Systems and methods for detecting sensor compression in continuous glucose monitoring (CGM) sensors

Abstract

[0003] Embodiments relate to a system and method for automatically detecting sensor compression in real time in continuous glucose monitoring. The system can 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: collect first measurement data including at least one time series of blood glucose (BG) measurements, the at least one time series being measured by the at least one sensor while the at least one sensor is not undergoing compression; receive second measurement data including at least one BG measurement from the at least one sensor; determine a clearance value between BG measurements based on the first measurement data and the second measurement data; and generate a signal output indicating that the at least one sensor is undergoing compression based on the clearance value between BG measurements being above a predetermined threshold.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This patent application is related to and claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 421,883, filed November 2, 2022, the contents of which are incorporated by reference in their entirety.

[0002] Aspects of the embodiments relate generally to pharmaceuticals and medical devices such as those used in monitoring blood glucose levels in the treatment of diabetes and other metabolic disorders, 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. Embodiments relate to systems and methods for detecting when a continuous glucose monitoring (CGM) sensor is under compression and detecting compression artifacts present in blood glucose (BG) measurements. [Background technology]

[0003] Past advances in CGM devices have contributed to diabetes treatment and led to some modern closed-loop control systems (e.g., artificial pancreas). Despite advances in CGM, CGM sensors can be vulnerable to compression artifacts (e.g., pressure-induced sensor attenuations (PISA)). Compression artifacts can appear in measurement data when a sensor is compressed (e.g., compressed) during measurement collection. For example, compression artifacts can occur when a subject with an attached sensor falls asleep with the sensor insertion area (e.g., arm) facing down. Compression artifacts can be characterized by a sudden drop in the magnitude of the sensor measurement (e.g., to a low reading) followed by an eventual recovery of the sensor measurement (e.g., to a normal reading). Such a drop in sensor measurement can lead to false hypoglycemia alarms, insulin shutoff in low glucose suspend or closed-loop systems, and other adverse effects on diabetes treatment. There are no reliable methods for detecting and / or predicting compression artifacts, and no methods have been developed to date for preventing compression artifacts by predicting them. [Prior art documents] [Patent documents]

[0004] [Non-Patent Document 1] Cisco Systems, Publication No. 1-587005-001-3 (6 / 99), "Internetworking Technologies Handbook," Chapter 7: "Ethernet Technologies," pages 7-1 to 7-38 [Non-patent document 2] Standard Microsystems Datasheet "LAN91C111 10 / 100 Non-PCI Ethernet Single-Chip MAC+PHY" Datasheet, Revision 15 (02-20-04) Summary of the Invention

[0005] An exemplary embodiment may relate to a system for automatic real-time detection of sensor compression in a CGM. 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 be programmed or configured to execute program code. The at least one processor may be programmed or configured to cause the processor to collect first measurement data including at least one time series of BG readings. The at least one time series may be measured by the at least one sensor while the at least one sensor is not experiencing compression. The at least one processor may be programmed or configured to cause the processor to receive second measurement data including at least one BG reading from the at least one sensor. The at least one BG reading may be measured by the at least one sensor. The at least one processor may be programmed or configured to cause the processor to determine a clearance value between BG readings based on the first measurement data and the second measurement data. The at least one processor can be programmed or configured to cause the processor to generate a signal output indicating that the at least one sensor is experiencing compression based on the clearance value between the BG measurements being above a predetermined threshold.

[0006] An exemplary embodiment may relate to a system for automatically detecting the end of a 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 be capable of executing program code. The at least one processor may be programmed or configured to cause the processor to receive first measurement data from the at least one sensor, the first measurement data including at least one time series of BG readings. The at least one time series of BG readings may be measured by the at least one sensor while the at least one sensor is receiving a compression. The at least one processor may be programmed or configured to cause the processor to receive second measurement data from the at least one sensor, the second measurement data including at least one BG reading. The at least one BG reading may be measured by the at least one sensor after the at least one time series of BG readings is measured by the at least one sensor. The at least one processor may be programmed or configured to cause the processor to determine a clearance value between BG readings based on the first measurement data and the second measurement data. The at least one processor can be programmed or configured to cause the processor to generate a signal output indicating that the at least one sensor is no longer experiencing compression based on the clearance value between the BG measurements being less than a predetermined threshold.

[0007] An exemplary embodiment may relate to a computer-implemented method for accurately detecting sensor compression in continuous glucose monitoring. The method may include receiving first measurement data including at least one time series of BG readings measured by a first sensor not undergoing compression. The method may also include receiving second measurement data including multiple BG readings measured consecutively by a second sensor undergoing compression. The method may also include determining multiple clearance values ​​between the BG readings based on the first measurement data and the second measurement data. The method may also include detecting that a third sensor is undergoing compression based on a distribution of the multiple clearance values.

[0008] Other features and advantages of the present disclosure will become more apparent from the following detailed description when read in conjunction with the accompanying drawings, in which like numerals refer to like elements and in which: [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 illustrates an exemplary system configuration of an embodiment of a system for detecting sensor compression of a CGM sensor as disclosed herein. [Figure 2] 1 illustrates an exemplary method for detecting sensor compression of a CGM sensor as disclosed herein. [Figure 3] FIG. 1 illustrates an exemplary system implementation for detecting sensor compression of a CGM as disclosed herein. [Figure 4] FIG. 1 illustrates an exemplary system for implementing an embodiment of a CGM sensor compression detection disclosed herein. [Figure 5] 1A-1C illustrate exemplary plots of unchanged CGM sensor measurements without compression artifact and simulated CGM measurements representative of compression artifact, as disclosed herein. [Figure 6] FIG. 10 illustrates an exemplary plot of CGM sensor measurements from multiple sensors, along with a plot of the clearance values ​​for each sensor over a time series of BG measurements, as disclosed herein. [Figure 7] 10A-10C illustrate exemplary distributions of clearance values ​​for a sensor in a normal state and a sensor under stress, as disclosed herein. [Figure 8] FIG. 10 illustrates an exemplary plot of a time series of BG measurements for a single sensor including a compression artifact detected based on clearance values ​​over the time series of BG measurements, as disclosed herein. [Figure 9A] FIG. 10 illustrates an exemplary plot of the area under the receiver operating characteristic curve of a classifier model used to classify a time series of BG measurements as containing a compression artifact based on clearance values ​​to detect sensor compression, as disclosed herein. [Figure 9B] FIG. 10 illustrates an exemplary plot of precision-recall curves for a classifier model used to classify a time series of BG measurements as containing compression artifacts based on clearance values ​​to detect sensor compression, as disclosed herein. [Figure 10A] FIG. 1 illustrates an exemplary system configuration of an exemplary computer device disclosed herein. [Figure 10B] FIG. 1 illustrates an exemplary system environment in which the systems, methods and / or computer-readable media disclosed herein may be implemented. [Figure 11] FIG. 1 is a block diagram of an example computer system capable of implementing the systems, methods and / or computer-readable media disclosed herein. [Figure 12] FIG. 1 illustrates an exemplary environment in which the systems, methods and / or computer-readable media disclosed herein may be implemented. [Figure 13] FIG. 1 is a block diagram of an example machine capable of implementing the systems, methods and / or computer-readable media disclosed herein. DETAILED DESCRIPTION OF THE INVENTION

[0010] Various embodiments provide the ability to prevent compression low effects that adversely affect diabetes treatment by detecting compression lows (e.g., compression artifacts) in CGM sensors in real time. Such adverse effects that can be prevented by various embodiments include false hypoglycemia alarms and insulin blockage in insulin delivery systems. Various embodiments can fully identify pressure-induced sensor attenuation (PISA) in real time and provide an indication that PISA is occurring. Embodiments can improve the accuracy of CGM sensors by detecting compression artifacts inherent to CGM devices. Embodiments can provide detection of compression artifacts that are independent of normal physiological fluctuations in interstitial fluid (ISF) glucose, thereby reducing false positive detections. Embodiments improve the operation of subcutaneous insulin infusion therapy and related systems, such as sensor-augmented pumps (SAPs), low glucose suspends (LGSs), predictive low glucose suspends (PLGSs), or automated insulin delivery (AIDs) (e.g., artificial pancreas). Some embodiments achieved a receiver operating characteristic (ROC) curve with an area under the ROC curve of 96%, indicating generally good performance. Some embodiments produced a precision-recall (PR) curve with an average precision (AP) score of approximately 0.38, i.e., a weighted average precision across all thresholds where recall is used as a weight, of approximately 0.38. This result is generally considered to be a good result. Thus, embodiments provide reliable detection of compression artifacts in BG measurement data.

[0011] Various embodiments improve the ability of a computer (e.g., a computer processor) to automatically detect PISA and improve diabetes treatment. Prior to the development of various embodiments disclosed herein, computers not programmed or configured with aspects of some embodiments were unable to automatically detect PISA in CGM sensors and diabetes treatment. According to embodiments, CGM sensors may no longer be vulnerable to compression artifacts. Compression artifacts may be automatically detected in real time in measurement data when the sensor is compressed (e.g., subjected to compression) during measurement collection. For example, embodiments may detect compression artifacts when a subject with a sensor is sleeping with the sensor inserted (e.g., on their arm). Embodiments may detect PISA in such sensor measurements to predict and / or prevent false hypoglycemic alarms, low glucose suspend or insulin blockage in closed-loop systems, and other effects that may adversely affect diabetes treatment. The ability to accurately predict compression artifacts allows for real-time intervention when compression artifacts occur to enhance diabetes treatment.

