System and method for detecting sensor compression of continuous blood glucose monitoring (CGM) sensor

Abstract

Embodiments may relate to systems and methods for real-time automatic detection of sensor pressure in continuous blood glucose monitoring. A system may include at least one sensor and at least one processor in communication with the at least one sensor. The at least one processor is programmed or configured to cause the processor to: retrieve first measurement data, the first measurement data comprising at least one time series of blood glucose (BG) measurements measured by the at least one sensor while not being compressed; receiving, from the at least one sensor, second measurement data comprising at least one BG measurement; determining a gap value between BG measurements based on the first measurement data and the second measurement data; and generating a signal output indicating that the at least one sensor is compressed based on the value of the gap between the BG measurements exceeding a predefined threshold.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application is related to and claims priority from U.S. Provisional Patent Application No. 63 / 421,883, filed on November 2, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0003] One aspect of the embodiments generally relates to pharmaceutical devices and medical devices for monitoring blood glucose levels in the treatment of diabetes and other metabolic conditions, including but not limited to type 1 and type 2 diabetes, type 2 (T1D, T2D), latent autoimmune diabetes in adults (LADA), postprandial or reactive hyperglycemia, or insulin resistance. The embodiments relate to systems and methods for detecting compression of a continuous glucose monitoring (CGM) sensor and detecting compression artifacts in blood glucose (BG) measurements. Background Art

[0004] Advances in CGM devices made in the past have contributed to the treatment of diabetes and have led to some contemporary closed-loop control systems (e.g., artificial pancreas). Despite the advances in CGM, CGM sensors may be susceptible to compression artifacts (e.g., pressure-induced sensor attenuation (PISA)). Compression artifacts may appear in the measurement data when the sensor is squeezed (e.g., compressed) while collecting measurements. For example, compression artifacts may appear when a subject with a sensor attached sleeps with the sensor inserted (e.g., resting their head on their arm). Compression artifacts may be characterized by a rapid drop in the magnitude of the sensor measurement (e.g., a low reading) followed by a final recovery of the sensor measurement (e.g., a normal reading). Such a drop in sensor measurements may lead to false hypoglycemia alarms, hypoglycemia suspension systems, or insulin cessation in closed-loop systems, as well as other effects that negatively impact diabetes treatment. There are no reliable methods for detecting and / or predicting compression artifacts. Methods for predicting and thereby preventing compression artifacts have not been developed before. Summary of the Invention

[0005] Example embodiments may relate to a system for automatically detecting sensor stress in a CGM in real time. The system may include at least one sensor. The system may also include at least one processor in communication with the at least one sensor. The at least one processor may execute program code. The at least one processor may be programmed or configured to retrieve first measurement data, the first measurement data comprising at least one time series of blood glucose (BG) measurements. The at least one time series may be measured by the at least one sensor when not stressed. The at least one processor may be programmed or configured to receive second measurement data from the at least one sensor comprising at least one BG measurement. The at least one BG measurement may be measured by the at least one sensor. The at least one processor may be programmed or configured to determine a gap value between BG measurements based on the first and second measurement data. The at least one processor may be programmed or configured to generate a signal output indicating that the at least one sensor is stressed based on the gap value between the BG measurements exceeding a predefined threshold.

[0006] Example embodiments may relate to a system for automatically detecting the end of sensor compression in continuous glucose monitoring in real time. The system may include at least one sensor. The system may also include at least one processor in communication with the at least one sensor. The at least one processor may execute program code. The at least one processor may be programmed or configured to receive first measurement data from the at least one sensor, the first measurement data including at least one time series of BG measurement results. The at least one time series of BG measurement results may be measured by the at least one sensor while under compression. The at least one processor may be programmed or configured to receive second measurement data from the at least one sensor including at least one BG measurement result. The at least one BG measurement result may be measured by the at least one sensor after the at least one sensor has measured the at least one time series of BG measurement results. The at least one processor may be programmed or configured to determine a gap value between BG measurement results based on the first and second measurement data. The at least one processor may be programmed or configured to generate a signal output indicating that the at least one sensor is no longer under compression based on the gap value between the BG measurement results being less than a predefined threshold.

[0007] Exemplary embodiments may relate to a computer-implemented method for accurately detecting sensor compression in continuous blood glucose monitoring. The method may include receiving first measurement data. The first measurement data includes at least a time series of blood glucose (BG) measurements measured by a first sensor that is not compressed. The method may also include receiving second measurement data. The second measurement data includes a plurality of BG measurements continuously measured by a second sensor that is compressed. The method may also include determining a plurality of gap values ​​between the BG measurements based on the first measurement data and the second measurement data. The method may also include detecting compression of a third sensor based on a distribution of the plurality of gap values. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Other features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings, in which like elements are represented by like reference numerals, and in which:

[0009] Figure 1 An exemplary system configuration of an embodiment of a system for detecting sensor stress of a CGM sensor as disclosed herein is shown.

[0010] Figure 2 An exemplary method for detecting sensor stress of a CGM sensor as disclosed herein is shown.

[0011] Figure 3 An exemplary system implementation for detecting sensor compression in a CGM as disclosed herein is shown.

[0012] Figure 4 An exemplary system implementing embodiments for detecting sensor stress in a CGM as disclosed herein is shown.

[0013] Figure 5 Exemplary graphs showing constant CGM sensor measurements without compression artifacts and simulated CGM measurements showing compression artifacts as disclosed herein are shown.

[0014] Figure 6 An exemplary graph of CGM sensor measurements from multiple sensors as disclosed herein is shown, along with a graph of gap values ​​for a time series of BG measurements for each sensor.

[0015] Figure 7 Exemplary distributions of gap values ​​for a sensor under normal conditions and for a sensor under stress as disclosed herein are shown.

[0016] Figure 8An exemplary graph of a time series of BG measurements for a single sensor as disclosed herein is shown, the measurements including compression artifacts detected based on gap values ​​in the time series of BG measurements.

[0017] Figure 9A and Figure 9B Example graphs illustrating a receiver operating characteristic curve and the area under the precision-recall curve, respectively, of a classifier model as disclosed herein for classifying a time series of BG measurements as including compression artifacts based on gap values ​​to detect sensor compression.

[0018] Figure 10A An exemplary system configuration of an exemplary computing device as disclosed herein is shown.

[0019] Figure 10B An exemplary system environment is shown in which systems, methods, and / or computer-readable media as disclosed herein may be implemented.

[0020] Figure 11 is a block diagram of an exemplary computing system in which systems, methods, and / or computer-readable media as disclosed herein may be implemented.

[0021] Figure 12 An exemplary environment is shown in which systems, methods, and / or computer-readable media as disclosed herein may be implemented.

[0022] Figure 13 is a block diagram of an exemplary machine in which systems, methods, and / or computer-readable media as disclosed herein may be implemented. DETAILED DESCRIPTION

[0023] The embodiments provide the ability to detect CGM sensor compression lows (e.g., compression artifacts) in real time, thereby preventing the compression low effect from negatively impacting diabetes treatment. Such negative impacts that can be prevented by the embodiments include false hypoglycemia alarms and insulin cessation in insulin infusion systems. The embodiments can fully identify pressure-induced sensor attenuation (PISA) in real time to provide an indication that PISA is occurring. The embodiments can improve the accuracy of CGM sensors by detecting compression artifacts inherent to CGM devices. The embodiments can detect compression artifacts, where the detection of compression artifacts is independent of normal physiological fluctuations in interstitial fluid (ISF) blood glucose, thereby reducing false positive detections. The embodiments improve the behavior of continuous subcutaneous insulin infusion therapies and related systems, such as sensor-enhanced insulin pumps (SAPs), hypoglycemia pause (LGS), predictive hypoglycemia pause (PLGS), or automated insulin infusion (AID) (e.g., artificial pancreas). Some embodiments achieve an area under the receiver operating characteristic (ROC) curve of 96%, which generally indicates good performance. Some embodiments produce a precision-recall (PR) curve with an average precision (AP) score of approximately 0.38, which means that the weighted average precision across all thresholds (the recall at these thresholds is used as the weight) is approximately 0.38. Such a result is generally considered to be a good result. Therefore, embodiments provide reliable detection of compression artifacts in BG measurement data.

[0024] Each embodiment improves the operation of a computer (e.g., a computer processor) to automatically detect PISA and improve the treatment of diabetes. Prior to the development of the embodiments disclosed herein, a computer that was not programmed or configured using aspects of some embodiments could not automatically detect PISA in a CGM sensor and diabetes treatment. According to an embodiment, a CGM sensor may no longer be susceptible to compression artifacts. When the sensor is squeezed (e.g., compressed) when collecting measurement results, compression artifacts in the measurement data can be automatically detected in real time. For example, when a subject with a sensor attached presses against the area where the sensor is inserted (e.g., resting his head on his arm) while sleeping, an embodiment can detect compression artifacts. An embodiment can detect PISA in such sensor measurements to anticipate and / or prevent false hypoglycemia alarms, hypoglycemia suspend systems or insulin cessation in closed-loop systems, and other effects that may have a negative impact on diabetes treatment. An embodiment can accurately predict compression artifacts so that when compression artifacts occur, they can be processed in real time to improve the treatment of diabetes.

[0025] Embodiments can continuously determine gap values ​​in real time, allowing the gap values ​​to be plotted and identified over time as the CGM sensor collects measurements. This determination is performed as the CGM sensor collects measurements and is performed continuously to provide a holistic view of the sensor's state with respect to compression over time. Real-time, iterative determination of gap values ​​allows embodiments to improve computer operation, enabling detection and treatment or remediation of compression artifacts in real time.

[0026] Figure 1 An exemplary system configuration 100 is shown for an embodiment of a system operable by program code (eg, software instructions executed by a processor) to detect sensor stress (eg, via stress artifacts) in a CGM. Figure 1 The components of may be implemented in and / or processed by a processor (e.g., a central processing unit (CPU)), and / or may be implemented on any number of distributed processors (e.g., a distributed computing system) coupled to memory and connected via a communication network. Figure 1 Each component shown in .

[0027] like Figure 1 As shown, embodiments relate to a system configured to detect sensor strain in a CGM. In some embodiments, system configuration 100 can automatically detect sensor strain in a CGM in real time. In some embodiments, system configuration 100 can include a strain detection system 102, a computing device 104, a processor 106, a memory 108, and a sensor 110.

[0028] In some embodiments, the system configuration 100 may include at least one sensor (e.g., sensor 110). The at least one sensor may measure and / or collect BG measurements associated with timestamps. The BG measurements (e.g., BG measurement data) may be in the form of a time series including multiple timestamps. For example, each BG measurement measured and / or collected by the at least one sensor may be associated with exactly one timestamp, such that the collection of multiple BG measurements forms a time series of BG measurements collected by the at least one sensor over time. The at least one time series of BG measurements may span various amounts of time and may have any of various lengths (e.g., the number of timestamps and the number of BG measurements in the time series). For example, the at least one time series of BG measurements may include measurement data measured by the at least one sensor for (e.g., a duration of) 30 minutes. The at least one sensor may measure and / or collect BG measurements over time at specified time intervals (e.g., every 30 seconds, every minute, etc.), which may define the resolution of the time series of BG measurements. For example, multiple timestamps of a time series of BG measurements may be spaced apart by one or more of: 30 second intervals, 1 minute intervals, 2.5 minute intervals, and / or 5 minute intervals. Other time intervals may be used for purposes of collecting measurements and / or for resolution of the time series.

[0029] In some embodiments, system configuration 100 may include multiple sensors. For example, system configuration 100 may include at least one additional sensor (including sensor 110). The at least one processor may be programmed or configured to cause the processor to: receive first measurement data including at least a time series of BG measurement results from the at least one additional sensor. The at least one processor may store the first measurement data collected by the additional sensor in at least one memory device (e.g., memory 108).

[0030] In some embodiments, the system configuration 100 can include at least one processor (e.g., processor 106) in communication with the at least one sensor. The at least one processor can execute program code (e.g., software instructions) for performing one or more steps of the method. In some embodiments, the stress detection system 102 can include program code for one or more steps of the embodiments described herein. The at least one processor can execute program code for detecting sensor stress in the CGM in real time relative to the measurement data collected by the at least one sensor.

[0031] In some embodiments, the at least one processor can be programmed or configured (e.g., via software instructions) to receive measurement data from the at least one sensor, the measurement data comprising at least a time series of BG measurements measured by the at least one sensor. The measurement data can be transmitted to the at least one processor in real time from the at least one sensor (e.g., relative to when the sensor collects the measurement data). Alternatively, the measurement data can be transmitted from the at least one sensor to a memory (e.g., memory 108) coupled to the at least one processor, such that the at least one processor can access the measurement data at a later time relative to when the at least one sensor collects the measurement data.

[0032] In some embodiments, the at least one time series of BG measurements may include a plurality of time stamps. Each time stamp may be associated with a BG measurement. For example, each time stamp may represent a relative time or an absolute time at which the BG measurement was collected by a sensor (e.g., sensor 110). In some embodiments, the plurality of time stamps may be separated by one or more of: 30-second intervals, 1-minute intervals, 2.5-minute intervals, and / or 5-minute intervals.

[0033] In some embodiments, the at least one processor can be programmed or configured (e.g., via software instructions) to retrieve first measurement data, comprising at least one time series of blood glucose (BG) measurements. For example, the at least one processor can retrieve the first measurement data from a memory coupled to the at least one processor. The first measurement data can be collected by at least one sensor and transmitted to the memory for storage and / or transmission to the at least one processor. The at least one time series of BG measurements can be measured by the at least one sensor while stressed or unstressed. In some embodiments, the at least one processor can be programmed or configured to receive the first measurement data from the at least one sensor (e.g., in real time). In this manner, the at least one processor can be programmed or configured to receive the first measurement data for immediate use, and / or the at least one processor can be programmed or configured to retrieve the first measurement data from a storage component (e.g., memory 108) after the first measurement data is collected by the at least one sensor for later use. In some embodiments, the at least one time series of BG measurements (e.g., the first measurement data) can be measured and / or collected by the at least one sensor before the second measurement data is measured and / or collected.

[0034] In some embodiments, at least one processor may be programmed or configured to receive second measurement data (e.g., from the at least one sensor). The second measurement data may include at least one BG measurement. The at least one BG measurement may be measured by the at least one sensor (e.g., in real time). The second measurement data may be transmitted from the at least one sensor to the at least one processor in real time (e.g., relative to when the sensor collects the second measurement data). Alternatively, the second measurement data may be transmitted from the at least one sensor to a memory (e.g., memory 108) coupled to the at least one processor, such that the at least one processor can access the second measurement data at a later time relative to when the at least one sensor collects the second measurement data. In this manner, the at least one processor may be programmed or configured to receive the second measurement data for immediate use, and / or the at least one processor may be programmed or configured to retrieve the second measurement data from a storage component (e.g., memory 108) after the second measurement data is collected by the at least one sensor for later use.

