire an electrochemical impedance spectroscopy 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 this template; if the deviation is less than a preset threshold, the interface is deemed "reliable"; otherwise, the interface is deemed "abnormal," and the current blood glucose calculation is terminated.

[0033] In a preferred embodiment, the digital processing unit is further configured to insert and execute electrode quiescent recovery logic after executing the physical interface integrity verification logic and before executing the multidimensional data cross-contextualization correction logic. This logic is used to restore the electrochemical state of the sensing interface to a stable baseline before raw readings are acquired after a physical detection signal is applied to the sensing interface.

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

[0035] In another preferred embodiment, when the digital processing unit executes the physical interface integrity verification logic, it further includes sub-logic for distinguishing the time scale of the response features based on the matching logic between the response features and the interface reliability model; wherein, the digital processing unit is configured to use changes identified by the sub-logic as fast time scales to determine the result of the gating decision as an interface anomaly, and to use 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 features and the model, but also to analyze the rate of change of that difference over time. If its internal logic detects a change on a rapid timescale, it determines it as an acute physical fault, and the gating decision is "interface anomaly." If it detects a change on a slow timescale, it determines it as normal aging and is configured to update the "healthy connection template" in memory using the most recent response feature data. This configuration allows the system's physical verification capability to adapt to the normal aging of the sensor.

[0037] Furthermore, when executing the multidimensional data cross-contextualization correction logic, the digital processing unit is configured as follows:

[0038] The digital readings of all the main functional sensors and the auxiliary status sensors are synchronously acquired from the analog-to-digital conversion front end;

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

[0040] The dynamic correction factor is applied to the raw readings of the main functional sensor to obtain the context-filtered data.

[0041] Specifically, after confirming the reliability of the physical connection, the digital processing unit is configured to synchronously acquire the digital 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) from a lookup table stored internally as a cross-parameter influence model based on the pH-sensitive electrode readings, and apply this factor to the current value to obtain context-filtered data.

[0042] Finally, when executing the forward-looking compensation logic for the time-dependent decay, the digital processing unit is configured as follows:

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

[0044] Substitute the cumulative running time of the system into the currently applicable attenuation model to calculate the current sensitivity attenuation ratio of the sensor;

[0045] The prospective compensation factor is obtained based on the sensitivity attenuation ratio.

[0046] The forward-looking compensation factor is applied to the context-purified data to complete data standardization and generate the final output data.

[0047] In one implementation, the digital processing unit is configured to: select the currently applicable attenuation model from the multi-model attenuation database.

[0048] Based on the historical data of the auxiliary state sensor collected by the multi-dimensional data cross-contextual correction logic, the current working state of the system is determined;

[0049] Based on the operating state, an attenuation model that matches the operating state is dynamically selected from the multi-model attenuation database as the currently applicable attenuation model.

[0050] Specifically, the digital processing unit stores a multi-model attenuation database in its flash memory. This unit is configured to periodically analyze historical auxiliary sensor data (such as average pH value, EIS variation trend, etc.) stored in its memory over a past period to determine the current operating status and select the best-matching 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 cumulative runtime provided by an internal clock and apply it to the context-filtered data.

[0051] In one embodiment, when the digital processing unit dynamically selects from the multi-model decay database according to the operating state, it is further configured to: if the currently identified operating state does not have a directly matching decay model in the database, generate an aging model for use by performing weighted interpolation on two or more of the closest aging models.

[0052] Specifically, for example, when the judgment logic of the digital processing unit determines that the current working state is between "normal" and "mild inflammation," it is configured to execute a weighted interpolation algorithm to linearly combine the parameters of the "normal decay model" and the "inflammatory accelerated decay model" based on the degree of deviation of the current state, thereby generating a customized compensation model specific to the current cycle. This configuration enables continuous, smooth, and adaptive adjustment of the compensation, resulting in higher accuracy.

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

[0054] The final output data, together with the diagnostic information generated during the execution of the processing logic, is combined into a complete data frame for output.

[0055] The diagnostic information includes: error codes indicating physical layer faults, dynamic correction factors applied by the multidimensional data cross-contextualization correction logic, and forward-looking compensation factors applied by the time-dependent decay forward-looking compensation logic.

[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 a status code, pH correction factor (k_ph), and aging compensation factor (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 this 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. It enables the provision of highly reliable, highly consistent, and traceable physiological monitoring data throughout the entire life cycle, demonstrating significant technical effects and application value.

[0058] It should be understood that the embodiments disclosed in this invention and the above description enable those skilled in the art to implement this invention. However, this 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 foregoing embodiments or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this invention, and should all be included within the protection scope of this invention.