[0012] Embodiments may determine clearance values ​​continuously in real time so that clearance values ​​can be plotted and identified over time while the CGM sensor is collecting measurements. Such determinations are made continuously while measurement data is being collected by the CGM sensor to provide an overall view of the sensor's status with respect to compression over time. By repeatedly determining clearance values ​​in real time, embodiments may improve the computer's ability to detect and process or ameliorate real-time compression artifacts.

[0013] 1 illustrates an exemplary system configuration 100 of an embodiment of a system operable via program code (e.g., software instructions executed by a processor) for detecting sensor compression in a CGM (e.g., via compression artifact). The various components of FIG. 1 may be implemented in and / or processed by a processor (e.g., a central processing unit (CPU)) and / or any number of distributed processors (e.g., a distributed computer system) coupled to memory and connected via a communications network. Each component illustrated in FIG. 1 will be described in the context of an exemplary embodiment.

[0014] 1, embodiments relate to a system configured to detect sensor compression in a CGM. In some embodiments, a system configuration 100 can automatically detect sensor compression in a CGM in real time. In some embodiments, the system configuration 100 can include a compression detection system 102, a computing device 104, a processor 106, a memory 108, and a sensor 110.

[0015] In some embodiments, the system configuration 100 can include at least one sensor (e.g., sensor 110). The at least one sensor can measure and / or collect BG measurements associated with timestamps. The BG measurements (e.g., BG measurement data) can 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 can be associated with exactly one timestamp, such that a group 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 can span a variety of times and can be of any variety of lengths (e.g., the number of timestamps and BG measurements in the time series). For example, the at least one time series of BG measurements can include 30 minutes (e.g., in duration) of measurement data measured by the at least one sensor. The at least one sensor can measure and / or collect BG measurements over a period of time at specified time intervals (e.g., every 30 seconds and / or every minute), which can define the resolution of the time series of BG measurements. For example, the timestamps in a time series of BG measurements may be separated by any one or more of the following intervals: 30 seconds, 1 minute, 2.5 minutes, and / or 5 minutes. Other time intervals for measurement collection and / or resolution of the time series may also be used.

[0016] In some embodiments, system configuration 100 can include multiple sensors. For example, system configuration 100 can include at least one additional sensor (including sensor 110). The at least one processor can be programmed or configured to cause the processor to receive first measurement data from the at least one additional sensor, the first measurement data including at least one time series of BG measurements. The at least one processor can store the first measurement data collected by the additional sensor in at least one memory device (e.g., memory 108).

[0017] In some embodiments, system configuration 100 can include at least one processor (e.g., processor 106) in communication with at least one sensor. The at least one processor can execute program code (e.g., software instructions) for performing one or more steps of a method. In some embodiments, compression 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 compression in the CGM in real time relative to the at least one sensor collecting measurement data.

[0018] In some embodiments, at least one processor can be programmed or configured (e.g., via software instructions) to cause the processor to receive measurement data from at least one sensor, the measurement data including at least one time series of BG measurements measured by the at least one sensor. The measurement data can be transmitted from the at least one sensor to the at least one processor in real time (e.g., relative to the sensor collecting 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 so that the measurement data can be accessed by the at least one processor at a time later than the time the at least one sensor collected the measurement data.

[0019] In some embodiments, at least one time series of BG readings can include multiple timestamps. Each timestamp can be associated with a BG reading. For example, each timestamp can represent a relative or absolute time at which the BG reading was collected by a sensor (e.g., sensor 110). In some embodiments, the multiple timestamps can be separated by any one or more of 30-second intervals, 1-minute intervals, 2.5-minute intervals, and / or 5-minute intervals.

[0020] In some embodiments, at least one processor can be programmed or configured (e.g., via software instructions) to cause the processor to retrieve first measurement data including at least one time series of BG readings. For example, the at least one processor can retrieve the first measurement data from a memory coupled to the at least one processor. The at least one sensor can collect the first measurement data and transmit it to the memory for storage and / or to the at least one processor. The at least one sensor can measure the at least one time series of BG readings during compression or without compression. In some embodiments, the at least one processor can be programmed or configured to cause the processor 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 also be programmed or configured to cause the processor to retrieve the first measurement data from a storage component (e.g., memory 108) for later use after the first measurement data is collected by the at least one sensor. In some embodiments, at least one sensor may measure and / or collect second measurement data after measuring and / or collecting at least one time series of BG measurements (e.g., first measurement data).

[0021] In some embodiments, at least one processor can be programmed or configured to cause the processor to receive second measurement data (e.g., from at least one sensor). The second measurement data can include at least one BG measurement. The at least one BG measurement can be measured by the at least one sensor (e.g., in real time). The second measurement data can be transmitted from the at least one sensor to the at least one processor in real time (e.g., relative to the sensor collecting the second measurement data). Alternatively, the second 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 second measurement data at a later time relative to the time the at least one sensor collected the second measurement data. In this manner, the at least one processor can be programmed or configured to cause the processor to receive the second measurement data for immediate use, and / or the at least one processor can be programmed or configured to cause the processor to retrieve the second measurement data from a storage component (e.g., memory 108) for later use after the second measurement data is collected by the at least one sensor.

[0022] In some embodiments, the at least one processor configured to receive second measurement data including at least one BG measurement can be programmed or configured to cause the processor to receive second measurement data including successive BG measurements (e.g., multiple BG measurements) from the at least one sensor as the BG measurements are being obtained in real time by the at least one sensor.

[0023] In some embodiments, at least one processor can be programmed or configured to cause the processor to determine that at least one time series of BG measurements is a candidate series that includes a compression artifact. A candidate series can include a time series of BG measurements that includes a drop time window (e.g., a series of BG measurements whose values ​​decrease over time). A candidate series can include a time series of BG measurements that includes one or more BG measurements below a BG measurement threshold (e.g., a threshold). In some embodiments, a candidate series can include a time series of BG measurements that includes one or more attributes that can indicate that the time series of BG measurements includes a compression artifact. However, in some cases, a candidate series can include one or more attributes that indicate that the time series of BG measurements does not include a compression artifact, while the time series of BG measurements includes a compression artifact. In other cases, a candidate series includes a compression artifact. Determining that at least one time series of BG measurements is a candidate series can be a first step in finding a compression artifact in the measurement data and detecting sensor compression.

[0024] In some embodiments, at least one processor can be programmed or configured to cause the processor to determine that at least one time series of BG readings includes a change in BG readings across multiple timestamps (e.g., across two consecutive timestamps, or across multiple timestamps starting from a first timestamp). The change in BG readings can exceed a threshold (e.g., a threshold value). For example, the at least one processor can calculate the change in BG readings by determining a difference between a first BG reading and a second BG reading to determine the change in BG readings. As a result, the at least one processor can determine that the difference between the first BG reading and the second BG reading exceeds a threshold (e.g., a fall time threshold and / or a rise time threshold, etc.). In some embodiments, the first BG reading can be associated with a first timestamp and the second BG reading can be associated with a second timestamp, where the first timestamp and the second timestamp are consecutive timestamps in the at least one time series of BG readings. Alternatively, a first BG measurement can be associated with a first timestamp and a second BG measurement can be associated with a second timestamp, the first timestamp and the second timestamp being separated by one or more timestamps in at least one time series of BG measurements.

[0025] In some embodiments, the at least one processor can be programmed or configured to cause the processor to determine a clearance value between BG readings (e.g., a clearance value between a first BG reading and a second BG reading) based on the first measurement data and the second measurement data. For example, the at least one processor can be programmed or configured to cause the processor to select a first BG reading from the first measurement data and a second BG reading from the second measurement data. The at least one processor can be programmed or configured to cause the processor to determine a clearance value based on a difference between the first BG reading and the second BG reading. The at least one processor can be programmed or configured to cause the processor to determine a clearance value between at least one BG reading of the second measurement data and a first BG reading associated with a first timestamp of the first measurement data.

[0026] In some embodiments, at least one processor configured to determine clearance values ​​between BG measurements can be programmed or configured to cause the processor to determine each clearance value of a plurality of clearance values ​​between each successive BG measurement and each BG measurement of the first measurement data in real time as each successive BG measurement is received. A first BG measurement of the successive BG measurements can be associated with a first timestamp of the first measurement data, and a second BG measurement of the successive BG measurements can be associated with a second timestamp, and so on for other successive BG measurements. In this manner, each successive BG measurement can have a clearance value determined for the BG measurement as the BG measurements are collected by the at least one sensor and received in real time by the at least one processor.

[0027] In some embodiments, at least one processor may be programmed or configured to cause the processor to determine the clearance value based on a model such as: TIFF2025538984000002.tif13150 where, G LSC is the glucose concentration in the local sensor compartment of at least one sensor, and G ISF is the glucose concentration in the interstitial fluid, TIFF2025538984000003.tif13150 is the rate of change of glucose concentration in the local sensor compartment, k1 is the clearance value, k0 is the glucose transport rate, The file is TIFF2025538984000004.tif13150.

[0028] In some embodiments, at least one processor configured to determine that at least one time series of BG measurements is a candidate sequence can be programmed or configured to cause the processor to determine that the at least one time series of BG measurements includes a time series subsequence having a fall time window and a rise time window associated with the fall time window. The time series subsequence can include a series of BG measurements associated with timestamps in the form of a time series including a plurality of timestamps that is a subset of the at least one time series of BG measurements. For example, each BG measurement measured and / or collected by the at least one sensor can be associated with exactly one timestamp, and thus a group of multiple BG measurements forms a time series of BG measurements collected by the at least one sensor over time. The time series of BG measurements can include one or more time series subsequences that can span different times and be of any different length within the time series of BG measurements (e.g., the number of timestamps and BG measurements within the time series subsequence).