[0035] In some embodiments, when configured to receive second measurement data including at least one BG measurement result, at least one processor may be programmed or configured to cause the processor to: receive the second measurement data including the continuous BG measurement result (e.g., multiple BG measurement results) from the at least one sensor when the at least one sensor obtains the continuous BG measurement result in real time.

[0036] In some embodiments, at least one processor may be programmed or configured to cause the processor to determine that the BG measurement results of at least one time series are candidate sequences that include a compression artifact. The candidate sequence may include the BG measurement results of a time series having a falling time window (e.g., a series of BG measurement results whose values ​​decrease over time). The candidate sequence may include the BG measurement results of a time series having one or more BG measurement results below a BG measurement threshold (e.g., a threshold value). In some embodiments, the candidate sequence may include the BG measurement results of a time series that includes one or more of the following attributes: the one or more attributes may indicate that the BG measurement results of the time series include a compression artifact. However, in some cases, the candidate sequence may include one or more attributes indicating that the BG measurement results of the time series include a compression artifact, while the BG measurement results of the time series do not include a compression artifact. In other cases, the candidate sequence will include a compression artifact. Determining that the BG measurement results of at least one time series are a candidate sequence may be the first step in discovering compression artifacts in the measurement data and detecting sensor compression.

[0037] In some embodiments, at least one processor may be programmed or configured to cause the processor to determine that the BG measurement results of the at least one time series include a change in BG measurement values ​​across multiple time stamps (e.g., across two consecutive time stamps, or across multiple time stamps starting from a first time stamp). The change in the BG measurement value may exceed a threshold (e.g., a threshold value). For example, the at least one processor may calculate the change in BG measurement value by determining the difference between the first BG measurement value and the second BG measurement value, thereby determining the change in BG measurement value. The at least one processor may then determine that the difference between the first BG measurement value and the second BG measurement value exceeds a threshold (e.g., a fall time threshold, a rise time threshold, etc.). In some embodiments, the first BG measurement value may be associated with a first time stamp, and the second BG measurement value may be associated with a second time stamp, wherein the first time stamp and the second time stamp are consecutive time stamps in the BG measurement results of the at least one time series. Alternatively, the first BG measurement value may be associated with a first timestamp and the second BG measurement value may be associated with a second timestamp, wherein the first timestamp and the second timestamp are separated by one or more timestamps in the BG measurement results of the at least one time series.

[0038] In some embodiments, the at least one processor may be programmed or configured to cause the processor to determine a gap value between BG measurement results (e.g., a gap value between a first BG measurement value and a second BG measurement value) based on the first measurement data and the second measurement data. For example, the at least one processor may be programmed or configured to cause the processor to select a first BG measurement value from the first measurement data and a second BG measurement value from the second measurement data. The at least one processor may be programmed or configured to cause the processor to determine the gap value based on a difference between the first BG measurement value and the second BG measurement value. The at least one processor may be programmed or configured to cause the processor to determine the gap value between the at least one BG measurement result of the second measurement data and the first BG measurement result associated with the first timestamp of the first measurement data.

[0039] In some embodiments, when configured to determine a gap value between BG measurements, the at least one processor can be programmed or configured to determine each of a plurality of gap values ​​between each consecutive BG measurement and each BG measurement in the first measurement data in real time as the processor receives each consecutive BG measurement. For the consecutive BG measurements, a first BG measurement in the consecutive BG measurements can be associated with a first timestamp of the first measurement data, a second BG measurement in the consecutive BG measurements can be associated with a second timestamp, and so on. In this manner, for each consecutive BG measurement, a gap value can be determined for the BG measurement as the BG measurement is collected by the at least one sensor and received by the at least one processor in real time.

[0040] In some embodiments, at least one processor may be programmed or configured such that the processor determines the gap value based on a model such as:

[0041]

[0042] Among them, G LSC is the glucose concentration in the local sensor compartment of the at least one sensor, G ISF is the glucose concentration in the tissue fluid, is the rate of change of glucose concentration in the local sensor compartment, k1 is the gap value, and k0 is the glucose transport rate, where

[0043] In some embodiments, when configured to determine that the BG measurement results of the at least one time series are a candidate sequence, the at least one processor can be programmed or configured to cause the processor to determine that the BG measurement results of the at least one time series include a time series subsequence, the time series subsequence having a falling time window and a rising time window associated with the falling time window. The time series subsequence can include a series of BG measurement results associated with a timestamp as a subset of the BG measurement results of the at least one time series, the timestamp being in the form of a time series including a plurality of timestamps. For example, each BG measurement result measured and / or collected by the at least one sensor can be associated with exactly one timestamp, such that the set of multiple BG measurement results forms the BG measurement results of the time series collected by the at least one sensor over time. The BG measurement results of the time series can include one or more time series subsequences within the BG measurement results of the time series, the time series subsequences can span various amounts of time and can have any of various lengths (e.g., the number of timestamps and BG measurement results in the time series subsequence).

[0044] In some embodiments, the time series subsequence may include a timestamp sequence corresponding to at least a portion of a falling time window (e.g., at least one timestamp of the time series subsequence is within the falling time window) and at least a portion of a rising time window (e.g., at least one timestamp of the time series subsequence is within the rising time window). In some embodiments, the time series subsequence may span a BG measurement duration of at least 2.5 minutes.

[0045] In some embodiments, when the at least one processor is configured to determine that the BG measurement results of the at least one time series are a candidate sequence, the at least one processor can be programmed or configured to cause the processor to determine a fall time window within the at least one time series based on a difference between the first BG measurement value and the second BG measurement value exceeding a fall time threshold. The fall time window can include a series of multiple timestamps and multiple BG measurement results, the BG measurement results starting at a first timestamp associated with the first BG measurement result (e.g., the BG measurement value) and ending at a second timestamp associated with the second BG measurement result (e.g., the BG measurement value). In some embodiments, the first BG measurement value and the second BG measurement value can be part of the same measurement data (e.g., the BG measurement results of the same time series).

[0046] In some embodiments, when the at least one processor is configured to determine that the BG measurement results of the at least one time series are a candidate sequence, the at least one processor can be programmed or configured to cause the processor to determine a rise time window within the at least one time series based on the difference between the third BG measurement value and the fourth BG measurement value exceeding the rise time threshold. The rise time window can include a series of multiple timestamps and multiple BG measurement results, the BG measurement results starting at a third timestamp associated with the third BG measurement result (e.g., BG measurement value) and ending at a fourth timestamp associated with the fourth BG measurement result (e.g., BG measurement value). The rise time window can be associated with the fall time window because the rise time window appears only after at least one fall time window appears within the BG measurement results of the time series.

[0047] In some embodiments, the fall time threshold may be equal to 10 mg / dL (eg, a BG measurement). In some embodiments, the rise time threshold may be equal to 6 mg / dL (eg, a BG measurement).

[0048] In some embodiments, the at least one processor may be programmed or configured to generate a signal output indicating that the at least one sensor is stressed based on a gap value between the BG measurements. For example, the at least one processor may generate a signal output indicating that the at least one sensor is stressed based on the gap value exceeding a predefined threshold (e.g., a threshold defined by the BG measurements).

[0049] In some embodiments, at least one processor can be programmed or configured to generate a signal output indicating that the at least one time series of BG measurements was obtained while the at least one sensor was compressed. For example, the signal output can include a signal transmitted to an insulin infusion system to cause the insulin infusion system to perform an action, a signal transmitted to a display, a signal transmitted to another processor to cause the processor to perform an action, etc.

[0050] In some embodiments, when configured to generate a signal output, at least one processor can be programmed or configured to predict that at least one sensor is compressed when obtaining a BG measurement. At least one processor can be programmed or configured to predict (e.g., generate a prediction) using a predefined model executed by the processor. The processor can generate the prediction in real time (e.g., in real time relative to the sensor collecting the measurement data) by outputting an indication based on receiving the measurement data from the at least one sensor as the sensor collects the measurement data.

[0051] In some embodiments, when configured to generate a signal output, the at least one processor can be programmed or configured such that the processor predicts in real time by outputting an indication that the at least one sensor is stressed when obtaining a BG measurement. When configured to generate a signal output, the at least one processor can be programmed or configured such that the processor indicates in real time by outputting an indication that the at least one sensor is stressed when obtaining a BG measurement.

[0052] For example, the at least one processor can generate a signal output based on determining that a gap value is outside of a normal distribution of gap values. The normal distribution of gap values ​​can represent the magnitude of gap values ​​normally determined by the at least one processor when the at least one sensor collects BG measurements in a non-stressed state.

[0053] In some embodiments, the at least one processor can generate a signal output based on determining that the gap value is within a distribution of gap values ​​at a compression low point. The distribution of gap values ​​at compression low points can represent the magnitude of gap values ​​determined by the at least one processor when the at least one sensor collects BG measurements while under compression. In some embodiments, the distribution of gap values ​​at compression low points can be experimentally determined (e.g., predetermined) using one or more sensors that are known to collect BG measurements while under compression and / or without compression.

[0054] In this manner, at least one processor can generate a gap value in real time that can be compared to a normal distribution of gap values ​​and a distribution of gap values ​​at compression low points. Based on comparing the gap value to these distributions, the at least one processor can identify whether a time series includes BG measurements obtained while at least one sensor is under compression in real time. That is, BG measurements can be collected and compared to previous BG measurements from at least one sensor (e.g., based on a delay) such that the BG measurements used to determine the gap value are close in time (e.g., collected by at least one sensor within 30 seconds to 5 minutes of each other) to allow for real-time comparison of the gap value with these distributions. In some embodiments, the BG measurements (e.g., first measurement data) for the at least one time series can include at least one BG measurement that is extrapolated (rather than measured) from the BG measurements for the at least one time series. The at least one processor can then generate a signal output indicating whether the sensor is under compression based on the comparison of the gap value to these distributions.

[0055] In some embodiments, at least one processor may be programmed or configured to cause the processor to identify one or more features of the BG measurement results of the at least one time series. For example, the at least one processor may identify and / or extract features of the BG measurement results of the at least one time series (e.g., through feature extraction techniques) for input into at least one machine learning model. The at least one processor may be programmed or configured to cause the processor to input the one or more features into the at least one machine learning model for classification and / or generate a signal output based on the classification of the BG measurement results of the at least one time series.

[0056] In some embodiments, at least one processor can be combined with the insulin infusion system, and the insulin infusion system communicates with the at least one processor.At least one processor can be programmed or configured to make the processor output to the insulin infusion system transmission signal.Described signal output can indicate that at least one sensor is compressed, and described signal output can make the insulin infusion system perform at least one or more of the following: start insulin infusion (for example, after insulin infusion stops due to compression artifact), continue insulin infusion (for example, after determining that at least one sensor is compressed and works normally), disable alarm (for example, after sensor compression causes alarm to be triggered) and / or its any combination.

[0057] refer to Figure 1 , the strain detection system 102 may include software instructions (e.g., program code) implemented on a computing device 104. The strain detection system 102 may include a memory 108 that stores the software instructions. The strain detection system 102 may include a processor 106 that executes the software instructions to cause the processor 106 to perform one or more functions. The strain detection system 102 may include a sensor 110 (e.g., a CGM sensor).

[0058] The pressure detection system 102 may include at least one processor 106. The at least one processor 106 may generate at least one signal output based on at least one time series of BG measurement data with pressure artifacts provided to the at least one processor 106 as a runtime input. The signal output of the at least one processor 106 may include an indication of whether the at least one time series of BG measurement data was obtained while at least one sensor was under pressure. The at least one time series of BG measurement data may be received by the computing device 104 and / or the processor 106 from the memory 108 and / or the sensor 110. Additionally or alternatively, the at least one processor 106 may generate at least one signal output (e.g., a prediction result) based on a training dataset and / or a test dataset.

[0059] In some embodiments, the pressure detection system 102 can be implemented in a single computing device. In some embodiments, the pressure detection system 102 can be implemented in multiple computing devices (e.g., a group of servers, such as a group of computing devices 104, etc.) as a distributed system, such that software instructions are implemented on different computing devices. In some embodiments, the pressure detection system 102 can be associated with the computing device 104, such that the pressure detection system 102 is executed on the computing device 104, or a portion of the pressure detection system 102 is executed on the computing device 104 (as part of the distributed computing system), wherein the sensor 110 is not part of the computing device 104. Alternatively, the pressure detection system 102 can include at least one computing device 104 that executes software instructions and at least one sensor 110 for detecting pressure artifacts and / or PISA.

[0060] The sensor 110 may include a CGM sensor configured to detect and / or measure blood glucose in a subject (e.g., a patient). In some embodiments, the sensor 110 may include one or more sensors. For example, the sensor 110 may include a CGM sensor and a pressure sensor. In some embodiments, the sensor 110 may include multiple CGM sensors. The sensor 110 may be worn on and / or attached to various parts of the subject's body (e.g., an arm, abdomen, etc.). The sensor 110 may be configured to collect measurement results (e.g., BG measurement results) and send the measurement results to a processor (e.g., processor 106). When the sensor 110 collects and / or obtains the measurement results, the sensor 110 may be compressed (e.g., by the subject or other means), thereby affecting the accuracy of the measurement results obtained by the sensor 110.

[0061] The sensor 110 may include a sampling rate that can be configured to collect and / or obtain measurements with a specific sampling resolution over time. For example, the sensor 110 may be configured to sample measurements at 30-second intervals, 1-minute intervals, 2.5-minute intervals, 5-minute intervals, and so on. As the measurements are sampled, the sensor 110 may transmit the sampled measurements to the processor 106 in real time. In some embodiments, the sensor 110 may transmit at least a time series of sampled measurements to the processor 106 and / or the memory 108 at specified time intervals. The time series may include multiple BG measurements, each associated with a timestamp. The timestamp may represent an absolute or relative time when the sensor 110 collected the BG measurement. The sensor 110 may communicate with the computing device 104 and / or the processor 106 via wired (e.g., data bus, Ethernet, etc.) or wireless (e.g., Wi-Fi, Bluetooth, etc.) means and / or communication interfaces.

[0062] Computing device 104 may include a processor 106 (e.g., a CPU) and memory 108. Processor 106 may execute software instructions (e.g., compiled program code) for pressure detection system 102. In some embodiments, sensor 110 may be separate from computing device 104. Alternatively, sensor 110 may be integrated with computing device 104 (e.g., as part of computing device 104).