Claims

1. A digital processing system for biocompatible multiparameter detection, characterized in that, The system comprises: a multi-modal sensing interface for contacting a living body for signal sensing, and comprising a primary functional sensor and an auxiliary status sensor; an analog-to-digital conversion front-end for processing and digitizing signals from the multi-modal sensing interface; a digital processing unit executing a physical interface integrity verification logic, which captures response features of the multi-modal sensing interface by controlling the analog-to-digital conversion front-end to apply preset physical probing signals to the multi-modal sensing interface, and makes a gating decision based on matching results of the response features against a preset interface reliability model; according to the gating decision, if the interface is reliable, subsequent data processing logic is authorized to be executed, and if the interface is abnormal, the current data processing cycle is aborted; under the condition that the interface is reliable, a multi-dimensional data cross-scenario correction logic is executed, which synchronously acquires raw readings of the primary functional sensor and the auxiliary status sensor, and corrects the raw readings of the primary functional sensor based on a preset cross-parameter influence model and real-time readings of the auxiliary status sensor, to generate scenario-corrected data; after obtaining the scenario-corrected data, a time-dependent decay forward-looking compensation logic is executed, which selects a currently applicable decay model from a preset multi-model decay database, and calculates a forward-looking compensation factor according to a cumulative running time of the system and the currently applicable decay model, and applies the forward-looking compensation factor to the scenario-corrected data to generate final output data.

2. A digital processing system for biocompatible multiparameter detection as claimed in claim 1, characterized in that, The analog-to-digital conversion front-end comprises 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 probing signal, and to capture electrochemical impedance spectroscopy, dielectric spectroscopy or thermal dissipation curves as the response features.

3. A digital processing system for biocompatible multiparameter sensing as defined in claim 1, characterized in that, When executing the multi-dimensional data cross-scenario correction logic, the digital processing unit is configured to: synchronously acquire digitized readings of all the primary functional sensor and the auxiliary status sensor from the analog-to-digital conversion front-end; substitute real-time digitized readings of the auxiliary status sensor as input into the cross-parameter influence model to calculate a dynamic correction factor; apply the dynamic correction factor to the raw readings of the primary functional sensor to obtain the scenario-corrected data.

4. A digital processing system for biocompatible multiparameter sensing as defined in claim 1, characterized in that, When executing the time-dependent decay forward-looking compensation logic, the digital processing unit is configured to: select the currently applicable decay model from the multi-model decay database; substitute the cumulative running time of the system into the currently applicable decay model to calculate a current sensitivity decay ratio of the sensor; obtain the forward-looking compensation factor according to the sensitivity decay ratio; apply the forward-looking compensation factor to the scenario-corrected data to complete data standardization and generate the final output data.

5. A digital processing system for biocompatible multiparameter detection as claimed in claim 4, characterized in that, When selecting the currently applicable decay model from the multi-model decay database, the digital processing unit is configured to: determining a current working state of the system according to historical data of the auxiliary state sensors collected by the multi-dimensional data cross-scenario correction logic; dynamically selecting a decay model matching the working state from the multi-model decay database as the current applicable decay model according to the working state.

6. A digital processing system for biocompatible multiparameter sensing as defined in claim 1, characterized by The digital processing unit is further configured to: output the final output data together with diagnostic information generated in the process of executing the processing logic to form a complete data frame; The diagnostic information includes error codes indicating physical layer faults, dynamic correction factors applied by the multi-dimensional data cross-scenario correction logic, and the prospective compensation factors applied by the time-dependent decay prospective compensation logic.

7. A digital processing system for biocompatible multiparameter sensing as defined in claim 1, wherein, The digital processing unit is further configured to insert and execute electrode quiescence recovery logic before executing the multi-dimensional data cross-scenario correction logic after executing the physical interface integrity verification logic, which 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 biocompatible multiparameter sensing as defined in claim 1, characterized by The digital processing unit, when executing the physical interface integrity verification logic, further includes sub-logic for distinguishing the time variation scale of the response characteristics based on the matching logic of the response characteristics and the interface reliability model; wherein the digital processing unit is configured to use the change identified by the sub-logic as a fast time scale to determine that the result of the gating decision is an interface anomaly, and use the change identified as a slow time scale to update the interface reliability model to adapt to normal aging of the sensor.

9. A digital processing system for biocompatible multiparameter sensing as defined in claim 5, characterized by The digital processing unit, when dynamically selecting from the multi-model decay database according to the working state, is further configured to generate an aging model for use by weighted interpolation of two or more closest aging models if the currently identified working state has no directly matching decay model in the database.

Images