[0029] In some embodiments, the time series subsequence can include a series of timestamps corresponding to at least a portion of the fall time window (e.g., at least one timestamp of the time series subsequence falls within the fall time window) and at least a portion of the rise time window (e.g., at least one timestamp of the time series subsequence falls within the rise time window). In some embodiments, the time series subsequence can span at least 2.5 minutes in duration of the BG measurement.

[0030] In some embodiments, at least one processor configured to determine that at least one time series of BG measurements is a candidate series can be programmed or configured to cause the processor to determine a drop time window in the at least one time series based on a difference between a first BG measurement and a second BG measurement exceeding a drop time threshold. The drop time window can include a series of multiple timestamps and multiple BG measurements starting from a first timestamp associated with the first BG measurement (e.g., the BG measurement) and ending at a second timestamp associated with the second BG measurement (e.g., the BG measurement). In some embodiments, the first BG measurement and the second BG measurement can be part of the same measurement data (e.g., a time series of BG measurements).

[0031] In some embodiments, at least one processor configured to determine that at least one time series of BG measurements is a candidate series can be programmed or configured to cause the processor to determine a rise time window within the at least one time series based on a difference between a third BG measurement and a fourth BG measurement exceeding a rise time threshold. The rise time window can include a series of timestamps and BG measurements starting from a third timestamp associated with the third BG measurement (e.g., the BG measurement) and ending with a fourth timestamp associated with the fourth BG measurement (e.g., the BG measurement). The rise time window can be related to the fall time window in that only a rise time window can occur within the time series of BG measurements after at least one fall time window has occurred.

[0032] In some embodiments, the fall time threshold can be equal to 10 mg / dL (e.g., BG reading). In some embodiments, the rise time threshold can be equal to 6 mg / dL (e.g., BG reading).

[0033] In some embodiments, the at least one processor can be programmed or configured to cause the processor to generate a signal output indicating that the at least one sensor is under compression based on a clearance value between BG readings. For example, the at least one processor can generate a signal output indicating that the at least one sensor is under compression based on the clearance value being above a predetermined threshold (e.g., a threshold defined by the BG readings).

[0034] In some embodiments, at least one processor can be programmed or configured to cause the processor to generate a signal output indicating that at least one time series of BG measurements was obtained while the at least one sensor was under compression. For example, the signal output can include a signal sent to an insulin delivery system to cause the insulin delivery system to perform an action, a signal sent to a display, and / or a signal sent to another processor to cause another processor to perform an action, etc.

[0035] In some embodiments, the at least one processor configured to generate the signal output can be programmed or configured to cause the processor to predict that the at least one sensor is experiencing a compression while the at least one sensor is obtaining a BG measurement. The at least one processor can be programmed or configured to cause the processor to predict (e.g., generate a prediction) via a predetermined model executed by the processor. The processor can generate the prediction in real time (e.g., in real time relative to the sensors collecting the measurement data) via outputting instructions based on receiving measurement data from the at least one sensor as the sensors are collecting the measurement data.

[0036] In some embodiments, the at least one processor configured to generate the signal output can be programmed or configured to cause the processor to predict, via outputting an indication, in real time that the at least one sensor is experiencing compression while the at least one sensor is taking a BG measurement. The at least one processor configured to generate the signal output can be programmed or configured to cause the processor to indicate, via outputting an indication, in real time that the at least one sensor is experiencing compression while the at least one sensor is taking a BG measurement.

[0037] For example, the at least one processor may generate a signal output based on determining that the clearance value falls outside a normal distribution of clearance values, which may generally represent the magnitude of the clearance value determined by the at least one processor when the at least one sensor collects BG measurements while the at least one sensor is not undergoing compression.

[0038] In some embodiments, the at least one processor may generate a signal output based on determining that the clearance value is within a distribution of clearance values ​​representative of compression low values. The distribution of clearance values ​​representative of compression low values ​​may represent a magnitude of the clearance value determined by the at least one processor when at least one sensor collects BG measurements while the at least one sensor is under compression. In some embodiments, the distribution of clearance values ​​representative of compression low values ​​may be empirically determined (e.g., predetermined) using one or more sensors known to collect BG measurements during and / or without compression.

[0039] In this manner, the at least one processor can generate a clearance value in real time and compare it to a normal distribution of clearance values ​​and a distribution of clearance values ​​representing compression lows. Based on comparing the clearance value to the distribution, the at least one processor can identify, in real time, whether the time series includes BG measurements taken while at least one sensor was under compression. That is, collecting BG measurements so that the BG measurements used to determine the clearance value are close in time (e.g., collected by at least one sensor within 30 seconds to 5 minutes of each other) and comparing them to previous BG measurements from at least one sensor (e.g., based on a delay) can enable the comparison of the clearance value to the distribution in real time. In some embodiments, at least one time series of BG measurements (e.g., first measurement data) can include at least one BG measurement extrapolated (not measured) from the at least one time series of BG measurements. The at least one processor can then generate a signal output indicating whether the sensor is under compression based on comparing the clearance value to the distribution.

[0040] In some embodiments, the at least one processor can be programmed or configured to cause the processor to identify one or more features of the at least one time series of BG readings. For example, the at least one processor can identify and / or extract (e.g., via feature extraction) features of the at least one time series of BG readings so that they can be input into at least one machine learning model. The at least one processor can 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 at least one time series of BG readings.

[0041] In some embodiments, the at least one processor can be coupled to an insulin delivery system in communication with the at least one processor. The at least one processor can be programmed or configured to cause the processor to send a signal output to the insulin delivery system. The signal output can indicate that the at least one sensor is under compression, and the signal output can cause the insulin delivery system to do at least one or more of: start insulin delivery (e.g., after insulin delivery has been stopped due to a compression artifact), continue insulin delivery (e.g., after determining that the at least one sensor is under compression and functioning normally), disable an alarm (e.g., after an alarm has been triggered by sensor compression), and / or any combination thereof.

[0042] 1, compression detection system 102 may include software instructions (e.g., program code) implemented on a computing device 104. Compression detection system 102 may include a memory 108 that stores the software instructions. Compression detection system 102 may include a processor 106 that executes the software instructions, causing processor 106 to perform one or more functions. Compression detection system 102 may include a sensor 110 (e.g., a CGM sensor).

[0043] The compression 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 having a compression artifact provided as a runtime input to the at least one processor 106. 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 the at least one sensor was subjected to compression. The computing device 104 and / or the processor 106 may receive the at least one time series of BG measurements 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) based on a training data set and / or a test data set.

[0044] In some embodiments, compression detection system 102 can be implemented on a single computing device. In some embodiments, compression detection system 102 can be implemented as a distributed system across multiple computing devices (e.g., a collection of servers, such as a collection of computing devices 104), such that software instructions are implemented on different computing devices. In some embodiments, compression detection system 102 can be associated with computing device 104, such that compression detection system 102 executes on computing device 104, or a portion of compression detection system 102 executes on computing device 104 as part of a distributed computing system where sensor 110 is not part of computing device 104. Alternatively, compression detection system 102 can include at least one computing device 104 that executes software instructions and at least one sensor 110 that detects compression artifacts and / or PISA.

[0045] 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 and / or attached to various parts of the subject's body (e.g., the arm, abdomen, etc.). The sensor 110 may be configured to collect and transmit measurements (e.g., BG measurements) to a processor (e.g., processor 106). The sensor 110 may be subjected to compression (e.g., by the subject or via other means) while collecting and / or obtaining measurements, which may affect the accuracy of the measurements obtained by the sensor 110.

[0046] The sensor 110 may include a sampling rate that may be configured to collect and / or obtain measurements over a period of time having a particular sampling resolution. For example, the sensor 110 may be configured to sample measurements at 30-second intervals, 1-minute intervals, 2.5-minute intervals, and / or 5-minute intervals, etc. The sensor 110 may transmit the sampled measurements to the processor 106 in real time as the measurements are sampled. In some embodiments, the sensor 110 may transmit at least one time series of the sampled measurements to the processor 106 and / or memory 108 at specified time intervals. The time series may include multiple BG measurements, and each BG measurement may be associated with a timestamp. The timestamp may represent an absolute or relative time at which the BG measurement was collected by the sensor 110. The sensor 110 may communicate with the computing device 104 and / or the processor 106 via wired means (e.g., a data bus and / or Ethernet, etc.) or wireless means (e.g., Wi-Fi and / or Bluetooth, etc.) and / or a communication interface.

[0047] The computing device 104 may include a processor 106 (e.g., a CPU) and a memory 108. The processor 106 may execute software instructions (e.g., compiled program code) for the compression detection system 102. In some embodiments, the sensor 110 may be separate from the computing device 104. Alternatively, the sensor 110 may be integrated with (e.g., part of) the computing device 104.

[0048] Computing device 104 may include one or more processors (e.g., processor 106) configured to execute software instructions. For example, computing device 104 may include a desktop computer, a portable computer (e.g., laptop computer, tablet computer), a workstation, a mobile device (e.g., smartphone, mobile phone, personal digital assistant, wearable device), a server, and / or other similar devices. Computing device 104 may include a computing device configured to communicate with one or more other computing devices over a network. Computing device 104 may include a group of computing devices (e.g., a group of servers) and / or other similar devices. In some embodiments, computing device 104 may include a data storage device. Alternatively, the data storage device may be separate from and in communication with computing device 104.