[0063] The computing device 104 may include one or more processors (e.g., processor 106) configured to execute software instructions. For example, the computing device 104 may include a desktop computer, a portable computer (e.g., a laptop computer, a tablet computer), a workstation, a mobile device (e.g., a smartphone, a cellular phone, a personal digital assistant, a wearable device), a server, and / or other similar devices. The computing device 104 may include a computing device configured to communicate with one or more other computing devices via a network. The computing device 104 may include a group of computing devices (e.g., a group of servers) and / or other similar devices. In some embodiments, the computing device 104 may include a data storage device. Alternatively, the data storage device may be separate from the computing device 104 and may communicate with the computing device 104.

[0064] The processor 106 may be implemented in hardware, software, or a combination of hardware and software. For example, the processor 106 may include a conventional processor (e.g., a CPU, a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and / or any processing component that can be programmed with software instructions (e.g., a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.), such that the processor is configured to perform functions when executing the software instructions. In some embodiments, the processor 106 may include multiple processors (e.g., a CPU and a GPU) implemented in a single computing device 104, or the processor 106 may include multiple processors implemented across multiple distributed computing devices 104. The processor 106 may be coupled to the memory 108 via a data bus to transfer data between the processor 106 and the memory 108. In some embodiments, the processor 106 may be coupled to the sensor 110 via a wired (e.g., a data bus, Ethernet, etc.) or wireless (e.g., Wi-Fi, Bluetooth, etc.) device and / or communication interface.

[0065] The memory 108 may include random access memory (RAM), read-only memory (ROM), and / or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, optical memory, etc.) that stores information and / or software instructions for use by the processor 106. The memory 108 may include computer-readable media and / or storage components. Computer-readable media (e.g., non-transitory computer-readable media) are defined herein as non-transitory memory devices. Non-transitory memory devices include memory space located within a single physical storage device or memory space distributed across multiple physical storage devices.

[0066] The software instructions may be read into the memory 108 from another computer-readable medium or from another device through a communication interface with the computing device 104. When executed, the software instructions stored in the memory 108 and executed by the processor 106 may cause the processor 106 to perform one or more functions described herein. The embodiments described herein are not limited to any specific combination of hardware circuitry and software.

[0067] Figure 1 The number and arrangement of systems, hardware, and / or modules (eg, software instructions) shown in the are provided as examples. Figure 1 There may be additional systems, hardware, and / or modules, fewer systems, hardware, and / or modules, different systems, hardware, and / or modules, or differently arranged systems, hardware, and / or modules than those shown in . Figure 1 Two or more systems, hardware and / or modules shown in the drawings may be implemented within a single system, hardware and / or module. Figure 1 The single system, hardware and / or module shown may be implemented as multiple distributed systems, hardware and / or modules. Additionally or alternatively, Figure 1 A set of systems, a set of hardware, and / or a set of modules (e.g., one or more systems, one or more hardware devices, one or more modules) may perform the operations described as being performed by Figure 1 One or more functions performed by another set of systems, another set of hardware, or another set of modules.

[0068] like Figure 2As shown, embodiments relate to an exemplary method 200 for detecting sensor compression in continuous glucose monitoring as disclosed herein. Method 200 can be performed by one or more components of system configuration 100. In some embodiments, one or more of the functions described with respect to method 200 can be performed (e.g., completely, partially, etc.) by compression detection system 102 (e.g., via processor 106). In some embodiments, one or more of the steps of method 200 can be performed (e.g., completely, partially, etc.) by another system, hardware, or module or a group of systems, hardware, or modules (e.g., a client device and / or a separate computing device) that is separate from or includes compression detection system 102.

[0069] like Figure 2 As shown, at step 202, method 200 may include receiving first measurement data as at least a time series of BG measurements. For example, the pressure detection system 102 (e.g., via the computing device 104 and / or the processor 106) may receive at least a time series of BG measurements from the sensor 110. In some embodiments, the pressure detection system 102 may receive the first measurement data to provide the measurement data as input to at least one processor 106 for generating a signal output. The first measurement data may include historical data (e.g., data collected at a previous time). Alternatively, the first measurement data may include real-time runtime input from the sensor 110. In some embodiments, the first measurement data may be received from the sensor 110 or another sensor. In some embodiments, when the first measurement data includes historical data, the first measurement data may be retrieved from one or more storage devices (e.g., the memory 108). The first measurement data is collected by at least one sensor (e.g., the sensor 110) without being subjected to pressure.

[0070] In some embodiments, the first measurement data may include multiple time series of BG measurement results. A time series of BG measurement results may include multiple timestamps. Each timestamp may be associated with a BG measurement result. For example, one BG measurement result collected by sensor 110 may be associated with exactly one timestamp. The timestamp may represent the absolute time or relative time when sensor 110 collected the BG measurement result. Processor 106 may receive as input first measurement data including a time series of BG measurement results. A time series of BG measurement results may span various amounts of time and may have any of various lengths (e.g., the number of timestamps and the number of BG measurement results). For example, at least one time series of BG measurement results may include at least 30 minutes of measurement data measured by at least one sensor. In this example, the number of timestamps and the number of BG measurement results may depend on the resolution of the BG measurement results, or the sampling rate at which sensor 110 collects the BG measurement results. In some embodiments, a time series subsequence may span a BG measurement duration of at least 2.5 minutes. In some embodiments, a time series subsequence may be longer or shorter than 2.5 minutes.

[0071] At step 204, method 200 may include receiving second measurement data comprising at least one BG measurement. For example, stress detection system 102 (e.g., via computing device 104 and / or processor 106) may receive at least a time series of BG measurements from sensor 110 collected while sensor 110 is stressed. In some embodiments, stress detection system 102 may receive the second measurement data to provide the measurement data as input to at least one processor 106 for generating a signal output. The second measurement data may include historical data (e.g., data collected at a previous time). Alternatively, the second measurement data may include real-time runtime input from sensor 110 (e.g., measurement data received by processor 106 simultaneously with or shortly after sensor 110 collects the measurement data). In some embodiments, the first measurement data may be received from sensor 110 or another sensor. In some embodiments, when the first measurement data includes historical data, the first measurement data may be retrieved from one or more storage devices (e.g., memory 108).

[0072] In some embodiments, step 202 and step 204 of method 200 may occur separately, or step 202 may occur before step 204, or step 204 may occur before step 202. In some embodiments, step 202 may occur simultaneously with step 204.

[0073] At step 206, method 200 may include determining at least one gap value. For example, stress detection system 102 (e.g., via computing device 104 and / or processor 106) may determine a gap value between BG measurements based on first and second measurement data. The gap value may include a model-based value for the at least one BG measurement of the second measurement data and the first BG measurement associated with the first timestamp of the first measurement data.

[0074] At step 208, method 200 can include detecting sensor compression. For example, compression detection system 102 can detect that at least one sensor is compressed based on the gap value. In some embodiments, compression detection system 102 can detect that at least one sensor is compressed based on a distribution of multiple gap values.

[0075] In some embodiments, the stress detection system 102 can determine that the measurement data contains a stress artifact based on determining that at least one BG measurement value is less than a stress estimation threshold. For example, one or more BG measurements (e.g., within a time series of BG measurements) can form a stress artifact in the measurement data. The one or more BG measurements can indicate the occurrence of sensor stress (e.g., the sensor 110 was stressed when the one or more BG measurements were collected).

[0076] In some embodiments, stress detection system 102 may determine that second measurement data received from sensor 110 includes an occurrence of sensor stress based on determining that the measurement data (e.g., a time-series subsequence of measurement data) includes at least one stress artifact. A stress artifact may include a time series of BG measurements where a gap value for each BG measurement in the time series is above or below a predefined threshold, such that the gap value is outside a normal range (e.g., outside a range of equilibrium gap values ​​that may be indicative of normal BG levels in a subject).

[0077] In some embodiments, the strain detection system 102 (e.g., via the computing device 104 and / or the processor 106) may identify one or more features of the at least one time series of BG measurements. The strain detection system 102 may input the one or more features into at least one machine learning model for classification and / or for generating a signal output.

[0078] The strain detection system 102 may generate a signal output indicating that the at least one BG measurement result (e.g., the second measurement data) was obtained while at least one sensor (e.g., sensor 110) was strained. In some embodiments, the strain detection system 102 may generate the signal output by determining a gap value and determining whether the gap value is within a distribution of gap values ​​for strain low points.

[0079] In some embodiments, when configured to generate a signal output, stress detection system 102 (e.g., via computing device 104 and / or processor 106) may predict in real time that at least one sensor is stressed while obtaining a BG measurement by outputting an indication. For example, stress detection system 102 may generate a signal output based on a gap value determined by stress detection system 102. The gap value may be determined using a model (e.g., a physiological model) that models glucose diffusion between tissue fluid and a local sensor compartment where a sensor (e.g., sensor 110) may collect BG measurements. The signal output (e.g., the gap value) indicating that at least one sensor is stressed may be determined using the model and measurement data, including BG measurements obtained while at least one sensor is stressed. In this manner, stress detection system 102 may use the physiological model to generate gap values ​​that can be used to determine that a BG measurement was obtained while at least one sensor was stressed. A gap value that is within a distribution of gap values ​​at a low point in stress may indicate that the BG measurement (e.g., the BG measurement associated with the gap value) was collected by at least one sensor while stressed. Alternatively, a gap value that is outside the distribution of gap values ​​at stress low points (and within the normal distribution of gap values) may indicate that the BG measurement (e.g., the BG measurement associated with the gap value) was collected by the at least one sensor without stress.

[0080] In some embodiments, when stress detection system 102 determines the gap value, stress detection system 102 may generate a model output of BG measurements, wherein the model output includes the BG measurement (e.g., second measurement data) as a function of the glucose concentration in the local sensor compartment. Stress detection system 102 may receive a model input of the BG measurement, wherein the model input of the BG measurement represents an estimate of the glucose concentration in the tissue fluid associated with the local sensor compartment. In some embodiments, stress detection system 102 may determine the gap value based on the model input, the model output, and a physiological model. For example, stress detection system 102 may determine the gap value based on the following model:

[0081]

[0082] Among them, G LSC is the glucose concentration at the local sensor compartment, G ISF is the glucose concentration at the tissue fluid associated with the local sensor compartment, k1 is the gap value, and k0 is the glucose transport rate to the local sensor compartment, which is set to

[0083]

[0084] In some embodiments, when G ISF With G LSC In some embodiments, when the pressure detection system 102 uses a physiological model, the gap value may not depend on G. ISF In this way, the gap value can be determined by G ISF With G LSC This ensures that the detection of the stress low is not dependent on any normal physiological fluctuations and can be based solely on the stress of the sensor.

[0085] In some embodiments, "real-time" may include a time instant at which a response (e.g., generation of a signal output) may occur within a specified time relative to the occurrence of an event (e.g., "real-time" relative to the collection of measurement data), typically within a relatively short period of time (e.g., within a few seconds or less) of the occurrence of the event. For example, "real-time" may refer to a time instant at which a signal output is generated by stress detection system 102 simultaneously with or shortly after (e.g., within milliseconds or seconds) measurement data is collected by sensor 110 (or another sensor). As another example, relative to collecting and / or receiving a time series of BG measurements or at least one BG measurement, a real-time (e.g., runtime) signal output may be generated simultaneously with or shortly after the time series of BG measurements or the at least one BG measurement is received by stress detection system 102, and / or may be generated simultaneously with or shortly after the gap value is determined by stress detection system 102.

[0086] In some embodiments, the compression detection system 102 (e.g., computing device 104 and / or processor 106) can be combined with an insulin infusion system. The compression detection system 102 (e.g., via processor 106) can communicate with the insulin infusion system (e.g., via wired and / or wireless means). The compression detection system 102 can transmit a signal output to the insulin infusion system, wherein the signal output indicates that the sensor 110 is compressed when collecting BG measurements. The insulin infusion system can receive the signal output, wherein the signal output causes the insulin infusion system to perform at least one or more of the following: start insulin infusion, continue insulin infusion, disable an alarm, and / or any combination thereof.

[0087] The steps of method 200 may be performed in various orders and sequences and are not limited to Figure 2. For example, the second measurement data may be received before the pressure detection system 102 receives the first measurement from the at least one sensor. Similarly, in some cases, the pressure detection system 102 may receive the first measurement data from the at least one sensor (e.g., sensor 110) after the pressure detection system 102 detects the pressure on the sensor. Thus, the steps of method 200 are not limited to any particular order and may be performed on various components, whether implemented on a single computing device or on multiple distributed computing devices. The steps of method 200 may also be performed by a single sensor (e.g., sensor 100) or by multiple sensors.

[0088] like Figure 3 As shown, embodiments may relate to an exemplary system implementation 300 for detecting sensor compression in continuous glucose monitoring. System 300 may include a glucose monitoring device 302, an insulin device 304, a processor 306, and a subject 308. In some embodiments, system 300 may include glucose monitoring device 302, processor 306, and subject 308 without insulin device 304. For example, system 300 may include a glucose monitoring device 302 (e.g., and at least one sensor included therein) in communication with at least one processor 306 to implement embodiments as disclosed herein.

[0089] In some embodiments, the blood glucose monitoring device 302 and / or the insulin device 304 can be identical or similar to the sensor 110. In some embodiments, the blood glucose monitoring device 302 and / or the insulin device 304 can include the sensor 110. For example, the blood glucose monitoring device 302 can include the sensor 110 as a component of the blood glucose monitoring device 302, or the insulin device 304 can include the sensor 110 as a component of the insulin device 304. In some embodiments, the blood glucose monitoring device 302, the insulin device 304 and / or the sensor 110 can be implemented in a single system. Alternatively, the processor 306 can be implemented on a computing device separate from the blood glucose monitoring device 302 and / or the insulin device 304.

[0090] In some embodiments, blood glucose monitoring device 302 and / or insulin device 304 may include processor 306. For example, blood glucose monitoring device 302 may include processor 306 as a component of blood glucose monitoring device 302, or insulin device 304 may include processor 306 as a component of insulin device 304. In some embodiments, blood glucose monitoring device 302, insulin device 304, processor 306 and / or sensor 110 may be implemented in a single system. Alternatively, processor 306 may be implemented on a computing device separate from blood glucose monitoring device 302, insulin device 304 and / or sensor 110. Processor 306 may be implemented locally in blood glucose monitoring device 302, insulin device 304 or an independent device (e.g., computing device 104) (or in any combination of two or more devices in blood glucose monitoring device 302, insulin device 302 or an independent device). In some embodiments, processor 306 may be identical or similar to processor 106.

[0091] The blood glucose monitoring device 302 may include a device that can be used to monitor and / or test the blood glucose level of a subject 308 (e.g., as a standalone device). The blood glucose monitoring device 302 may adhere to and / or be attached to the subject 308 to monitor the blood glucose level. The blood glucose monitoring device 302 may communicate with the subject 308 (e.g., via a sensor, such as sensor 110) to monitor the blood glucose level of the subject 308. In this manner, the blood glucose monitoring device 302 may collect measurement data (e.g., BG measurement data) to transmit to the processor 306 for use in detecting whether the blood glucose monitoring device 302 and / or sensor 110 are compressed by the subject 308. The processor 306 may be a component of the blood glucose monitoring device 302 or execute software instructions (e.g., compression detection system 102) separately from the blood glucose monitoring device 302. For example, the processor 306 may be implemented locally in the blood glucose monitoring device 302. In some embodiments, the blood glucose monitoring device 302 and the insulin device 304 may each be implemented as a separate device, or the blood glucose monitoring device 302 and the insulin device 304 may be implemented as a single device.

[0092] In some embodiments, the blood glucose monitoring device 302 can generate outputs, errors, parameters for accuracy improvement, and / or accuracy-related information, which can be transmitted to, for example, a processor 306 for performing various analyses, such as error analysis and / or further improvements to the embodiments of the present invention.

[0093] Insulin device 304 can comprise insulin infusion system, such as insulin pump.Insulin device 304 can be communicated with experimenter 308 to deliver insulin to experimenter 308.In certain embodiments, processor 306 can be as the component of insulin device 304 or separately execute software instruction (for example, compression detection system 102) with insulin device 304.For example, processor 306 can be local realization in insulin device 304.In certain embodiments, insulin device 304 can adhere to and / or be attached to experimenter 308 so that insulin device 304 can deliver insulin to experimenter 308.A part for processor 306 and / or system 300 can be positioned at a distance so that blood glucose monitoring device 302 and / or insulin device 304 can be used as telemedicine device operation.

[0094] The processor 306 can be implemented in hardware, software, or a combination of hardware and software. For example, the processor 306 may include a common processor (e.g., a CPU, a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and / or any processing component that can be programmed with software instructions (e.g., a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc.), so that the processor is configured to perform functions when executing software instructions. In some embodiments, the processor 306 may include multiple processors (e.g., a CPU and a GPU) implemented in a single computing device, or the processor 306 may include multiple processors implemented between multiple distributed computing devices. The processor 306 can be coupled to the memory via a data bus to transfer data between the processor 306 and the memory. In some embodiments, the processor 306 can be coupled to the sensor (e.g., sensor 110), the blood glucose monitoring device 302, and / or the insulin device 304 via a wired (e.g., data bus, Ethernet, etc.) or wireless (e.g., Wi-Fi, Bluetooth, etc.) device and / or communication interface.

[0095] Subject 308 can include a patient at home or another desired location. In some embodiments, the subject can include a human or any animal. It should be understood that the animal can be of any suitable type, including but not limited to mammals, veterinary animals, livestock animals, or pet-type animals. As an example, the animal can be a laboratory animal (e.g., a mouse, dog, pig, monkey) that is specifically selected to have certain characteristics similar to those of a human. It should be understood that, for example, the subject can be any suitable human patient.

[0096] Figure 3 The number and arrangement of systems, hardware and / or devices shown in are provided as examples. Figure 3There may be additional systems, hardware, and / or devices, fewer systems, hardware, and / or devices, different systems, hardware, and / or devices, or differently arranged systems, hardware, and / or devices than those shown in . Figure 3 Two or more systems, hardware and / or devices shown in FIG. 1 may be implemented within a single system, hardware and / or device. Figure 3 The single system, hardware and / or device shown may be implemented as multiple distributed systems, hardware and / or devices. Additionally or alternatively, Figure 3 A set of systems, a set of hardware, and / or a set of devices (e.g., one or more systems, one or more hardware components, one or more devices) may perform the operations described as being performed by Figure 3 One or more functions performed by another set of systems, another set of hardware, or another set of devices.

[0097] Figure 4 An exemplary system 400 implementing an embodiment for detecting sensor stress in a CGM is shown, for example, Figure 4 Example components that may be used in an embodiment for implementing a method for detecting sensor compression in a CGM system, wherein the sensor is compressed while collecting blood glucose measurements, are shown. System 400 may include a blood glucose monitoring device 402, a subject 408, a sensor 410, an interstitial fluid compartment 412, and a local sensor compartment 414.

[0098] The blood glucose monitoring device 402 can be the same as or similar to the blood glucose monitoring device 302. In some embodiments, the blood glucose monitoring device 402 and / or the sensor 410 can be the same as or similar to the sensor 110. In some embodiments, the blood glucose monitoring device can include the sensor 410. For example, the blood glucose monitoring device 402 can include the sensor 410 as a component of the blood glucose monitoring device 402. In some embodiments, the blood glucose monitoring device 402 and / or the sensor 410 can be implemented in a single system such that the blood glucose monitoring device 402 communicates with the sensor 410. In some embodiments, at least one processor (e.g., the processor 106 or the processor 306) can be implemented as part of the blood glucose monitoring device 402. Alternatively, the at least one processor can be implemented on a computing device separate from the blood glucose monitoring device 402.

[0099] In some embodiments, the blood glucose monitoring device 402, the at least one processor, and / or the sensor 410 may be implemented in a single system. Alternatively, the at least one processor may be implemented on a computing device separate from the blood glucose monitoring device 402 and / or the sensor 410. The at least one processor may be implemented locally in the blood glucose monitoring device 402 or in a separate device (e.g., the computing device 104).

[0100] Blood glucose monitoring device 402 may include a device (e.g., as a standalone device) that can be used to monitor and / or test the blood glucose level of a subject (e.g., subject 408). Blood glucose monitoring device 402 may be adhered to and / or attached to subject 408 to monitor blood glucose levels. Blood glucose monitoring device 402 may communicate with subject 408 (e.g., via a sensor, such as sensor 410) to monitor subject 408's blood glucose level. In this manner, blood glucose monitoring device 402 may collect measurement data (e.g., BG measurement data) for transmission to at least one processor for use in detecting whether blood glucose monitoring device 402 and / or sensor 410 are being compressed by subject 408. At least one processor (e.g., of blood glucose monitoring device 402 or another device) may execute software instructions (e.g., compression detection system 102) as a component of blood glucose monitoring device 402 or separately from blood glucose monitoring device 402. For example, at least one processor may be implemented locally in blood glucose monitoring device 402.

[0101] In some embodiments, the blood glucose monitoring device 402 can generate outputs, errors, parameters for accuracy improvement, and / or accuracy-related information, which can be transmitted to, for example, the processor 306 for performing various analyses, such as error analysis and / or further improvements to the embodiments of the present invention.

[0102] Subject 408 can include a patient at home or another desired location. In some embodiments, the subject can include a human or any animal. It should be understood that the animal can be of any suitable type, including but not limited to mammals, veterinary animals, livestock animals, or pet-type animals. As an example, the animal can be a laboratory animal (e.g., a mouse, dog, pig, monkey), etc. that is specifically selected to have certain characteristics similar to those of a human. It should be understood that, for example, the subject can be any suitable human patient.

[0103] Local sensor compartment (LSC) 414 may include tissue of subject 408 that is immediately adjacent to and / or immediately surrounding sensor 410. LSC 414 may include an area and / or volume of tissue of subject 408 that includes both glucose flowing into and out of LSC 414. LSC 414 may include the glucose concentration at any given time. Interstitial fluid compartment (ISF) 412 may include tissue of subject 408 that is not immediately surrounding sensor 410. For example, ISF 412 may include tissue of subject 408 other than LSC 414. ISF 412 may include an area and / or volume of tissue of subject 408 that includes both glucose flowing into and out of ISF 412 (e.g., into LSC 414). In this manner, stress detection system 102 may use the glucose concentration balance between LSC 414 and ISF 412 as an indication that sensor 410 is in normal condition and not stressed. When the ISF 412 and the LSC 414 have balanced glucose concentrations, then the flow of blood glucose from the LSC 414 to the ISF 412 and vice versa is constant (e.g., balanced), and the stress detection system 102 can determine that the sensor 410 is not stressed (e.g., no stress is present) based on the glucose concentration balance between the ISF 412 and the LSC 414.

[0104] Figure 4 The number and arrangement of systems, hardware and / or devices shown in are provided as examples. Figure 4 There may be additional systems, hardware, and / or devices, fewer systems, hardware, and / or devices, different systems, hardware, and / or devices, or differently arranged systems, hardware, and / or devices than those shown in . Figure 4 Two or more systems, hardware and / or devices shown in FIG. 1 may be implemented within a single system, hardware and / or device. Figure 4 The single system, hardware and / or device shown may be implemented as multiple distributed systems, hardware and / or devices. Additionally or alternatively, Figure 4 A set of systems, a set of hardware, and / or a set of devices (e.g., one or more systems, one or more hardware components, one or more devices) may perform the operations described as being performed by Figure 4 One or more functions performed by another set of systems, another set of hardware, or another set of devices.

[0105] Figure 5 A shows an exemplary graph of constant CGM sensor measurements without compression artifacts and simulated CGM measurements showing compression artifacts (top graph). Figure 5 A also shows the pressure detection system 102 based on the system (e.g., the pressure detection system 102). Figure 5Example graphs of gap values ​​determined from constant CGM measurements and simulated CGM measurements (bottom graph) shown in the time graph of A. For example, the top graph shows a constant glucose concentration (G) in an ISF (e.g., ISF 412). IsF ),exist Figure 5 Labeled "Model Input" in A. The top graph also shows the variable glucose concentration (G LSc ),exist Figure 5 A is marked as "model output". Figure 5 As shown in the bottom curve of A, when G ISF With G LSC Note that in a time series of BG measurement results, each BG measurement result can be associated with at least one gap value, such as Figure 5 A is shown over a 50 minute span with multiple BG measurements and multiple gap values. The embodiment also provides improvements and allows for the top graph to show variable glucose concentrations (G ISF ). In this manner, the pressure detection system 102 can determine the gap value independently of the glucose concentration in the ISF (e.g., outside the local sensor compartment). The pressure detection system 102 can determine the gap value based on the difference between the glucose concentration in the ISF and the glucose concentration in the LSC. Thus, detection of sensor pressure is independent of normal physiological fluctuations that may occur in the glucose concentration in the ISF.

[0106] Figure 6 An exemplary graph of CGM sensor measurements from multiple sensors is shown, along with a graph of gap values ​​(e.g., ISF-based glucose concentration and LSC-based glucose concentration) for a time series of BG measurements for each sensor. For example, Figure 6 The present invention provides embodiments in the context of using multiple sensors to determine whether any of the multiple sensors is under pressure when collecting BG measurements. Figure 6 , showing multiple time series of BG measurements, each time series of BG measurements collected by one of the plurality of sensors (top graph). A first time series of BG measurements collected by a first sensor is shown, wherein one or more compression artifacts are present in the BG measurements of the first time series. Also shown are a second time series of BG measurements collected by a second sensor and a third time series of BG measurements collected by a third sensor. A fourth time series of BG measurements is shown, which represents BG measurements collected by a fourth sensor (or sensors) that is not compressed. ISFMeasurement results. Figure 6 In this embodiment, the fourth time series of BG measurements can include at least one BG measurement that can be used as a model input for the stress detection system 102 to determine the gap value. In this manner, the fourth time series of BG measurements can be considered a "baseline" for BG measurements collected by the sensor in the absence of stress.

[0107] like Figure 6 As shown, the BG measurement results of the first time series include two low points of pressure based on the gap value shown. The pressure detection system 102 can determine the gap value of the first sensor based on the BG measurement results of the fourth time series (e.g., as a model input) and based on the BG measurement results of the first time series (e.g., as a model output). Plot the gap value against the BG measurement results of the time series (e.g., Figure 6 ) shows where the sensor has a stress low point. The BG measurements for the second time series shown do not have any stress low points because the gap value for the second sensor shown is within a normal distribution (e.g., a normal range) of gap values. The second sensor does not have any gap value "spikes," as determined by the stress detection system 102 based on the BG measurements for the fourth time series and the BG measurements for the second time series (and as shown in FIG. Figure 6 The BG measurements for the third time series are also shown to be free of any low points, as the gap values ​​for the third sensor are shown to be within the normal distribution of gap values ​​(e.g., Figure 6 There are no gap values ​​"spikes" in it). Figure 6 The figure also shows that the BG measurements of the second and third time series do contain variable BG measurements rather than being completely constant, but the gap value remains relatively constant. In this way, the embodiment is independent of the variable changes in normal BG measurements (e.g., normal physiological variance). The gap value "spike" at the low point of stress allows for accurate detection of stress in the CGM sensor.

[0108] Figure 7 An exemplary distribution of gap values ​​for a sensor under normal conditions and a sensor under stress is shown. For example, Figure 7 An exemplary normal distribution of gap values ​​(shown in dark grey, e.g., non-PISA) and an exemplary distribution of gap values ​​for compression low points (shown in light grey, e.g., PISA) are shown. Figure 7As shown, normal gap values ​​(e.g., gap values ​​determined by stress detection system 102 for BG measurements collected by sensors that are not stressed) may average approximately 0.9-1.0, while gap values ​​for stress lows may average approximately 1.1-1.2. Such a distribution of gap values ​​may provide sufficient gap value differences between normal conditions and stress low conditions such that sensor stress may be detected by determining whether a real-time estimate of the gap value is within the distribution of normal gap values ​​or whether the real-time estimate of the gap value is within the distribution of stress low gap values.

[0109] In some embodiments, the compression detection system 102 can use the distribution of normal gap values ​​and the distribution of gap values ​​for compression low points to determine a predefined threshold (e.g., a gap value threshold) that can be used for gap values ​​to indicate the occurrence of a compression low point. For example, the compression detection system 102 can determine a predefined threshold for the gap value, wherein a gap value greater than the threshold indicates that the sensor is compressed, and a gap value less than the threshold indicates that the sensor is not compressed. The predefined threshold can also be used by the compression detection system 102 to indicate the presence of sensor compression and / or the absence of sensor compression when the gap value is greater than or equal to the predefined threshold or less than or equal to the predefined threshold. In some embodiments, the predefined threshold can be equal to 1.05 and 1.3 or between 1.05 and 1.3. In some embodiments, the predefined threshold can be equal to 0.9 and 1.1 or between 0.9 and 1.1.