[0049] The processor 106 can be implemented in hardware, software, or a combination of hardware and software. For example, the processor 106 can include a general 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 (e.g., a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc.) that can be programmed with software instructions that, when executed, configure the processor to perform a function. In some embodiments, the processor 106 can include multiple processors (e.g., a CPU and a GPU) implemented on a single computing device 104 or multiple processors implemented across multiple distributed computing devices 104. The processor 106 can be coupled to the memory 108 via a data bus for transferring data between the processor 106 and the memory 108. In some embodiments, the processor 106 can be coupled to the sensor 110 via wired means (e.g., a data bus and / or Ethernet, etc.) or wireless means (e.g., Wi-Fi and / or Bluetooth, etc.) and / or a communication interface.

[0050] 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 used by processor 106. 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.

[0051] The software instructions may be loaded into memory 108 from another computer-readable medium or another device via a communications interface with computing device 104. The software instructions stored in memory 108 and executed by processor 106, when executed, may cause 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.

[0052] The number and arrangement of systems, hardware, and / or modules (e.g., software instructions) shown in FIG. 1 are provided as an example. Additional, fewer, different, or differently arranged systems, hardware, and / or modules may be present. Furthermore, two or more of the systems, hardware, and / or modules shown in FIG. 1 may be implemented within a single system, hardware, and / or module. A single system, hardware, and / or module shown in FIG. 1 may be implemented as multiple distributed systems, hardware, and / or modules. Additionally or alternatively, a set of systems, hardware, and / or modules (e.g., one or more systems, one or more hardware devices, one or more modules) in FIG. 1 may perform one or more functions described as being performed by another set of systems, another set of hardware, or another set of modules in FIG. 1.

[0053] 2 , an embodiment relates to an exemplary method 200 for detecting sensor compression in continuous glucose monitoring as disclosed herein. Method 200 may 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 may be performed (e.g., fully, partially, etc.) by compression detection system 102 (e.g., via processor 106). In some embodiments, one or more of the steps of method 200 may be performed (e.g., fully, partially, etc.) by another system, hardware, or module separate from or including compression detection system 102, or a group of systems, hardware, or modules, such as a client device and / or another computing device.

[0054] As shown in FIG. 2 , method 200 may include, at step 202, receiving first measurement data as at least one time series of BG measurements. For example, compression detection system 102 (e.g., via computing device 104 and / or processor 106) may receive at least one time series of BG measurements from sensor 110. In some embodiments, compression detection system 102 may receive the first measurement data and provide the measurement data to at least one processor 106 as input for generating a signal output. The first measurement data may include historical data (e.g., data collected at a past time). Alternatively, the first measurement data may include real-time runtime input from sensor 110. In some embodiments, the first measurement data may be received from sensor 110 or another sensor. In some embodiments, if the first measurement data includes historical data, the first measurement data may be retrieved from one or more storage devices, such as memory 108. The first measurement data may be collected by at least one sensor (e.g., sensor 110) while the at least one sensor is not experiencing compression.

[0055] In some embodiments, the first measurement data may include multiple time series of BG measurements. The time series of BG measurements may include multiple timestamps. Each timestamp may be associated with a BG measurement. For example, one BG measurement collected by the sensor 110 may be associated with exactly one timestamp. The timestamp may represent an absolute or relative time at which the BG measurement was collected by the sensor 110. The processor 106 may receive the first measurement data, including the time series of BG measurements, as input to the processor 106. The time series of BG measurements may span various times and may be of any various lengths (e.g., timestamps and number of BG measurements). For example, at least one time series of BG measurements may include at least 30 minutes of measurement data measured by at least one sensor. In this example, the number of timestamps and BG measurements may depend on the resolution of the BG measurements, or the sample rate at which the sensor 110 collects the BG measurements. In some embodiments, the time series subsequence may span at least 2.5 minutes within the duration of the BG measurements. In some embodiments, the time series subsequences can be longer or shorter than 2.5 minutes in duration.

[0056] Method 200 may include receiving second measurement data at step 204, the second measurement data including at least one BG measurement. For example, compression detection system 102 may receive from sensor 110 (e.g., via computing device 104 and / or processor 106) at least one time series of BG measurements collected while sensor 110 was under compression. In some embodiments, compression detection system 102 may receive the second measurement data and provide the measurement data to at least one processor 106 as input for generating the signal output. The second measurement data may include historical data (e.g., data collected at a past time). Alternatively, the second measurement data may include real-time, run-time 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, if the first measurement data includes historical data, the first measurement data may be retrieved from one or more storage devices, such as memory 108.

[0057] In some embodiments, step 202 and step 204 of method 200 occur separately, with step 202 occurring before step 204, or step 204 occurring before step 202. In some embodiments, step 202 can occur simultaneously with step 204.

[0058] Method 200 may include determining at least one clearance value at step 206. For example, compression detection system 102 may determine (e.g., via computing device 104 and / or processor 106) a clearance value between BG readings based on the first measurement data and the second measurement data. The clearance value may include a model-based value for at least one BG reading of the second measurement data and a first BG reading associated with a first timestamp of the first measurement data.

[0059] Method 200 may include detecting sensor compression at step 208. For example, compression detection system 102 may detect that at least one sensor is compressed based on a clearance value. In some embodiments, compression detection system 102 may detect that at least one sensor is compressed based on a distribution of a plurality of clearance values.

[0060] In some embodiments, the compression detection system 102 can determine that the measurement data includes a compression artifact based on determining that at least one BG measurement is below a compression estimation threshold. For example, one or more BG measurements (e.g., within a time series of BG measurements) can create a compression artifact in the measurement data. The one or more BG measurements can indicate the onset of sensor compression (e.g., the sensor 110 experiencing compression while collecting one or more BG measurements).

[0061] In some embodiments, the compression detection system 102 can determine that second measurement data received from the sensor 110 includes the onset of a sensor compression based on determining that the measurement data (e.g., a time series subsequence of the measurement data) includes at least one compression artifact. The compression artifact can include a time series of BG measurements in which the clearance value for each BG measurement in the time series is above or below a predetermined threshold, such that the clearance value is outside of a normal range (e.g., outside of a band of equilibrium clearance values ​​that can indicate a normal BG level for the subject).

[0062] In some embodiments, the compression detection system 102 (e.g., via the computing device 104 and / or the processor 106) can identify one or more features of at least one time series of BG measurements. The compression detection system 102 can input the one or more features into at least one machine learning model for classification and / or generation of a signal output.

[0063] The compression detection system 102 can generate a signal output indicating that at least one BG measurement (e.g., of the second measurement data) was obtained while at least one sensor (e.g., sensor 110) was experiencing a compression. In some embodiments, the compression detection system 102 can determine a clearance value and generate the signal output based on determining whether the clearance value is within a distribution of clearance values ​​representing a low compression value.

[0064] In some embodiments, the compression detection system 102 configured to generate a signal output can predict in real time via output of an indication (e.g., via the computing device 104 and / or the processor 106) that at least one sensor is experiencing compression while the at least one sensor is acquiring a BG measurement. For example, the compression detection system 102 can generate the signal output based on a clearance value determined by the compression detection system 102. The clearance value can be determined via a model (e.g., a physiological model) that models glucose diffusion between the interstitial fluid from which the sensor (e.g., sensor 110) is acquiring a BG measurement and the local sensor compartment. The signal output (e.g., a clearance value) indicative of at least one sensor experiencing compression can be determined using the model and measurement data including BG measurements acquired while the at least one sensor is experiencing compression. In this manner, the compression detection system 102 can generate a clearance value using the physiological model and use the clearance value to determine that a BG measurement was acquired while the at least one sensor was experiencing compression. A clearance value having a value within the distribution of clearance values ​​representing a compression low value can indicate that a BG measurement (e.g., the BG measurement associated with the clearance value) was collected by at least one sensor while the at least one sensor was under compression, whereas a clearance value having a value outside the distribution of clearance values ​​representing a compression low value (and within the normal distribution of clearance values) can indicate that a BG measurement (e.g., the BG measurement associated with the clearance value) was collected by at least one sensor while the at least one sensor was not under compression.

[0065] In some embodiments, once the compression detection system 102 has determined the clearance value, it can generate a model output of the BG measurement that includes a BG measurement (e.g., second measurement data) that is a function of the glucose concentration in the local sensor compartment. The compression detection system 102 can receive a model input of the BG measurement that represents an estimate of the glucose concentration in the interstitial fluid associated with the local sensor compartment. In some embodiments, the compression detection system 102 can determine the clearance value based on the model input, the model output, and a physiological model. For example, the compression detection system 102 can determine the clearance value based on the following model: TIFF2025538984000005.tif13150 where, G LSC is the glucose concentration in the local sensor compartment, and G ISF is the glucose concentration in the interstitial fluid associated with the local sensor compartment, k1 is the clearance value, k0 is the glucose transport rate into the local sensor compartment, It is set to TIFF2025538984000006.tif13150.

[0066] In some embodiments, G ISF and G LSC If a difference between G and G is detected, the clearance value may be increased (e.g., may have a higher value). In some embodiments, if the compression sensing system 102 uses a physiological model, the clearance value may be increased (e.g., may have a higher value). ISF Thus, the clearance value can be calculated as ISF and G LSC and can be independent of fluctuations in glucose concentration, thereby ensuring that detection of compression lows is independent of normal physiological fluctuations and solely depends on the compression of the sensor.