[0110] Figure 8 An exemplary graph illustrating a time series of BG measurements for a single sensor determined using embodiments disclosed herein, including compression artifacts detected based on gap values ​​in the time series of BG measurements. Figure 8 The use of delayed measurement data (e.g., a 3-minute delay) collected by a single sensor and current measurement data (e.g., a real-time sensor trace) collected by the single sensor to determine a gap value is shown. The delayed measurement data collected by the single sensor can be used as an input to a model (e.g., a physiological model as disclosed herein), and the current measurement data can be used as an output of the model to determine a gap value for the BG measurement results of the single sensor. For example, the pressure detection system 102 can use a model defined as follows:

[0111]

[0112] Among them, G LSC is the glucose concentration in the LSC and is the output of the model, G ISF is the glucose concentration in the ISF compartment and is the input to the model, k1 is the gap value of the BG measurement, and k0 is the glucose transport rate, which is set to In some embodiments, the model can be used by the pressure detection system 102 to determine a plurality of gap values ​​at each time stamp and each BG measurement in a time series of BG measurements. Thus, the pressure detection system 102 can use the model to generate a time series of gap values, such as Figure 8 (bottom graph). Using band-delayed measurement data collected by a single sensor is one way that the pressure detection system 102 can determine a gap value for a sensor. In some embodiments, the pressure detection system 102 can use band-delayed measurement data collected from a single sensor, extrapolated measurement data, predicted measurement data (e.g., by a machine learning model), or smoothed measurement data to determine a gap value. For example, the first measurement data can include at least one BG measurement extrapolated from at least a time series of BG measurements. In this way, the pressure detection system 102 can rely on measurement data collected by a sensor, wherein a portion of the measurement data is collected by the sensor when it is not under pressure. This can allow the pressure detection system 102 to develop a measurement data baseline that represents the measurement data that is not affected by pressure lows.

[0113] Figure 9A and Figure 9B An exemplary graph illustrating a receiver operating characteristic (ROC) curve and the area under the precision-recall curve, respectively, of a classifier model (e.g., a physiological model) for classifying a time series of BG measurements as including compression artifacts based on gap values ​​to detect sensor compression. Figure 9A In , the area under the ROC curve is 96%, indicating good performance of the embodiment in accurately classifying BG measurement data as indicating sensor compression using the gap value. Figure 9B In , the precision-recall curve summarizing the trade-off between the model’s true positive rate and positive predictive value shows an average position score of 0.38. Therefore, Figure 9A and 9B The embodiment model is shown to provide accurate detection of sensor compression.

[0114] Figure 10AAn exemplary system configuration 1000A for an exemplary computing device (e.g., computing device 104) is shown. System configuration 1000A may include a processing unit 1006, a memory 1008, a removable storage device 1012, a non-removable storage device 1014, and a communication interface 1016. Processing unit 1006 may be the same as or similar to processor 106 and / or processor 306. Memory 1008 may be the same as or similar to memory 108. System configuration 1000A for a computing device (e.g., computing device 104) may include at least one processing unit 1006 and memory 1008. In some embodiments, memory 1008 may include volatile (e.g., random access memory (RAM)), non-volatile (e.g., read-only memory (ROM), flash memory, etc.), and / or any combination thereof.

[0115] In addition, system configuration 1000A may include other features and / or functionality. For example, system configuration 1000A may include additional removable storage 1012 and / or non-removable storage 1014, including but not limited to magnetic or optical disks or tape, and writable electronic storage media. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules and / or other data). Memory 1008, removable storage 1012, and non-removable storage 1014 are all examples of computer storage media. Computer storage media may include but are not limited to RAM, ROM, erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CDROM), digital versatile disk (DVD) or other optical storage device, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device, or any other medium that can be used to store desired information and can be accessed by a computing device (e.g., computing device 104). Any such computer storage media may be part of or used in conjunction with a computing device (eg, computing device 104 ).

[0116] The system configuration 1000A of the exemplary computing device may include one or more communication interfaces 1016 that allow the computing device (e.g., computing device 104) to communicate with other devices (e.g., other computing devices). The communication interface 1016 can transmit and / or carry information and / or signals in a communication medium. The communication medium can include computer-readable instructions, data structures, program modules and / or other data in a modulated data signal (such as a carrier wave) or other transmission mechanism, and can include any information transport medium. The term "modulated data signal" may refer to a signal in which one or more of its characteristics are set or changed to encode, execute and / or process the information in the signal. By way of example and not limitation, the communication medium may include wired media such as a wired network or a direct wired connection, and wireless media such as radio, RF, infrared and / or other wireless media. As discussed, the term "computer-readable medium" as used herein may include both storage media and / or communication media.

[0117] Figure 10A The number and arrangement of systems, hardware, devices, and / or modules (eg, software instructions) shown in the are provided as examples. Figure 10A There may be fewer systems, hardware, devices, and / or modules, different systems, hardware, devices, and / or modules, or differently arranged systems, hardware, devices, and / or modules than those shown in FIG. Figure 10A Two or more systems, hardware, devices and / or modules shown in the drawings may be implemented within a single system, hardware, device and / or module. Figure 10A The single system, hardware, device and / or module shown in can be implemented as multiple distributed systems, hardware, devices and / or modules. Additionally or alternatively, Figure 10A A set of systems, a set of hardware, a set of devices and / or a set of modules (e.g., one or more systems, one or more hardware devices, one or more devices, one or more modules) may perform the operations described as being performed by Figure 10A One or more functions performed by another set of systems, another set of hardware, another set of devices, or another set of modules.

[0118] Figure 10B An exemplary system environment 1000B is shown in which systems, methods, devices, and / or computer-readable media may be implemented. System environment 1000B may include at least one server 1004 (e.g., a network server), at least one client device 1018, a mobile device 1020, and a communication network 1022. In some embodiments, server 1004 may be the same as or similar to computing device 104. Client device 1018 may be the same as or similar to computing device 104.

[0119] Figure 10BA network system may be included that includes multiple computing devices (e.g., computing device 104, server 1004, and / or client device 1018) communicating with a networking means (e.g., communication network 1022), such as a network with an infrastructure or an ad hoc network. The network connection may be a wired connection and / or a wireless connection to the multiple computing devices. As an example, Figure 10B A system environment including a network system in which an embodiment can be implemented is shown. In this example, the network system may include a server 1004 (e.g., a network server), a communication network 1022 (e.g., a wired connection and / or a wireless connection), a client device 1018, and a mobile device (e.g., a smart phone) 1020 (or other handheld or portable device, such as a cellular phone, a laptop computer, a tablet computer, a GPS receiver, an mp3 player, a handheld video player, a pocket projector, etc., or a handheld device (or non-portable device) with a combination of such features). In some embodiments, it should be understood that the server 1004, the client device 1018, and / or the mobile device 1020 may include a blood glucose monitoring device (e.g., a blood glucose monitoring device 302). In some embodiments, it should be understood that the server 1004, the client device 1018, and / or the mobile device 1020 may include a blood glucose monitoring device (e.g., a blood glucose monitoring device 302), an artificial pancreas, and / or an insulin device (e.g., an insulin device 304), or other invasive devices or diagnostic devices. Figure 10B Any component shown or discussed may be plural in number.

[0120] Some embodiments may be Figure 10B For example, the execution of instructions (e.g., software instructions) or other desired processing may be performed on the same computing device, which may be any of the server 1004, the client device 1018, and / or the mobile device 1020. Alternatively, some embodiments may be implemented on Figure 10B The network system shown is implemented and / or executed on different computing devices. For example, certain desired or required processing or execution may be performed on one of the computing devices of the network (e.g., server 1004, client device 1018, mobile device 1020, and / or blood glucose monitoring device), while other processing and execution may be performed at another computing device of the network system (e.g., server 1004, client device 1018, and / or mobile device 1020), and vice versa.

[0121] In some embodiments, certain processing and / or execution may be performed at one computing device (e.g., server 1004, client device 1018, mobile device 1020, and / or insulin device 304, artificial pancreas, or blood glucose monitoring device 302 (or other invasive or diagnostic device)), and other processing and / or execution (e.g., software instructions, stress detection system 102, etc.) may be performed at a different computing device, which may or may not be part of a network system. For example, certain processing may be performed at client device 1018, while other processing and / or instructions are passed to server 1004 and / or mobile device 1020, which may execute a portion of the software instructions (e.g., stress detection system 102). This scenario may be suitable where mobile device 1020 can access communication network 1022, for example, via client device 1018 (or an access point in an ad hoc network). As another example, one or more embodiments may be utilized to execute, encode, and / or process software instructions to be protected. The processed, encoded, and / or executed software can then be distributed to one or more customers (e.g., customer and / or subject client devices 1018, mobile devices 1020, and / or blood glucose monitoring devices 302). The distribution of software instructions (e.g., software modules and / or software packages) can be in the form of a storage medium (e.g., a disk) or an electronic copy.

[0122] Figure 10B The number and arrangement of systems, hardware, devices, and / or modules (eg, software instructions) shown in the are provided as examples. Figure 10B There may be fewer systems, hardware, devices, and / or modules, different systems, hardware, devices, and / or modules, or differently arranged systems, hardware, devices, and / or modules than those shown in FIG. Figure 10B Two or more systems, hardware, devices and / or modules shown in the drawings may be implemented within a single system, hardware, device and / or module. Figure 10B The single system, hardware, device and / or module shown in can be implemented as multiple distributed systems, hardware, devices and / or modules. Additionally or alternatively, Figure 10B A set of systems, a set of hardware, a set of devices and / or a set of modules (e.g., one or more systems, one or more hardware devices, one or more devices, one or more modules) may perform the operations described as being performed by Figure 10B One or more functions performed by another set of systems, another set of hardware, another set of devices, or another set of modules.

[0123] Figure 11is a block diagram illustrating a system 1100 including a computer system 140 and an associated Internet 11 connection on which embodiments may be implemented. This configuration is typically used for computers (hosts) to connect to the Internet 11 and execute server or client (or a combination) software. For example, a source computer (such as a laptop), a final destination computer and a relay server, as well as any computer or processor described herein, may use Figure 11 , and an Internet connection. In some embodiments, computer system 140 can be the same as or similar to computing device 104. System 1100 can be used as a portable electronic device such as a notebook / laptop computer, a media player (e.g., an MP3-based player or video player), a cellular phone, a personal digital assistant (PDA), a blood glucose monitoring device, an artificial pancreas, an insulin infusion device (or other invasive device or diagnostic device), an image processing device (e.g., a digital camera or video recorder), and / or any other handheld computing device, or a combination of any of these devices. Note that although Figure 11 Various components of a computer system are shown, but are not intended to represent any particular architecture or manner of interconnecting these components.

[0124] It should also be understood that network computers, handheld computers, cellular telephones, and other data processing systems with fewer components, or possibly more components, may also be used. Figure 11 The computer system 140 may be, for example, an Apple Macintosh computer or an Apple PowerBook, or an IBM-compatible personal computer. The computer system 140 includes a bus 137, an interconnect, or other communication mechanism for transmitting information, and a processor 138 (typically in the form of an integrated circuit) coupled to the bus 137 to process information and execute computer-executable instructions. The computer system 140 also includes a main memory 134, such as a RAM or other dynamic storage device, coupled to the bus 137 to store information and instructions to be executed by the processor 138. In some embodiments, the processor 138 may be the same as or similar to the processor 106 and / or the processor 306.

[0125] Main memory 134 can also be used to store temporary variables or other intermediate information during the execution of instructions to be executed by processor 138. Computer system 140 also includes a read-only memory (ROM) 136 (or other non-volatile memory) or other static storage device coupled to bus 137 for storing static information and instructions for processor 138. Storage devices 135 (such as magnetic disks or optical disks), hard disk drives for reading from and writing to a hard disk, magnetic disk drives for reading from and writing to a hard disk, and / or optical disk drives (such as DVDs) for reading from and writing to a removable optical disk are coupled to bus 137 to store information and instructions. The hard disk drive, magnetic disk drive, and optical disk drive can be connected to the system bus via a hard disk drive interface, a magnetic disk drive interface, and an optical disk drive interface, respectively. The drives and their associated computer-readable media provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for general-purpose computing devices. Typically, computer system 140 includes an operating system (OS) stored in non-volatile storage for managing computer resources and providing access to computer resources and interfaces to applications and programs. An operating system typically processes system data and user input and responds by allocating and managing tasks and internal system resources, such as controlling and allocating memory, prioritizing system requests, controlling input and output devices, facilitating networking, and managing files. Non-limiting examples of operating systems are Microsoft Windows, Mac OS X, and Linux.

[0126] The term "processor" is intended to include any integrated circuit or other electronic device (or collection of devices) capable of performing an operation on at least one instruction, including but not limited to reduced instruction set core (RISC) processors, CISC microprocessors, microcontroller units (MCUs), CISC-based central processing units (CPUs), and digital signal processors (DSPs). The hardware of such devices may be integrated onto a single substrate (e.g., a silicon "die") or distributed across two or more substrates. In addition, various functional aspects of a processor may be implemented separately as software or firmware associated with the processor.

[0127] Computer system 140 can be coupled to a display 131 via bus 137, such as a cathode ray tube (CRT), liquid crystal display (LCD), flat-screen monitor, touch screen monitor, or similar device for displaying text and graphical data to a user. The display can be connected via a video adapter for supporting the display. The display allows a user to view, input, and / or edit information related to the operation of the system. Input devices 132 (including alphanumeric and other keys) are coupled to bus 137 for communicating information and command selections to processor 138. Another type of user input device is cursor control 133, such as a mouse, trackball, or cursor direction keys, for communicating direction information and command selections to processor 138 and for controlling cursor movement on display 131. This input device typically has two degrees of freedom along two axes: a first axis (e.g., x) and a second axis (e.g., y), which allows the device to specify a position in a plane.

[0128] The computer system 140 can be used to implement the methods and techniques described herein. According to one embodiment, those methods and techniques are performed by the computer system 140 in response to the processor 138 executing one or more sequences of one or more instructions contained in the main memory 134. Such instructions can be read into the main memory 134 from another computer-readable medium (such as the storage device 135). Execution of the sequence of instructions contained in the main memory 134 causes the processor 138 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry can be used in place of or in combination with software instructions to implement such an arrangement. Thus, the embodiments are not limited to any specific combination of hardware circuitry and software.

[0129] As used herein, the term "computer-readable medium (or machine-readable medium)" is an extensible term that refers to any medium or any memory that participates in providing instructions to a processor (such as processor 138) for execution, or any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). Such media can store computer-executable instructions to be executed by processing elements and / or control logic, as well as data manipulated by processing elements and / or control logic, and can take a variety of forms, including but not limited to non-volatile media, volatile media, and transmission media. Transmission media include coaxial cables, copper wire, and optical fiber, including wires (including bus 137). Transmission media can also take the form of sound or light waves (such as the transmission media generated during radio wave and infrared data communications), or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). Common forms of computer readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with a pattern of holes, a RAM, a PROM and EPROM, a Flash-EPROM, any other memory chip or cartridge, a carrier wave as described below, or any other medium from which a computer can read.

[0130] Various forms of computer-readable media may be used to carry one or more sequences of one or more instructions to processor 138 for execution. For example, the instructions may initially be carried on a disk of a remote computer. The remote computer may load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 140 may receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector may receive the data carried in the infrared signal, and appropriate circuitry may place the data on bus 137. Bus 137 carries the data to main memory 134, from which processor 138 retrieves and executes the instructions. The instructions received by main memory 134 may optionally be stored on storage device 135 before or after execution by processor 138.

[0131] The computer system 140 also includes a communication interface 141 coupled to the bus 137. The communication interface 141 provides a two-way data communication connection to the network link 139, which is connected to the local network 111. For example, the communication interface 141 can be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another non-limiting example, the communication interface 141 can be a local area network (LAN) card to provide a data communication connection to a compatible LAN. For example, an Ethernet-based connection based on the IEEE 802.3 standard may be used, such as 10 / 100BaseT, 1000BaseT (Gigabit Ethernet), 10 Gigabit Ethernet (10GE or 10GbE or 10GigE as standard in accordance with IEEE standard 802.3AE-2002), 40 Gigabit Ethernet (40GbE), or 100 Gigabit Ethernet (100GbE as standard in accordance with Ethernet standard IEEE P802.3ba), as described in Cisco Systems, Inc. publication No. 1-587005-001-3 (6 / 99), “Internetworking Technologies Handbook,” Chapter 7: “Ethernet Technologies,” pages 7-1 to 7-38, which is incorporated herein by reference for all purposes as if fully set forth herein. In this case, the communication interface 141 typically includes a LAN transceiver or modem, such as the Standard Microsystems Corporation (SMSC) LAN91C11110 / 100 Ethernet transceiver described in the Standard Microsystems Corporation (SMSC) data sheet “LAN91C11110 / 100 Non-PCI Ethernet Single Chip MAC+PHY” Data Sheet Revision 15 (02-20-04), which data sheet is incorporated in its entirety for all purposes as if fully set forth herein.

[0132] Wireless links may also be implemented.In any such implementation, communication interface 141 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0133] Network link 139 typically provides data communication through one or more networks to other data devices. For example, network link 139 can provide a connection to a host computer or to data equipment operated by an Internet Service Provider (ISP) 142 through local network 111. ISP 142, in turn, provides data communication services through the Internet 11, a global packet data communication network. Both local network 111 and Internet 11 use electrical, electromagnetic, or optical signals to carry digital data streams. The signals through the various networks, as well as the signals on network link 139 and through communication interface 141, which carry the digital data to and from computer system 140, are exemplary forms of carrier waves transporting the information.

[0134] The received code may be executed by processor 138 as it is received, and / or stored in storage device 135 or other non-volatile storage for later execution. In this manner, computer system 140 may obtain application code in the form of a carrier wave.

[0135] Embodiments of the present disclosure include concepts for a) detecting CGM sensor compression (e.g., PISA), b) improving CGM sensor accuracy or sensing accuracy by detecting compression artifacts inherent in devices such as medical devices and pharmaceutical devices, and / or c) improving the behavior of continuous subcutaneous insulin infusion therapies and related systems such as sensor-augmented insulin pumps (SAPs), hypoglycemia pause (LGS), predictive hypoglycemia pause (PLGS), or automated insulin infusion (AID), known as an artificial pancreas. As provided in the embodiments discussed herein, devices to which the embodiments are applicable are used for the following purposes: a) providing single signal and / or multi-signal detection of CGM sensor compression (e.g., PISA), b) improving the accuracy or sensing accuracy of CGM sensors by detecting compression artifacts inherent in devices such as medical devices and pharmaceutical devices, and / or c) improving the behavior of continuous subcutaneous insulin infusion therapies and related systems (such as sensor-enhanced insulin pumps (SAPs), hypoglycemia pause (LGS), predictive hypoglycemia pause (PLGS), or automated insulin infusion (AID) (referred to as "artificial pancreas")), and these devices can be implemented and utilized in conjunction with relevant processors, networks, computer systems, the Internet, and components and functions according to the embodiments disclosed herein.

[0136] Figure 12304) (or other invasive devices or diagnostic devices) can be implemented locally by a subject (or patient) at home or other desired location. However, in alternative embodiments, the blood glucose monitor can be implemented in a clinic setting or an auxiliary setting. For example, referring to Figure 12 The clinic system 158 provides a place for doctors (e.g., 164) or clinicians / assistants to diagnose patients (e.g., 159) with blood glucose-related diseases and related diseases and conditions. The blood glucose monitoring device 10 can be used as a standalone device to monitor and / or test a patient's blood glucose level. In some embodiments, the blood glucose monitoring device 10 can be the same as or similar to the blood glucose monitoring device 302.

[0137] Figure 12 The system or components (e.g., blood glucose monitoring device 10) can be adhered to or communicate with a patient as desired or required. For example, a system or combination of components thereof - including the blood glucose monitoring device 10 (or other related devices or systems, such as a controller, and / or an artificial pancreas, an insulin pump (or other invasive color plate or diagnostic device), or any other desired or required device or component) - can be in contact with, connected to, or adhered to the patient by tape or tubing (or other medical devices or components), or can communicate via a wired or wireless connection. Such monitoring and / or testing can be short-term (e.g., a clinical visit) or long-term (e.g., a clinical hospital stay or family medicine).

[0138] The doctor (clinician or assistant) can use the blood glucose monitoring device output for appropriate behavior (such as injecting insulin or feeding the patient), or other appropriate behavior or modeling. Alternatively, the blood glucose monitoring device output can be transmitted to a computer terminal 168 for current or future analysis. This transmission can be carried out via a cable or wireless or any other suitable medium. The blood glucose monitoring device output from the patient can also be transmitted to a portable device, such as a mobile device 166. The blood glucose monitoring device output with improved accuracy can be transmitted to a blood glucose monitoring center 172 for processing and / or analysis. This transmission can be implemented in a variety of ways, such as a network connection 170, which can be wired or wireless.

[0139] In addition to the blood glucose monitoring device output, the error, parameters for accuracy improvement, and any accuracy-related information can be transmitted to, for example, a computer 168 and / or a blood glucose monitoring center 172 to perform error analysis. Due to the importance of blood glucose sensors (or other invasive or diagnostic sensors or devices), this can provide centralized accuracy monitoring, modeling, and / or accuracy improvement for a blood glucose center (or other invasive or diagnostic center).

[0140] Figure 13 The block diagram of the example machine 1300 on which one or more aspects of the embodiments can be implemented is shown. The machine 1300 can include, but is not limited to, systems, methods, and computer-readable media that provide: a) single and / or multi-signal detection of CGM sensor compression (e.g., PISA), b) improving the accuracy or sensing accuracy of CGM sensors by detecting compression artifacts inherent in devices such as medical devices and pharmaceutical devices, and / or c) improving the behavior of continuous subcutaneous insulin infusion therapy and related systems such as sensor-enhanced insulin pumps (SAPs), hypoglycemic pauses (LGSs), predictive hypoglycemic pauses (PLGSs), or automated insulin infusions (AIDs) (referred to as "artificial pancreas"), and a block diagram of an example machine 1300 on which one or more embodiments (e.g., the methods discussed) can be implemented (e.g., run).

[0141] Examples of machine 1300 may include logic, one or more components, circuits (e.g., modules), or mechanisms. A circuit is a tangible entity configured to perform certain operations. In an example, the circuit may be arranged in a specified manner (e.g., within or relative to an external entity (e.g., other circuits)). In an example, one or more computer systems (e.g., stand-alone, client, or server computer systems) or one or more hardware processors (processors) may be configured by software (e.g., instructions, application portions, or applications) to operate as a circuit to perform certain operations as described herein. In an example, the software may reside on (1) a non-transitory machine-readable medium or (2) in a transmission signal. In an example, the software, when executed by the circuit's underlying hardware, causes the circuit to perform certain operations.

[0142] In an example, the circuit can be implemented mechanically or electronically. For example, the circuit can include dedicated circuitry or logic that is specifically configured to perform one or more techniques such as those discussed above, such as including a dedicated processor, a field programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In an example, the circuit can include programmable logic (e.g., as contained within a general-purpose processor or other programmable processor) that can be temporarily configured (e.g., by software) to perform certain operations. It should be understood that cost and time considerations can drive the decision to implement the circuit mechanically (e.g., in a dedicated and permanently configured circuit) or in a temporarily configured circuit system (e.g., configured by software).

[0143] Thus, the term "circuit" may refer to a tangible entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily (e.g., provisionally) configured (e.g., programmed) to operate in a specified manner or perform a specified operation. In an example, given a plurality of temporarily configured circuits, each circuit need not be configured or instantiated at any one time. For example, where a circuit may include a general-purpose processor configured by software, the general-purpose processor may be configured as correspondingly different circuits at different times. The software may configure the processor accordingly, for example, to configure a particular circuit at one time and to configure a different circuit at a different time.

[0144] In an example, a circuit can provide information to other circuits, and receive information from other circuits. In this example, a circuit can be considered to be communicatively coupled to one or more other circuits. In the case of multiple such circuits being present at the same time, communication can be achieved by the signal transmission (e.g., by appropriate circuits and buses) connecting these circuits. In an embodiment in which multiple circuits are configured or instantiated at different times, communication between such circuits can be achieved, for example, by storing and retrieving information in a memory structure accessible to multiple circuits. For example, a circuit can perform an operation and store the output of the operation in a memory device communicatively coupled thereto. Then, another circuit can access the memory device at a later time to retrieve and process the stored output. In an example, a circuit can be configured to initiate or receive communication with an input or output device, and can operate on a resource (e.g., a collection of information).

[0145] Each operation of the method examples described herein may be performed at least in part by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily configured or permanently configured, such processors may constitute circuits implemented by the processor that operate to perform one or more operations or functions. In an example, the circuits mentioned herein may include circuits implemented by the processor.

[0146] Similarly, the methods described herein can be implemented at least in part by a processor. For example, at least some of the operations of the method can be performed by one or more processors or a circuit implemented by a processor. The execution of certain operations can be distributed between the one or more processors, not only residing in a single machine, but also deployed across multiple machines. In an example, one or more processors can be located in a single location (e.g., in a home environment, an office environment, or as a server group), while in other examples, the processor can be distributed across multiple locations.

[0147] The one or more processors may also be operable to support execution of the relevant operations in a cloud computing environment, or as software as a service (SaaS) that may be executed on a remote server and accessed or used by one or more client devices. For example, at least some operations may be performed by a group of computers (as an example of a machine including a processor), where the operations are accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., application program interfaces (APIs)).

[0148] Example embodiments (e.g., apparatus, systems, or methods) may be implemented in digital electronic circuitry, computer hardware, firmware, software, or any combination thereof. Example embodiments may be implemented using a computer program product (e.g., a computer program tangibly embodied in an information carrier or machine-readable medium for execution by, or to control the operation of, a data processing apparatus (e.g., a programmable processor, a computer, or multiple computers)).

[0149] A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a software module, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers that are distributed at one site or across multiple sites and interconnected by a communication network.

[0150] In an example, the operations may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Examples of method operations may also be performed by, and example apparatus may be implemented as, special purpose logic circuitry (e.g., a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC)).

[0151] The computing system may include a client and a server. The client and the server are typically remote from each other and typically interact via a communication network. The relationship between the client and the server is generated by computer programs that run on respective computers and have a client-server relationship with each other. In the embodiments of the deployed programmable computing system, it will be understood that both hardware architecture and software architecture need to be considered. Specifically, it will be understood that the choice of implementing certain functions in permanently configured hardware (e.g., ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or in a combination of permanently and temporarily configured hardware can be a design choice. The hardware (e.g., machine 1300) architecture and software architecture that can be deployed in an example embodiment are described below.

[0152] In some examples, the machine 1300 may operate as a standalone device. In some examples, the machine 1300 may be connected (e.g., using a network) to other machines.

[0153] In a networked deployment, the machine 1300 may operate in the capacity of a server or a client user machine in server-client user network environment. In an example, the machine 1300 may function as a peer machine in a peer-to-peer (or other distributed) network environment. The machine 1300 may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a network appliance, a network router, a switch, or a bridge, or any other machine capable of executing (sequentially or otherwise) instructions that specify actions to be taken (e.g., performed) by the machine 1300. While a single machine 1300 is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0154] Example machine (e.g., computer system) 1300 may include a processor 1302 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), main memory 1304, and static memory 1306, some or all of which may communicate with each other via a bus 1308. Machine 1300 may also include a display unit 1310, an alphanumeric input device 1312 (e.g., a keyboard), and a user interface (UI) navigation device 411 (e.g., a mouse). In an example, display unit 1310, input device 1312, and UI navigation device 1314 may be touch screen displays. Machine 1300 may also include a storage device (e.g., a drive unit) 1316, a signal generating device 1318 (e.g., a speaker), a network interface device 1320, and one or more sensors 1321 (e.g., a global positioning system (GPS) sensor, a compass, an accelerometer, or other sensors). In some embodiments, sensor 1321 may be the same as or similar to sensor 110 and / or sensor 410.

[0155] The storage device 1316 may include a machine-readable medium 1322 having stored thereon one or more sets of data structures or instructions 1324 (e.g., software) that embody or are utilized by any one or more of the methodologies or functionality described herein. The instructions 1324 may also reside, completely or at least partially, within the main memory 1304, within the static memory 1306, or within the processor 1302 during execution by the machine 1300. In an example, one or any combination of the processor 1302, the main memory 1304, the static memory 1306, or the storage device 1316 may constitute a machine-readable medium.

[0156] Although the machine-readable medium 1322 is illustrated as a single medium, the term "machine-readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) configured to store one or more instructions 1324. The term "machine-readable medium" may also be considered to include any tangible medium that can store, encode, or carry instructions for execution by a machine and cause the machine to perform any one or more of the methods disclosed herein, or any tangible medium that can store, encode, or carry data structures used by or associated with such instructions. Thus, the term "machine-readable medium" may be considered to include, but is not limited to, solid-state memory, and optical and magnetic media. Specific examples of machine-readable media may include non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices, magnetic disks (such as internal hard disks and removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks.

[0157] Instructions 1324 may also be sent or received over a communication network 1326 using a transmission medium via the network interface device 1320 using any of a number of transmission protocols (e.g., Frame Relay, IP, TCP, UDP, HTTP, etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), a mobile telephone network (e.g., a cellular network), a plain old telephone (POTS) network, and a wireless data network (e.g., a wireless network known as a cellular network). The IEEE 802.11 series of standards, known as The term "transmission medium" shall be deemed to include any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communication signals or other intangible media to facilitate communication of such software.