[0067] In some embodiments, real time can include a moment in time relative to the occurrence of an event (e.g., real time relative to the collection of measurement data) where a response (e.g., generation of a signal output) occurs within a specified time, which is generally a relatively short time (e.g., within a few seconds) from the occurrence of the event. For example, real time can refer to a moment in time where a signal output is generated by compression detection system 102 simultaneously or shortly thereafter (e.g., within a few milliseconds or seconds) with the collection of measurement data by sensor 110 (or another sensor). As a further example, a real-time (e.g., run-time) signal output can be generated relative to the collection and / or receipt of a BG measurement or a time series of at least one BG measurement simultaneously or shortly thereafter when compression detection system 102 receives a BG measurement or a time series of at least one BG measurement and / or when compression detection system 102 determines a clearance value.

[0068] In some embodiments, the compression detection system 102 (e.g., computing device 104 and / or processor 106) can be combined with an insulin delivery system. The compression detection system 102 (e.g., via the processor 106) can be in communication with the insulin delivery system (e.g., via wired and / or wireless means). The compression detection system 102 can send a signal output to the insulin delivery system indicating that the sensor 110 is experiencing compression while collecting BG measurements. The signal output received by the insulin delivery system can cause the insulin delivery system to at least one or more of: initiate insulin delivery, continue insulin delivery, disable an alarm, and / or any combination thereof.

[0069] The steps of method 200 may be performed in various orders and sequences and are not limited to being performed in the order shown in FIG. 2 . For example, the second measurement data may be received before the compression detection system 102 receives the first measurement data from the at least one sensor. Similarly, in some cases, the compression detection system 102 may receive the first measurement data from the at least one sensor (e.g., sensor 110) after detecting a sensor compression. Thus, the steps of method 200 are not limited to a particular order and may be performed on various components, whether implemented on a single computing device or multiple distributed computing devices. The steps of method 200 may be performed by a single sensor (e.g., sensor 100) or multiple sensors.

[0070] 3 , 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, but not insulin device 304. For example, system 300 may include glucose monitoring device 302 (e.g., along with at least one sensor included in glucose monitoring device 302) in communication with at least one processor 306 to perform embodiments disclosed herein.

[0071] In some embodiments, the glucose monitoring device 302 and / or the insulin device 304 can be the same as or similar to the sensor 110. In some embodiments, the glucose monitoring device 302 and / or the insulin device 304 can include the sensor 110. For example, the glucose monitoring device 302 can include the sensor 110 as a component of the 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 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 in a computing device separate from the glucose monitoring device 302 and / or the insulin device 304.

[0072] In some embodiments, the glucose monitoring device 302 and / or the insulin device 304 can include the processor 306. For example, the glucose monitoring device 302 can include the processor 306 as a component of the glucose monitoring device 302, or the insulin device 304 can include the processor 306 as a component of the insulin device 304. In some embodiments, the glucose monitoring device 302, the insulin device 304, the processor 306, and / or the sensor 110 can be implemented in a single system. Alternatively, the processor 306 can be implemented in a computing device separate from the glucose monitoring device 302, the insulin device 304, and / or the sensor 110. The processor 306 can be implemented locally in the glucose monitoring device 302, the insulin device 304, or a standalone device (e.g., computing device 104) (or in any combination of two or more of the glucose monitoring device 302, the insulin device 302, or the standalone device). In some embodiments, the processor 306 can be the same as or similar to the processor 106.

[0073] The glucose monitoring device 302 may include a device that can be used (e.g., as a stand-alone device) to monitor and / or test the blood glucose level of the subject 308. The glucose monitoring device 302 may be attached to and / or attached to the subject 308 to monitor the blood glucose level. The 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 glucose monitoring device 302 may collect measurement data (e.g., BG measurement data) that it transmits to the processor 306 to detect whether the glucose monitoring device 302 and / or sensor 110 are being compressed by the subject 308. The processor 306 may execute software instructions (e.g., compression detection system 102) as a component of the glucose monitoring device 302 or separate from the glucose monitoring device 302. For example, the processor 306 may be implemented locally in the glucose monitoring device 302. In some embodiments, the glucose monitoring device 302 and the insulin device 304 may be implemented as separate devices, or the glucose monitoring device 302 and the insulin device 304 may be implemented as a single device.

[0074] In some embodiments, the glucose monitoring device 302 may generate output, error, accuracy improvement parameters, and / or accuracy-related information that may be sent to the processor 306 or the like to perform various analyses, such as error analysis and / or further refinement of embodiments herein.

[0075] The insulin device 304 may include an insulin delivery system, such as an insulin pump. The insulin device 304 may be in communication with the subject 308 to deliver insulin to the subject 308. In some embodiments, the processor 306 may execute software instructions (e.g., the compression detection system 102) as a component of the insulin device 304 or separate from the insulin device 304. For example, the processor 306 may be implemented locally on the insulin device 304. In some embodiments, the insulin device 304 may be attached and / or mounted to the subject 308 so that the insulin device 304 can deliver insulin to the subject 308. The processor 306 and / or portions of the system 300 may be located remotely such that the glucose monitoring device 302 and / or the insulin device 304 can operate as telemedicine devices.

[0076] The processor 306 can be implemented in hardware, software, or a combination of hardware and software. For example, the processor 306 can include a general 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 that, when executed, configure the processor to perform a function (e.g., a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc.). In some embodiments, the processor 306 can include multiple processors (e.g., a CPU and a GPU) implemented in a single computing device or multiple processors implemented across multiple distributed computing devices. The processor 306 can be coupled to 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), glucose monitoring device 302, and / or insulin device 304 via wired means (e.g., data bus and / or Ethernet, etc.) or wireless means (e.g., Wi-Fi and / or Bluetooth, etc.) and / or communication interfaces.

[0077] The subject 308 may include a patient present at home or another desired location. In some embodiments, the subject may include a human or any animal. It is understood that the animal may be of various applicable types, including, but not limited to, a mammal, a veterinarian animal, a livestock animal, or a pet animal. By way of example, the animal may be a laboratory animal (e.g., a rat, a dog, a pig, a monkey) specifically selected to have certain characteristics similar to humans. It is understood that the subject may be, for example, any applicable human patient.

[0078] The number and arrangement of systems, hardware, and / or devices shown in FIG. 3 are provided as an example. Additional, fewer, different, or differently arranged systems, hardware, and / or modules may be present. Furthermore, two or more of the systems, hardware, and / or modules shown in FIG. 3 may be implemented within a single system, hardware, and / or module. A single system, hardware, and / or module shown in FIG. 3 may also be implemented as multiple, distributed systems, hardware, and / or modules. Additionally or alternatively, a set of systems, hardware, and / or devices (e.g., one or more systems, one or more hardware components, one or more devices) in FIG. 3 may perform one or more functions described as being performed by another set of systems, another set of hardware, or another set of devices in FIG. 3.

[0079] An exemplary system 400 for implementing an embodiment for detecting sensor compression in a CGM is shown in FIG. 4. For example, FIG. 4 illustrates exemplary components that may be used to implement an embodiment for detecting sensor compression in a CGM when a sensor is subjected to compression while collecting a BG measurement. System 400 may include a glucose monitoring device 402, a subject 408, a sensor 410, an interstitial fluid compartment 412, and a local sensor compartment 414.

[0080] Glucose monitoring device 402 can be the same as or similar to glucose monitoring device 302. In some embodiments, glucose monitoring device 402 and / or sensor 410 can be the same as or similar to sensor 110. In some embodiments, the glucose monitoring device can include sensor 410. For example, glucose monitoring device 402 can include sensor 410 as a component of glucose monitoring device 402. In some embodiments, glucose monitoring device 402 and / or sensor 410 can be implemented in a single system such that glucose monitoring device 402 is in communication with sensor 410. In some embodiments, at least one processor (e.g., processor 106 or processor 306) can be implemented as part of glucose monitoring device 402. Alternatively, at least one processor can be implemented on a computing device separate from glucose monitoring device 402.

[0081] In some embodiments, the 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 glucose monitoring device 402 and / or the sensor 410. The at least one processor may be implemented locally in the glucose monitoring device 402 or in a stand-alone device (e.g., computing device 104).

[0082] The glucose monitoring device 402 may include a device that can be used to monitor and / or test the blood glucose level of a subject (e.g., subject 408) (e.g., as a stand-alone device). The glucose monitoring device 402 may be attached to and / or attached to the subject 408 to monitor the blood glucose level. The glucose monitoring device 402 may communicate with the subject 408 (e.g., via a sensor, such as sensor 410) to monitor the blood glucose level of the subject 408. In this manner, the glucose monitoring device 402 may collect measurement data (e.g., BG measurement data) that it transmits to at least one processor to detect whether the glucose monitoring device 402 and / or sensor 410 are being compressed by the subject 408. The at least one processor (e.g., of the glucose monitoring device 402 or another device) may execute software instructions (e.g., compression detection system 102) as a component of the glucose monitoring device 402 or a component separate from the glucose monitoring device 402. For example, the at least one processor may be implemented locally in the glucose monitoring device 402.

[0083] In some embodiments, the glucose monitoring device 402 may generate output, error, accuracy improvement parameters, and / or accuracy-related information that may be sent to the processor 306 or the like to perform various analyses, such as error analysis and / or further refinement of embodiments herein.