[0158] Any processor disclosed herein may be part of or in communication with a machine (e.g., a computer device, a logic device, a circuit, an operating module (hardware, software, and / or firmware), etc.). A processor may be hardware (e.g., a processor, an integrated circuit, a central processing unit, a microprocessor, a core processor, a computer device, etc.), firmware, software, etc., configured to perform operations by executing instructions contained in computer program code, algorithms, program logic, control logic, data processing program logic, artificial intelligence programming, machine learning programming, artificial neural network programming, automated reasoning programming, etc. The processor may receive, process, and / or store data.

[0159] Any processor disclosed herein may be an extensible processor, a parallelizable processor, a multithreaded processing processor, etc. The processor may be a computer in which processing power is selected according to expected network traffic (e.g., data flow). The processor may include an integrated circuit or other electronic device (or device collection) capable of performing operations on at least one instruction, which may include a reduced instruction set core (RISC) processor, a complex instruction set computer (CISC) microprocessor, a microcontroller unit (MCU), a CISC-based central processing unit (CPU), a digital signal processor (DSP), a graphics processing unit (GPU), a field programmable gate array (FPGA), etc. The hardware of such devices may be integrated onto a single substrate (e.g., a silicon "die"), distributed between two or more substrates, etc. Each functional aspect of the processor may be implemented separately as software or firmware associated with the processor.

[0160] The processor may include one or more processing modules or operating modules. The processing modules or operating modules may be software operating modules or firmware operating modules configured to implement any of the functions disclosed herein. The processing modules or operating modules may be embodied as software and stored in a memory operatively associated with the processor. The processing modules may be embodied as web applications, desktop applications, console applications, etc.

[0161] The processor may include, or be associated with, a computer or machine-readable medium. The computer or machine-readable medium may include a memory. Any memory discussed herein may be a computer-readable memory configured to store data. The memory may include volatile or non-volatile memory, temporary or non-transient memory, and may be embodied as internal memory, active memory, cloud storage, etc. Examples of memory may include flash memory, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash-EPROM, compact disc (CD)-ROM, digital optical disc (DVD), optical storage device, optical medium, carrier wave, cassette tape, magnetic tape, magnetic disk storage device or other magnetic storage device, or any other medium that may be used to store desired information and may be accessed by the processor.

[0162] The memory can be a non-transient computer-readable medium. As used herein, the term "computer-readable medium (or machine-readable medium)" is an extensible term and refers to any medium or any memory that participates in providing instructions to the processor for execution, or any mechanism for storing or transmitting information in a machine (e.g., computer) readable form. This medium can store computer-executable instructions to be executed by processing elements and / or control logic, and data manipulated by processing elements and / or control logic, and can take various forms, including but not limited to non-volatile media, volatile media, transmission media, etc. A computer or machine-readable medium can be configured to store one or more instructions or computer programs thereon. An instruction or computer program can be in the form of an algorithm, program logic, etc. that causes a processor to perform any function disclosed herein.

[0163] Embodiments of the memory may include processor modules and other circuitry to allow data to be transferred to and from the memory, including data to and from other components of the communication system. This transfer may be performed via hardwired or wireless transmission. The communication system may include a transceiver, which may be used in combination with switches, receivers, transmitters, routers, gateways, waveguides, etc., to communicate via a communication method or protocol for controlled and coordinated signal transmission and processing to any other component or combination of components of the communication system. This transfer may be performed via a communication link. The communication link may be electronic, optical, optoelectronic, quantum, etc. Communication may be via Bluetooth, near-field communication, cellular communication, telemetry communication, Internet communication, etc.

[0164] The transmission of data and signals can be via a transmission medium. Transmission media can include coaxial cables, copper wires, optical fibers, etc. Transmission media can also take the form of sound waves or light waves (such as those generated during radio wave and infrared data communications), or other forms of propagated signals (e.g., carrier waves, digital signals, etc.).

[0165] Any processor can communicate with other processors of other devices (e.g., computer devices, computer systems, laptop computers, desktop computers, etc.). For example, the processor of system configuration 100 can communicate with the processor of another computing device 104, and the processor of computing device 104 can communicate with the processor of a display, sensor 110, etc. Any processor can have a transceiver or other communication device / circuitry to facilitate the transmission and reception of wireless signals. Any processor can include an application programming interface (API) as a software intermediary that allows two or more applications to talk to each other. The use of an API can allow software on one processor to communicate with software on another processor of another (one or more) device.

[0166] Any data transfer or communication between two components may be a push operation and / or a pull operation. For example, data transfer between the processor 106 and the memory 108 or the processor 106 and the sensor 110 may be a push operation (e.g., data may be pushed from the memory) and / or a pull operation (e.g., the processor may pull data from the memory), and data transfer between another system and the computing device 104 may be a push and / or pull operation.

[0167] As the data is received by the component, it can be processed in real time, stored in memory for later processing, or some combination of the two. After being processed by the component, the processed data can be used in real time, stored in memory for later use, or some combination of the two. The pre-processed data and / or the processed data can be encoded, tagged, or marked before, during, or after being stored in memory.

[0168] As described herein, the system configuration 100 may include a memory 108 containing a computer program (eg, software instructions for the stress detection system 102 ) that, when executed, may cause the processor 106 to perform any of the functions / operations disclosed herein.

[0169] The computer program can cause the processor to execute one or more machine learning models (e.g., linear regression model, tree-based model, perceptron-based model, Gaussian-based model, etc.) It is expected that at least one of these machine learning models is a random forest model or an AdaBoost machine learning model.

[0170] Examples

[0171] CGM sensors widely used in diabetes treatment are susceptible to so-called "compression artifacts," or pressure-induced sensor attenuation (PISA). Compression artifacts often occur when the sensor is compressed (for example, when someone sleeps with their head resting on the arm where the sensor is inserted) and are characterized by a rapid drop in sensor readings followed by an eventual recovery. These deviations negatively impact various aspects of diabetes treatment.

[0172] We hypothesized that: (i) under nominal conditions with no pressure on the sensor, the interstitial fluid (ISF) where the CGM sensor makes glucose measurements and the local sensor compartment (LSC) of the tissue immediately surrounding the CGM sensor needle are in equilibrium; (ii) pressure on the sensor causes compression of the LSC, which impairs equilibrium by reducing glucose influx, increasing glucose efflux, or both, and (iii) as a result, the sensor's electrochemical signal is attenuated and the sensor reading decreases until equilibrium is restored.

[0173] Based on this logic, in an embodiment, a method and system for detecting CGM sensor compression artifacts includes, but is not limited to, three components: (i) a physiological compartmental model of glucose transport between the ISF and LSC; (ii) a distribution of model parameters that are observed (and therefore fixed) under nominal conditions, i.e., in the absence of compression artifacts, and (iii) a method for tracking the model parameters in real time from CGM sensor data and detecting deviations from the nominal conditions (parameters) (interpreted as compression artifacts).

[0174] One aspect of embodiments of the method and system is to detect CGM sensor lows in real time, thereby preventing low-point effects that negatively impact diabetes treatment, such as false hypoglycemia alarms or insulin withdrawal in an insulin infusion system. For one purpose of one aspect of embodiments of the present invention, the insulin infusion system can be: (i) a sensor-augmented insulin pump (SAP) therapy; (ii) a hypoglycemia pause (LGS) system or a predictive hypoglycemia pause (PLGS) system, or (iii) an automated insulin infusion (AID) (referred to as an "artificial pancreas").

[0175] One aspect of embodiments of the present invention generally relates to, but is not limited to, pharmaceutical devices and medical devices for monitoring blood glucose levels in the treatment of diabetes and other metabolic conditions, including but not limited to type 1 and type 2 diabetes, type 2 (T1D, T2D), latent autoimmune diabetes in adults (LADA), postprandial or reactive hyperglycemia, or insulin resistance. In alternative embodiments, the present invention improves the accuracy of continuous glucose monitoring (CGM) sensors by detecting compression artifacts inherent to these devices. This improves the performance of continuous subcutaneous insulin infusion therapies and related systems, such as sensor-augmented insulin pumps (SAPs), hypoglycemia pause (LGS), predictive hypoglycemia pause (PLGS), or automated insulin infusion (AID), known as an "artificial pancreas."

[0176] In an alternative embodiment, one aspect of the present invention provides, among other things: a physiologically based method for detecting continuous glucose monitoring (CGM) sensor artifacts (referred to as "compression lows") or pressure induced sensor attenuation (PISA). CGM sensors widely used to treat diabetes have been susceptible to compression low artifacts since their introduction over 20 years ago and remain a problem. Compression artifacts often occur when the sensor is compressed (e.g., when someone sleeps with their head resting on the arm into which the sensor is inserted) and are characterized by a rapid drop in sensor reading followed by an eventual recovery. These deviations negatively impact various aspects of diabetes treatment, including, but not limited to, false hypoglycemia alarms and incorrect treatment actions taken by low glucose pause (LGS), predictive low glucose pause (PLGS), or automated insulin infusion (AID) systems.

[0177] One aspect of an embodiment of the present invention is based on a physiological model of glucose concentration at the LSC and the resulting glucose flux under normal conditions and under pressure in the sensing area. The pressure low hypothesis assumes that: (1) under nominal conditions with no pressure on the sensor, the interstitial fluid (ISF) where the CGM sensor makes glucose measurements and the local sensor compartment (LSC) of the tissue immediately surrounding the CGM sensor needle are in equilibrium, and therefore, the glucose flux into and out of the LSC is in equilibrium; and (2) pressure on the sensor causes compression of the LSC, which impairs the equilibrium by reducing the influx of glucose and possibly oxygen (not accounted for in the model), increasing the outflow of glucose, or both. As the glucose influx-outflow balance is impaired, the electrochemical signal of the sensor decays and the sensor reading decreases until equilibrium is restored.

[0178] The compartmental model can be used to describe the phenomenon of blood glucose diffusion between the ISF and LSF as follows: the flow balance between the ISF and LSC is controlled by two parameters: influx and efflux. The formal compartmental diagram of ISF-LSC fluid exchange can be described by the following equation:

[0179]

[0180] Among them, G LSc and G ISF are the glucose concentrations at the LSC and ISF, respectively, k1 is the gap, and k0 is the glucose transport rate to the LSC, which is set to

[0181] The model input is a running estimate G obtained at a specific frequency in different embodiments of the method. ISF , for example, every 30 seconds if using internal sensor data, or every minute for some CGM devices (e.g., Abbot Libre 3), or every 5 minutes for other CGM devices (e.g., Dexcom G6, G7). The model output is the sensor reading G LSC — is a function of the glucose concentration in the local sensor compartment. The gap parameter k1 is identified by solving the above equation (1) in real time using the input and output. Under nominal conditions, k1 has a stable value that increases when experiencing a low point of pressure. Therefore, when G is detected ISF With G LSC When there is a difference between G and G, the gap parameter k1 increases. Obviously, the estimated value of the gap parameter k1 does not depend on G. ISFTherefore, the gap is strictly determined by the difference between ISF and LCS glucose concentrations and is independent of interstitial fluid glucose fluctuations. This is an important design feature of the method, which ensures that the detection of the compressive low point is independent of the normal physiological fluctuations of ISF glucose.

[0182] One of the key elements of one aspect of an embodiment of the present invention is the nominal distribution of the gap parameter k1. Once the nominal distribution is determined, deviations of the real-time estimate of the gap parameter k1 from the nominal distribution indicate the occurrence of a compression low. The nominal distribution of the gap parameter k1 is obtained using synchronized data from multiple (up to 4) sensors simultaneously inserted into the same individual. In such a multi-sensor environment, one and rarely two of the sensors may experience a compression low; however, the true ISF glucose concentration is measured by the other sensors. Therefore, the model estimation is performed as follows: (1) Model input (e.g., G IsF ) is obtained from the data of the sensor that is not affected by the low point of pressure within a 2.5 minute window; (2) the output of the model (such as G LSc ) is obtained from data from sensors affected by the compression low point within the same time window; (3) the model is identified by using defined input and output signals to obtain a real-time estimate of the gap parameter k1, thereby quantifying the outflow from the LSC; and (4) the time window is slid in time in small increments (e.g., 1 minute) to produce any changes in the tracking gap in real time.

[0183] Figure 6 An example of this process in a multi-sensor environment is given in Figure 1. CGM readings from three different sensors are presented (blue, black, and red solid lines). The purple dashed line represents the G calculated according to step 1. ISF Signal. Figure 6 In Figure 1, sensor Abd1 exhibits a sequence of two compression lows, and the corresponding identified gap (second panel from the top) exhibits higher values ​​of k1 during the time period with compression lows. Sensors Arm1 and Arm2 exhibit no compression lows, and the corresponding identified gap for each sensor (third and fourth panels, respectively) shows constant values. In panel B, sensor Arm2 exhibits a compression low marked by an increase in the gap parameter k1, while the other two sensors remain stable.

[0184] Using a dataset of N=44 individuals wearing up to 4 sensors each and following steps 1-4 above, it was determined that the nominal distribution of the gap parameter k1 and the distribution of this parameter during compression lows have the following characteristics:

[0185] Table 1 Nominal distribution and compression-low point distribution of k1

[0186]

[0187] As shown in Table 1, these characteristics are sufficiently different between the nominal condition and the compression low point condition to allow the compression low point to be identified by measuring whether the real-time estimate of the gap parameter k1 is within normal limits.

[0188] Once the nominal distribution of the gap parameter k1 is significantly different from the distribution of k1 during the low-pressure condition, the logic for marking the low-pressure condition in real time can be as follows: (1) In various embodiments of the method, the model input (e.g., G ISF ) is obtained from the data of sensors that are not affected by the low point of pressure within a certain time window (e.g., 1 minute, 2.5 minutes, 5 minutes), or is obtained by delaying, extrapolating, predicting or smoothing the data of a single sensor using the duty cycle of the sensor that is not affected by the low point of pressure; (2) the output of the model (e.g., G LSC ) is whether the real-time data of the sensor is affected by the compression low point; (3) the model is identified by: using defined input and output signals to obtain a real-time estimate of the gap parameter k1; (4) the time window is slid in time in small increments (e.g., 1 minute) to produce real-time tracking of any changes in the gap parameter k1; (5) the occurrence of the compression low point is marked when the value of the gap parameter k1 exceeds a predefined threshold (e.g., about 1.1) corresponding to a cutoff point that well distinguishes the nominal distribution of k1 from the compression low point distribution; and (6) the end of the compression low point is noted when the value of the gap parameter k1 drops to a predefined threshold k1 (e.g., about 0.9) corresponding to a cutoff point that well distinguishes the nominal from the compression low point distribution. (5) when the value of the gap parameter k1 exceeds a predefined threshold corresponding to a cutoff point that well distinguishes the nominal from the compression low point distribution k1, e.g., about 1.1; and (6) when the value of the gap parameter k1 falls below a predefined threshold k1 corresponding to a cutoff point that well distinguishes the nominal from the compression low point distribution k1, e.g., about 0.9. Step (6) may only be applicable to single-sensor solutions when the sensor trajectories are used as the robot input and output of the physiological model, as described in the next section.

[0189] As an alternative to steps (5) and (6) above, a time series of k1 values ​​can be used as input to a pressure valley detection process using a time series forecasting method, machine learning techniques, or other methods. An example of such an implementation is described.

[0190] Enter G ISF and output G ISF A single sensor solution that obtains data from the same sensor stream and uses it to mark the low points of pressure experienced by that sensor is the intended practical implementation of this method. In this method, the steps 1-6 specified above are followed, and the model input G ISFIt is obtained from a short-term (a few minutes ahead) delay, extrapolation, or prediction of sensor values ​​(e.g., using linear regression, autoregression, moving average, or other standard time series prediction techniques).

[0191] The extrapolation / forecast range is determined by the frequency of data collection and can typically be 5 times the time interval between consecutive data points, e.g. 2.5 minutes to 5 minutes for a data collection cadence of 30 seconds to 60 seconds. Figure 8 The lower panel shows the expected typical behavior of the gap parameter k1 as described in steps (5) and (6): (i) at the time of the compression low, the delayed trajectory (the input to the method) remains above the sensor data, and this is interpreted as an increase in glucose outflow from the LSC causing the gap parameter k1 to increase rapidly; (ii) at the end of the compression low, the delayed trajectory remains below the sensor data, and this is interpreted as an influx of glucose into the LSC causing the gap parameter k1 to decrease below its nominal limit. The third possible compression low is not marked because the gap parameter k1 amplitude does not exceed the predefined thresholds in steps (5) and (6).

[0192] To validate some of these examples, we used a training dataset and a test dataset with manually annotated "reference truth" compression low points. There was CGM data from 111 individuals, totaling 94,610 hours of sensor data: 67 subjects (60.4%) were assigned to the training dataset, and 44 subjects (39.6%) were assigned to an independent test dataset. The training dataset was used for training and cross-validation of the method components, while the test dataset was used only to test the method after the method components were finalized and fixed.

[0193] The time series were reviewed for the following: using sensor data from all 111 subjects, the compression lows (e.g., subcompression lows or near subcompression lows) in each time series with a minimum BG value less than 85 mg / dL were annotated, resulting in 1623 subcompression lows or near subcompression lows. Of these 1623 annotations, 1089 were in the training dataset from 58,403 hours of sensor data. These annotations were used as a reference for testing the performance of the present method in various embodiments, as described in subsequent sections. Specifically, steps 1-4 described above in Section 4.5 generate a time series of parameter k1 values ​​that can be used to determine the presence of compression lows (embodiments of the method or related system) in several different ways.

[0194] This embodiment of the method (or related system) follows directly from steps (5) and (6) described above - k1 exceeding a threshold by a certain degree is used to determine the occurrence of a compression low point. Figure 9AFigure 1 shows the ROC performance curve for the test set. In this case, the k1 time series was generated using a moving average; as mentioned above, the k1 time series could be generated using other short-term (a few minutes ahead) forecasting methods, such as delayed, extrapolated, or forecasted sensor values ​​using linear regression, autoregression, or other standard time series forecasting techniques. The area under the ROC curve is 96%, which generally indicates very good performance. Another view of the same performance is provided by the precision-recall (PR) curve, which summarizes the Figure 9B The trade-off between the true positive rate and positive predictive value of the model in the PR curve is . In this case, the average precision (AP) score of the PR curve is 0.38, which means that the weighted average precision across all thresholds (the recall at these thresholds is used as weight) is 0.38 - generally considered a "good" result. We should note that while the area under the ROC curve is correlated with the AP score of the PR curve, the AP score is sometimes considered to be more sensitive in distinguishing between (usually good) algorithms with similar performance.

[0195] This embodiment of the following method (or related system) uses the k1 time series generated by moving average extrapolation as input to two standard machine learning models: Random Forest (RF) and AdaBoost (AB). In other alternative embodiments, the machine learning algorithm can be, but is not limited to, Gradient Boosted Trees, neural networks, support vector machines, etc., or any combination of the above. These models utilize the k1 time series in combination with other sensor internal data (such as temperature), but do not use sensor blood glucose readings as input. In other words, for these models, the k1 time series is the only input related to blood glucose.

[0196] This embodiment of the following method (or related system) uses the k1 time series generated by moving average extrapolation and sensor blood glucose data as input to the same two standard machine learning models described: Random Forest and AdaBoost.

[0197] From the various embodiments described, we can conclude that the present method performs well as a simple, single-sensor, stand-alone threshold detector for compressive low points, but its performance can be improved by using advanced machine learning models and additional inputs (e.g., sensor blood glucose). The trade-off may be the required data processing power.

[0198] It should be understood that the embodiments disclosed herein can be modified to meet a specific set of design criteria. For example, any component discussed herein can be of any suitable number or type to meet a specific purpose. Therefore, while certain exemplary embodiments of the systems and methods of making and using the systems disclosed herein have been discussed and illustrated, it should be clearly understood that the present disclosure is not limited thereto, but may be embodied and practiced in other ways within the scope of the appended claims.

[0199] It should be understood that some components, features, and / or configurations may be described in conjunction with only one particular embodiment, but these same components, features, and / or configurations may be applied or used with many other embodiments and should be considered applicable to the other embodiments unless otherwise stated or unless such components, features, and / or configurations are technically impossible to use with the other embodiments. Therefore, the components, features, and / or configurations of the various embodiments may be combined together in any manner, and this statement expressly contemplates and discloses such combinations.

[0200] Those skilled in the art will appreciate that the present disclosure may be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the presently disclosed embodiments are considered to be illustrative rather than restrictive in all respects. The scope of the present disclosure is indicated by the appended claims rather than the foregoing description, and all variations and equivalents falling within its meaning and scope are intended to be included therein. In addition, the disclosure of a range of values ​​is the disclosure of each numerical value within the range, including endpoints.

Claims

1. A system for real-time automatic detection of sensor compression in continuous blood glucose monitoring, the system comprising: at least one sensor; and at least one processor in communication with the at least one sensor, the at least one processor executing program code, wherein the at least one processor is programmed or configured to cause the processor to: retrieving first measurement data comprising at least one time series of blood glucose (BG) measurements, the at least one time series being measured by the at least one sensor when not stressed; receiving second measurement data from the at least one sensor, the second measurement data comprising at least one BG measurement, the at least one BG measurement being measured by the at least one sensor; determining a gap value between BG measurement results based on the first measurement data and the second measurement data; and Based on the gap value between the BG measurements exceeding a predefined threshold, a signal output is generated indicating that the at least one sensor is compressed.

2. The system of claim 1 , further comprising: at least one memory device configured to store the first measurement data, and wherein, when configured to retrieve the first measurement data, the at least one processor is programmed or configured to cause the processor to: The first measurement data is retrieved from the memory device.

3. The system of claim 1, wherein: The BG measurement results of at least one time series in the first measurement data include a plurality of time stamps, and each time stamp is associated with a BG measurement result.

4. The system of claim 3, wherein: When configured to determine a gap value between BG measurements, the at least one processor is programmed or configured to cause the processor to: A gap value between the at least one BG measurement result of the second measurement data and a first BG measurement result associated with a first timestamp of the first measurement data is determined.

5. The system of claim 1, wherein: When configured to receive second measurement data comprising at least one BG measurement, the at least one processor is programmed or configured to cause the processor to: When the at least one sensor obtains continuous BG measurement results in real time, the second measurement data including the continuous BG measurement results is received from the at least one sensor.

6. The system of claim 5, wherein: When configured to determine a gap value between BG measurements, the at least one processor is programmed or configured to cause the processor to: Upon receiving each consecutive BG measurement result, each gap value of a plurality of gap values ​​between each consecutive BG measurement result and each BG measurement result in the first measurement data is determined in real time, wherein a first BG measurement result in the consecutive BG measurement results is associated with a first timestamp of the first measurement data.

7. The system of claim 1, wherein: The gap value is determined based on the following formula: Among them, G LSC is the glucose concentration in the local sensor compartment of the at least one sensor, G ISF is the glucose concentration in the tissue fluid, is the rate of change of glucose concentration in the local sensor compartment, k1 is the gap value, and k0 is the glucose transport rate, where 8. The system of claim 1, wherein: When configured to generate a signal output, the at least one processor is programmed or configured to cause the processor to: The at least one sensor is indicated in real time by outputting an indication that it is compressed while obtaining the BG measurement.

9. The system of claim 1 , further comprising: an insulin infusion system in communication with the at least one processor, wherein the at least one processor is programmed or configured to cause the processor to: transmitting the signal output to the insulin infusion system to indicate that the at least one sensor is compressed, wherein the signal output causes the insulin infusion system to perform at least one or more of the following: Initiate insulin infusion, continue insulin infusion, disable alarm, and / or any combination thereof.

10. The system of claim 3, wherein: The multiple timestamps are separated by one or more of: 1 minute intervals, 2.5 minute intervals, and / or 5 minute intervals.

11. The system of claim 1, wherein: The predefined threshold is between 1.05 and 1.

3.

12. The system of claim 2, further comprising: at least one additional sensor, wherein the at least one processor is programmed or configured to cause the processor to: receiving first measurement data from the at least one additional sensor, the first measurement data comprising at least a time series of BG measurements; and The first measurement data is stored in the at least one memory device.

13. The system of claim 1, wherein: The at least one time series of BG measurement results is measured by the at least one sensor before the second measurement data.

14. The system of claim 1, wherein: The first measurement data comprises at least one BG measurement extrapolated from the BG measurements of the at least one time series.

15. A system for automatically detecting the end of sensor compression in real time during continuous blood glucose monitoring, the system comprising: at least one sensor; and at least one processor in communication with the at least one sensor, the at least one processor executing program code, wherein the at least one processor is programmed or configured to cause the processor to: receiving first measurement data from the at least one sensor, the first measurement data comprising at least one time series of blood glucose (BG) measurements, the at least one time series of BG measurements measured by the at least one sensor while under compression; receiving second measurement data including at least one BG measurement from the at least one sensor, the at least one BG measurement measured by the at least one sensor after the at least one sensor has measured the at least one time series of BG measurements; determining a gap value between BG measurement results based on the first measurement data and the second measurement data; and Based on the gap value between the BG measurements being less than a predefined threshold, a signal output is generated indicating that the at least one sensor is no longer compressed.

16. The system of claim 15, wherein: The BG measurement results of at least one time series in the first measurement data include a plurality of time stamps, and each time stamp is associated with a BG measurement result.

17. The system of claim 15, wherein: When configured to receive second measurement data comprising at least one BG measurement, the at least one processor is programmed or configured to cause the processor to: When the at least one sensor obtains continuous BG measurement results in real time, the second measurement data including the continuous BG measurement results is received from the at least one sensor.

18. The system of claim 17, wherein: When configured to determine a gap value between BG measurements, the at least one processor is programmed or configured to cause the processor to: Upon receiving each consecutive BG measurement result, each gap value of a plurality of gap values ​​between each consecutive BG measurement result and each BG measurement result in the first measurement data is determined in real time, wherein a first BG measurement result in the consecutive BG measurement results is associated with a first timestamp of the first measurement data.

19. The system of claim 15, wherein: The gap value is determined based on the following formula: Among them, G LSC is the glucose concentration in the local sensor compartment of the at least one sensor, G ISF is the glucose concentration in the tissue fluid, is the rate of change of glucose concentration in the local sensor compartment, k1 is the gap value, and k0 is the glucose transport rate, where 20. The system of claim 15, wherein: When configured to generate a signal output, the at least one processor is programmed or configured to cause the processor to: An indication that the at least one sensor was not compressed when obtaining the BG measurement is output in real time.

21. The system of claim 15, further comprising: an insulin infusion system in communication with the at least one processor, wherein the at least one processor is programmed or configured to cause the processor to: transmitting the signal output to the insulin infusion system to indicate that the at least one sensor is not compressed, wherein the signal output causes the insulin infusion system to perform at least one or more of the following: Initiate insulin infusion, continue insulin infusion, disable alarm, and / or any combination thereof.

22. The system of claim 15, wherein: The predefined threshold is between 0.9 and 1.

0.

23. The system of claim 15, wherein: The at least one time series of BG measurement results is measured by the at least one sensor before the second measurement data.

24. The system of claim 15, wherein: The first measurement data comprises at least one BG measurement extrapolated from the BG measurements of the at least one time series.

25. A computer-implemented method for accurately detecting sensor compression in continuous glucose monitoring, the method comprising: receiving first measurement data comprising at least a time series of blood glucose (BG) measurements measured by a first sensor that is not stressed; receiving second measurement data comprising a plurality of BG measurements continuously measured by a second sensor under pressure; determining a plurality of gap values ​​between BG measurements based on the first measurement data and the second measurement data; as well as It is detected that the third sensor is pressed based on the distribution of the plurality of gap values.

26. The computer-implemented method of claim 25, wherein: The BG measurement results of the at least one time series in the first measurement data include a plurality of time stamps, each time stamp being associated with a BG measurement result.

27. The computer-implemented method of claim 25, comprising: The plurality of gap values ​​between the plurality of BG measurements in the second measurement data and the at least one time series in the first measurement data are determined.

28. The computer-implemented method of claim 25, wherein: The plurality of BG measurement results are continuously measured by the second sensor in real time, and each gap value of the plurality of gap values ​​is continuously determined using each BG measurement result.

29. The computer-implemented method of claim 25, wherein: The plurality of gap values ​​are determined based on the following formula: Among them, G LSC is the glucose concentration in the local sensor compartment of the at least one sensor, G ISF is the glucose concentration in the tissue fluid, is the rate of change of glucose concentration in the local sensor compartment, k1 is the gap value, and k0 is the glucose transport rate, where 30. The computer-implemented method of claim 25, comprising: The at least one sensor is indicated as being compressed when obtaining the BG measurement.

31. The computer-implemented method of claim 25, comprising: Transmitting the signal output to an insulin infusion system, the signal output indicating that the third sensor is compressed, wherein the signal output causes the insulin infusion system to perform at least one or more of the following: Initiate insulin infusion, continue insulin infusion, disable alarm, and / or any combination thereof.

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