[0084] The subject 408 may include a patient present at home or another desired location. In some embodiments, the subject may include a human or any animal. It is understood that the animal may be of various applicable types, including, but not limited to, a mammal, a veterinarian animal, a livestock animal, or a pet animal. By way of example, the animal may be a laboratory animal (e.g., a rat, a dog, a pig, a monkey) specifically selected to have certain characteristics similar to humans. It is understood that the subject may be, for example, any applicable human patient.

[0085] The local sensor compartment (LSC) 414 can include the tissue of the subject 408 immediately adjacent to and / or surrounding the sensor 410. The LSC 414 can include the area and / or volume of the tissue of the subject 408, including the influx and efflux of glucose into and from the LSC 414. The LSC 414 can include the concentration of glucose at any point in time. The interstitial fluid compartment (ISF) 412 can include the tissue of the subject 408 that does not immediately surround the sensor 410. For example, the ISF 412 can include the tissue of the subject 408 excluding the LSC 414. The ISF 412 can include the area and / or volume of tissue of the subject 408, including the influx and efflux of glucose into and from the ISF 412 (e.g., to the LSC 414). In this manner, the compression detection system 102 can use the balance of glucose concentrations between the LSC 414 and the ISF 412 as an indicator that the sensor 410 is in a healthy state and not under compression. When the ISF 412 and the LSC 414 have balanced glucose concentrations, the glucose flux from the LSC 414 to the ISF 412 and vice versa is constant (e.g., balanced), and the compression detection system 102 can determine that the sensor 410 is not under compression (e.g., no compression is present) based on the equilibrium of the glucose concentrations between the ISF 412 and the LSC 414.

[0086] The number and arrangement of systems, hardware, and / or devices shown in FIG. 4 are provided as an example. Additional, fewer, different, or differently arranged systems, hardware, and / or modules may be present. Furthermore, two or more of the systems, hardware, and / or modules shown in FIG. 4 may be implemented within a single system, hardware, and / or module. A single system, hardware, and / or module shown in FIG. 4 may be implemented as multiple distributed systems, hardware, and / or modules. Additionally or alternatively, a set of systems, hardware, and / or devices (e.g., one or more systems, one or more hardware components, one or more devices) in FIG. 4 may perform one or more functions described as being performed by another set of systems, another set of hardware, or another set of devices in FIG. 4.

[0087] 5A shows an example plot (top plot) of unaltered CGM sensor measurements without compression artifact and simulated CGM measurements representative of compression artifact. FIG. 5A also shows an example plot (bottom plot) of clearance values ​​determined by a system (e.g., compression detection system 102) based on the unaltered and simulated CGM measurements shown in the time plot of FIG. 5A. For example, the top plot shows the ISF (e.g., ISF 412) (G ISF ) shows the unchanged glucose concentration in the LSCs (e.g., LSC414) labeled as "Model Output" in Figure 5A (G LSC ) are also shown. As shown in the lower plot of Figure 5A, G ISF and G LSCThe clearance value increases when there is a difference between the BG readings and the clearance value. Note that in the time series of BG readings shown in FIG. 5A with multiple BG readings and clearance values ​​over a 50 minute span, there may be at least one clearance value associated with each BG reading. Embodiments may be configured such that the top plot represents a variable glucose concentration (G ISF ) can be displayed. In this manner, the compression detection system 102 can determine the clearance value independent of the concentration of glucose in the ISF (e.g., outside the local sensor compartment). The compression detection system 102 can determine the clearance value based on the difference between the glucose concentration in the ISF and the glucose concentration in the LSC. Thus, the detection of sensor compression is not dependent on normal physiological fluctuations that may occur in the glucose concentration in the ISF.

[0088] FIG. 6 shows an exemplary plot of CGM sensor measurements from multiple sensors, along with a plot of each sensor's clearance value over a time series of BG measurements (e.g., based on ISF glucose concentration and LSC glucose concentration). For example, FIG. 6 illustrates an embodiment provided in an environment where multiple sensors are used to determine whether any of the multiple sensors are experiencing compression when collecting BG measurements. FIG. 6 shows multiple time series of BG measurements, each collected by one sensor of the multiple sensors (top plot). A first time series of BG measurements collected by a first sensor is shown, with one or more compression artifacts present in the first time series of BG measurements. 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 are also shown. A fourth sensor (or sensors) that was not experiencing compression is shown. ISFAlso shown is a fourth time series of BG measurements, which represents measurements. In Figure 6, the fourth time series of BG measurements can include at least one BG measurement that can be used as a model input to the compression detection system 102 to determine a clearance value. In this manner, the fourth time series of BG measurements can be considered a "baseline" of BG measurements collected by the sensor while the sensor is not experiencing compression.

[0089] As shown in FIG. 6, the first time series of BG measurements includes two compression lows based on the clearance values ​​shown. The compression detection system 102 can determine a clearance value for the first sensor based on the fourth time series of BG measurements (e.g., as a model input) and the first time series of BG measurements (e.g., as a model output). Plotting the clearance values ​​along with the time series of BG measurements (as shown in FIG. 6) shows where the sensor's compression lows occur. The second time series of BG measurements does not include compression lows because the clearance value for the second sensor is shown to be within a normal distribution (e.g., a normal range) of clearance values. The second sensor does not have a clearance value "spike" as determined by the compression detection system 102 based on the fourth time series of BG measurements and the second time series of BG measurements (and as plotted in FIG. 6). The third time series of BG measurements also does not include a compression low, as the clearance value for the third sensor is shown to be within a normal distribution of clearance values ​​(e.g., there is no clearance value "spike" in FIG. 6). FIG. 6 also shows that while the second and third time series of BG measurements include fluctuating BG measurements that are not completely constant, the clearance values ​​remain relatively constant. In this manner, embodiments do not rely on variable change in normal BG measurements (e.g., normal physiological fluctuations). In the event of a compression low, the clearance value "spikes" to enable accurate detection of compression at the CGM sensor.

[0090] FIG. 7 illustrates exemplary distributions of clearance values ​​for a sensor in a normal state and a sensor under compression. For example, FIG. 7 illustrates an exemplary normal distribution of clearance values ​​(shown in dark gray, e.g., non-PISA) and an exemplary distribution of clearance values ​​representative of a compression low (shown in light gray, e.g., PISA). As shown in FIG. 7, normal clearance values ​​(e.g., clearance values ​​determined by the compression detection system 102 for BG measurements collected by a sensor not under compression) may average approximately 0.9 to 1.0, and clearance values ​​representative of a compression low may average approximately 1.1 to 1.2. Such a distribution of clearance values ​​may indicate a sufficient clearance value difference between a normal state and a compression low state, such that sensor compression can be detected by determining whether a real-time estimate of the clearance value falls within the distribution of normal clearance values ​​or whether the real-time estimate of the clearance value falls within the distribution of clearance values ​​representative of a compression low.

[0091] In some embodiments, the compression detection system 102 can use the distribution of normal clearance values ​​and the distribution of clearance values ​​representative of compression lows to determine a predetermined threshold (e.g., a clearance value threshold) that can be used to indicate the onset of a compression low. For example, the compression detection system 102 can determine a predetermined threshold for the clearance value, where a clearance value higher than the threshold indicates that the sensor is under compression and a clearance value lower than the threshold indicates that the sensor is not under compression. The compression detection system 102 can also use the predetermined threshold to indicate the presence and / or absence of sensor compression when the clearance value is above or below the predetermined threshold. In some embodiments, the predetermined threshold can be equal to 1.05 and 1.3, or between 1.05 and 1.3. In some embodiments, the predetermined threshold can be equal to 0.9 and 1.1, or between 0.9 and 1.1.

[0092] 8 illustrates an exemplary plot of a time series of single-sensor BG measurements, including a detected compression artifact, based on a clearance value over the time series of BG measurements determined using embodiments disclosed herein. FIG. 8 illustrates the determination of a clearance value using 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 a single sensor. The delayed measurement data collected by a 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 clearance value for the single-sensor BG measurements. For example, the compression detection system 102 can use a model defined by: TIFF2025538984000007.tif13150 where, G LSC is the glucose concentration in the LSC, the output of the model, and G ISF is the glucose concentration in the ISF compartment and is the input to the model, k1 is the clearance value of the BG measurement, and k0 is TIFF2025538984000008.tif13150. In some embodiments, the compression detection system 102 can use this model to determine multiple clearance values ​​at each timestamp in the time series of BG measurements and at each BG measurement. Thus, the compression detection system 102 can use this model to generate a time series of clearance values ​​such as that shown in FIG. 8 (lower plot). Using delayed measurement data collected by a single sensor is one way the compression detection system 102 can determine the clearance value of a sensor. In some embodiments, the compression detection system 102 can determine the clearance value using delayed, extrapolated, predicted (e.g., via a machine learning model), or smoothed measurement data collected from a sensor. For example, the first measurement data can include at least one BG measurement extrapolated from at least one time series of BG measurements. In this manner, compression detection system 102 can rely on measurement data collected by one sensor, some of which was collected by the one sensor while the sensor was not experiencing compression, thereby allowing compression detection system 102 to establish a baseline of measurement data that represents measurement data that is not affected by compression lows.

[0093] 9A and 9B show exemplary plots of the area under the receiver operating characteristic (ROC) curve and precision-recall curve, respectively, of a classifier model (e.g., a physiological model) used to classify a time series of BG measurements as containing a compression artifact based on clearance values ​​to detect sensor compression. In FIG. 9A, the area under the ROC curve is 96%, indicating good performance of an embodiment in accurately classifying BG measurement data as indicative of sensor compression using clearance values. In FIG. 9B, the precision-recall curve, which summarizes the trade-off between the model's true positive rate and positive predictive value, shows a mean location score of 0.38. Thus, FIGS. 9A and 9B show that an embodiment of the model provides accurate detection of sensor compression.

[0094] 10A illustrates an exemplary system configuration 1000A of an exemplary computing device (e.g., computing device 104). System configuration 1000A may include a processing unit 1006, a memory 1008, removable storage 1012, non-removable storage 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 of 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 memory (e.g., random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), flash memory), and / or any combination thereof.

[0095] System configuration 1000A may also include other features and / or functionality. For example, system configuration 1000A may also include additional removable storage 1012 and / or non-removable storage 1014, including, but not limited to, magnetic or optical disks or tape, and writable electrical storage media. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of 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 disc read-only memory (CDROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium usable to store desired information and accessible by a computing device (e.g., computing device 104). Any such computer storage media may be part of or in combination with a computing device (e.g., computing device 104).

[0096] The exemplary computing device system configuration 1000A may include one or more communication interfaces 1016 that enable a computing device (e.g., computing device 104) to communicate with other devices (e.g., other computing devices). The communication interface 1016 may transmit and / or carry information and / or signals over a communication medium. A communication medium may embodi computer-readable instructions, data structures, program modules, and / or other data in a modulated data signal such as a carrier wave or other transport mechanism and may include any information delivery medium. The term modulated data signal may mean a signal having one or more characteristics set or changed in such a manner as to encode, execute, and / or process information therein. By way of example, and not limitation, communication media may include wired media, such as a wired network or direct-wired connection, and wireless media, such as wireless, RF, infrared, and / or other wireless media. As discussed, the term computer-readable media, as used herein, may include both storage media and / or communication media.

[0097] The number and arrangement of systems, hardware, devices, and / or modules (e.g., software instructions) shown in FIG. 10A are provided as examples. Additional, fewer, different, or differently arranged systems, hardware, devices, and / or modules may be present. Furthermore, two or more of the systems, hardware, devices, and / or modules shown in FIG. 10A may be implemented within a single system, hardware, device, and / or module. A single system, hardware, device, and / or module shown in FIG. 10A may also be implemented as multiple, distributed systems, hardware, devices, and / or modules. Additionally or alternatively, a set of systems, hardware, devices, and / or modules (e.g., one or more systems, one or more hardware devices, one or more devices, one or more modules) in FIG. 10A may perform one or more functions described as being performed by another set of systems, another set of hardware, another set of devices, or another set of modules in FIG. 10A.

[0098] 10B illustrates an exemplary system environment 1000B in which the systems, methods, devices, and / or computer-readable media may be implemented. The 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, the server 1004 may be the same as or similar to the computing device 104. The client device 1018 may be the same as or similar to the computing device 104.

[0099] FIG. 10B may include a network system including multiple computing devices (e.g., computing device 1004, server 1004, and / or client device 1018) in communication with a network means (e.g., communication network 1022), such as a network including an infrastructure or ad-hoc network. The network connections may be wired and / or wireless connections to the multiple computing devices. As an example, FIG. 10B illustrates a system environment including a network system in which embodiments may be implemented. In this example, the network system may include a server 1004 (e.g., a network server), a communication network 1022 (e.g., wired and / or wireless connections), a client device 1018, and a mobile device (e.g., a smartphone) 1020 (or other handheld or portable device, such as a mobile phone, laptop computer, tablet computer, GPS receiver, MP3 player, handheld video player, pocket projector, or a handheld device (or non-portable device) having a combination of such features). It should be understood that in some embodiments, the server 1004, the client device 1018, and / or the mobile device 1020 can include a glucose monitoring device (e.g., glucose monitoring device 302). It should be understood that in some embodiments, the server 1004, the client device 1018, and / or the mobile device 1020 can include a glucose monitoring device (e.g., glucose monitoring device 302), an artificial pancreas, and / or an insulin device (e.g., insulin device 304), or other intervention or diagnostic device. There can be more than one of any of the components shown or described in FIG. 10B.

[0100] Some embodiments may be implemented on any one of the devices in Figure 10B. For example, instructions (e.g., software instructions) or other desired processing execution may occur on the same computing device, which may be any one of the server 1004, the client device 1018, and / or the mobile device 1020. Alternatively, some embodiments may be implemented and / or executed on different computing devices in the network system shown in Figure 10B. For example, some desired or necessary processing or execution may occur on one of the computing devices in the network (e.g., the server 1004, the client device 1018, the mobile device 1020, and / or the glucose monitoring device), while other processing and execution may occur on another computing device in the network system (e.g., the server 1004, the client device 1018, and / or the mobile device 1020), or vice versa.

[0101] In some embodiments, some processing and / or execution may occur on one computing device (e.g., server 1004, client device 1018, mobile device 1020, and / or insulin device 304, artificial pancreas, or glucose monitoring device 302 (or other intervention or diagnostic device)), while other processing and / or execution (e.g., software instructions and / or compression detection system 102) may occur on a different computing device, which may or may not be part of a networked system. For example, some processing may occur on client device 1018, while other processing and / or instructions may be passed to server 1004 and / or mobile device 1020, where some of the software instructions (e.g., compression detection system 102) are executed. This scenario may be appropriate, for example, when mobile device 1020 has access to communication network 1022 through client device 1018 (or an access point in an ad-hoc network). As another example, one or more embodiments may be used 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 glucose monitoring devices 302). Distribution of the software instructions (e.g., software modules and / or software packages) can be in the form of a storage medium (e.g., disk) or an electronic copy.

[0102] The number and arrangement of systems, hardware, devices, and / or modules (e.g., software instructions) shown in FIG. 10B are provided as examples. Additional, fewer, different, or differently arranged systems, hardware, devices, and / or modules may be present. Furthermore, two or more of the systems, hardware, devices, and / or modules shown in FIG. 10B may be implemented within a single system, hardware, device, and / or module. A single system, hardware, device, and / or module shown in FIG. 10B may also be implemented as multiple, distributed systems, hardware, devices, and / or modules. Additionally or alternatively, a set of systems, hardware, devices, and / or modules (e.g., one or more systems, one or more hardware devices, one or more devices, one or more modules) in FIG. 10B may perform one or more functions described as being performed by another set of systems, another set of hardware, another set of devices, or another set of modules in FIG. 10A.

[0103] FIG. 11 is a block diagram illustrating a system 1100 including a computer system 140 and associated Internet 11 connection in which embodiments can be implemented. Typically, such a configuration is used for a computer (host) connected to the Internet 11 and running server or client (or a combination thereof) software. The computer system configuration and Internet connection illustrated in FIG. 11 can be used by a source computer, such as a laptop, a final destination computer, and an intermediary server, as well as any computer or processor described herein. In some embodiments, the computer system 140 can be the same as or similar to the computer device 104. The system 1100 can be used as a portable electronic device, such as a notebook / laptop computer, a media player (e.g., MP3-based or video player), a mobile phone, a personal digital assistant (PDA), a glucose monitor, an artificial pancreas, an insulin delivery device (or other interventional or diagnostic device), an imaging device (e.g., a digital camera or video recorder), and / or any other handheld computing device, or any combination of these devices. Note that while FIG. 11 illustrates various components of a computer system, it is not intended to represent a particular architecture or manner in which the components are interconnected.

[0104] It will be understood that network computers, handheld computers, mobile phones, and other data processing systems having fewer or more components can also be used. The computer system of FIG. 11 can be, for example, an Apple Macintosh computer or Power Book, or an IBM-compatible PC. Computer system 140 includes a bus 137, interconnect, or other communication mechanism for communicating information, and a processor 138, typically in the form of an integrated circuit, coupled to bus 137 for processing information and executing computer-executable instructions. Computer system 140 also includes a main memory 134, such as a RAM or other dynamic storage device, coupled to bus 137 for storing information and instructions executed by processor 138. In some embodiments, processor 138 can be the same as or similar to processor 106 and / or processor 306.

[0105] Main memory 134 may also be used for storing temporary variables or other intermediate information during execution of instructions by processor 138. Computer system 140 further includes read-only memory (ROM) 136 (or other nonvolatile memory) or other static storage device coupled to bus 137 for storing static information and instructions for processor 138. Also coupled to bus 137 are storage devices 135, such as magnetic or optical disks, hard disk drives that read from and write to hard disks, magnetic disk drives that read from and write to magnetic disks, and / or optical disk drives (such as DVDs) that read from and write to removable optical disks, for storing information and instructions. The hard disk drives, magnetic disk drives, and optical disk drives may be connected to the system bus by hard disk drive interfaces, magnetic disk drive interfaces, and optical disk drive interfaces, respectively. The drives and their associated computer-readable media provide nonvolatile storage of computer-readable instructions, data structures, program modules, and other data for the general-purpose computing device. Typically, computer system 140 includes an operating system (OS), stored in non-volatile storage, for managing computer resources and providing applications and programs with access to computer resources and interfaces. Generally, an operating system handles 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 / output devices, facilitating networking, and managing files. Non-limiting examples of operating systems are Microsoft Windows, Mac OS X, and Linux.

[0106] The term processor is intended to include an integrated circuit or other electronic device (or group of devices) capable of performing operations based 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 on a single substrate (e.g., a silicon "die") or distributed across two or more substrates. Furthermore, various functional aspects of a processor may be implemented solely as software or firmware associated with the processor.

[0107] The computer system 140 may be coupled via bus 137 to a display 131, such as a cathode ray tube (CRT), liquid crystal display (LCD), flat screen monitor, touch screen monitor, or similar means for displaying text and graphical data to a user. The display may be connected via a video adapter that supports the display. The display may allow a user to view, input, and / or edit information related to the operation of the system. Coupled to bus 137 are input devices 132, including alphanumeric and other keys, that communicate information and command selections to the processor 138. Another type of user input device includes a cursor control 133, such as a mouse, trackball, or cursor direction keys, that communicates directional information and command selections to the processor 138 to control cursor movement on the display 131. Typically, the input device has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that can specify a position in a plane.

[0108] Computer system 140 can be used to implement the methods and techniques described herein. According to one embodiment, these methods and techniques are performed by computer system 140 in response to processor 138 executing one or more sequences of one or more instructions contained in main memory 134. Such instructions may be read into main memory 134 from another computer-readable medium, such as storage device 135. Processor 138 executes the sequences of instructions contained in main memory 134 to perform the process steps described herein. In alternative embodiments, arrangements may be implemented using hardwired circuitry in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

[0109] The term computer-readable medium (or machine-readable medium), as used herein, is an extensible term meaning 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 executed by processing elements and / or control logic, as well as data operated on by processing elements and / or control logic, and can take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Transmission media include coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 137. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications, or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). For example, common forms of computer-readable media include floppy disks, flexible disks, hard disks, magnetic tape or other magnetic media, CD-ROMs, other optical media, punch cards, paper tape, any other physical media with a pattern of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described below, or any other medium from which a computer can read.

[0110] Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to processor 138 for execution. For example, the instructions may initially be carried on a magnetic 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 convert the data to an infrared signal using an infrared transmitter. An infrared detector receives 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 either before or after execution by processor 138.

[0111] Computer system 140 also includes a communication interface 141 coupled to bus 137. Communication interface 141 provides a two-way data communication coupling to a network link 139 that is connected to local network 111. For example, communication interface 141 may 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, communication interface 141 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. For example, Ethernet-based connections based on the IEEE 802.3 standard such as 10 / 100BaseT, 1000BaseT (Gigabit Ethernet), 10 Gigabit Ethernet (10GE or 10GbE or 10GigE according to IEEE Std 802.3ae-2002 as standard), 40 Gigabit Ethernet (40GbE), or 100 Gigabit Ethernet (100GbE according to Ethernet standard IEEE P802.3ba) can be used, as described in Cisco Systems, Publication No. 1-587005-001-3 (6 / 99), "Internetworking Technologies Handbook," Chapter 7: "Ethernet Technologies," pages 7-1 to 7-38. In such cases, communications interface 141 typically includes a LAN transceiver or modem, such as the Standard Microsystems (SMSC) LAN91C111 10 / 100 Ethernet transceiver described in Standard Microsystems Data Sheet "LAN91C111 10 / 100 Non-PCI Ethernet Single Chip MAC+PHY" Data Sheet, Revision 15 (02-20-04), which is incorporated herein for all purposes as if fully set forth in its entirety.

[0112] A wireless link 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.

[0113] Network link 139 typically provides data communication through one or more networks to other data devices. For example, network link 139 may provide a connection to a host computer through local network 111 or to data equipment operated by an Internet Service Provider (ISP) 142. ISP 142, in turn, provides data communication services through the Internet 11, a global packet data communication network. Local network 111 and the Internet 11 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and through communication interface 141 on network link 139, which carry the digital data to and from computer system 140, are exemplary forms of carrier waves transporting the information.

[0114] The received code may be executed by processor 138 as it is received by processor 138, and / or stored in memory 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.

[0115] Embodiments of the present disclosure include the concepts of a) detecting CGM sensor compression (e.g., PISA), b) improving the accuracy of sensing by detecting compression artifacts inherent in CGM sensors or devices such as medical and pharmaceutical devices, and / or c) improving the operation of continuous subcutaneous insulin infusion therapy and related systems such as sensor-augmented pumps (SAP), low glucose suspend (LGS), predictive low glucose suspend (PLGS), or automated insulin delivery (AID), also known as artificial pancreas. As illustrated by the embodiments described herein, the embodiments are applicable to a) devices that provide signal and / or multi-signal detection of CGM sensor compression (e.g., PISA), b) devices that improve sensing accuracy by detecting compression artifacts inherent in CGM sensors or devices such as medical and pharmaceutical devices, and / or c) devices that improve the operation of continuous subcutaneous insulin infusion therapy and related systems such as sensor-augmented pumps (SAP), low glucose suspend (LGS), predictive low glucose suspend (PLGS), or automated insulin delivery (AID), also known as artificial pancreas, and can be implemented and utilized in conjunction with associated processors, networks, computer systems, the Internet, and components and functionality according to the embodiments disclosed herein.

[0116] FIG. 12 illustrates an exemplary system and / or network in which embodiments can be implemented. In some embodiments, a subject (or patient) can locally implement a glucose monitor (e.g., glucose monitoring device 302), artificial pancreas, or insulin device (e.g., insulin device 304) (or other interventional or diagnostic device) at home or other desired location. However, in other embodiments, the glucose monitor can also be implemented in a clinic or assisted environment. For example, referring to FIG. 12 , clinic environment 158 ​​provides a location for a physician (e.g., 164) or clinician / assistant to diagnose a patient (e.g., 159) with glucose-related disorders and related diseases and conditions. Glucose monitoring device 10 can be used as a standalone device to monitor and / or test a patient's glucose level. In some embodiments, glucose monitoring device 10 can be the same as or similar to glucose monitoring device 302.

[0117] 12 (e.g., glucose monitoring device 10) can be attached to or in communication with a patient as desired or needed. For example, a system or combination of its components, including glucose monitoring device 10 (or a controller, and / or other associated devices or systems, such as an artificial pancreas, insulin pump (or other interventional or diagnostic device), or any other desired or necessary device or component), can contact, communicate with, or be attached to the patient via tape or tubing (or other medical implement or component), or can communicate through 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 stay or family).

[0118] A physician (clinician or assistant) can use the glucose monitor output for appropriate action, such as insulin injection or food feeding for the patient, or other appropriate action or modeling. Alternatively, the glucose monitor output can be delivered to a computer terminal 168 for immediate or future analysis. Delivery can be via cable, wireless, or any other suitable medium. The glucose monitor output from the patient can also be delivered to a portable device, such as a mobile device 166. Accurate glucose monitor output can also be delivered to a glucose monitoring center 172 for processing and / or analysis. Such delivery can be achieved in many ways, such as via a network connection 170, which can be wired or wireless.

[0119] In addition to the glucose monitoring device output, errors, parameters for accuracy improvement, and any accuracy-related information may also be distributed to computer 168 and / or glucose monitoring center 172, etc., to perform error analysis. This may result in centralized accuracy monitoring, modeling, and / or accuracy enhancement of the glucose center (or other intervention or diagnostic center) due to the importance of the glucose sensor (or other intervention or diagnostic sensor or device).

[0120] 13 is a block diagram illustrating an example machine 1300 capable of implementing one or more aspects of the embodiments. The machine 1300 may include, but is not limited to, systems, methods, and computer-readable media that provide a) multi-signal detection of CGM sensor compression (e.g., PISA), b) improved sensing accuracy by detecting compression artifacts inherent in CGM sensors or devices such as medical and drug devices, and / or c) improved operation of continuous subcutaneous insulin infusion therapy and related systems such as sensor-augmented pumps (SAPs), low glucose suspends (LGSs), predictive low glucose suspends (PLGSs), or automated insulin delivery (AIDs), also known as "artificial pancreases." A block diagram of an example machine 1300 is shown that may implement (e.g., execute) one or more aspects of the embodiments (e.g., the described methodologies).

[0121] An example of machine 1300 may include logic, one or more components, circuits (e.g., modules), or mechanisms. Circuits are tangible entities configured to perform specific operations. In some examples, circuits may be arranged in a specified manner (e.g., internally or relative to external entities such as other circuits). In some examples, software (e.g., instructions, application portions, or applications) may configure one or more computer systems (e.g., standalone, client, or server computer systems) or one or more hardware processors (processors) as circuitry that operates to perform specific operations as described herein. In some examples, software may reside (1) on a non-transitory machine-readable medium or (2) within a transmission signal. In some examples, software, when executed by hardware underlying the circuitry, causes the circuitry to perform specific operations.

[0122] In some examples, a circuit may be implemented mechanically or electronically. For example, a circuit may include dedicated circuitry or logic specifically configured to perform one or more of the techniques described above, including a dedicated processor, a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC). In some examples, a circuit may include programmable logic (e.g., circuitry contained within a general-purpose processor or other programmable processor) that can be temporarily configured (e.g., by software) to perform particular operations. It will be appreciated that whether a circuit is implemented mechanically (e.g., in dedicated, permanently configured circuitry) or in temporarily configured circuitry (e.g., configured by software) may be determined by cost and time considerations.

[0123] Thus, the term circuit can refer to a tangible entity that is physically configured and permanently configured (e.g., hardwired) or temporarily (e.g., transiently) configured (e.g., programmed) to operate in a specified manner or to perform specified operations. In some instances, given multiple temporarily configured circuits, it is not necessary for each circuit to be configured or instantiated at any one instance in time. For example, if a circuit can include a general-purpose processor configured via software, the general-purpose processor can be configured as different circuits at different times. Thus, the software can configure the processor to configure a particular circuit at one instance in time and a different circuit at a different instance in time, for example.

[0124] In some examples, circuits can provide information to and receive information from other circuits. In these examples, circuits can be considered to be communicatively coupled to one or more other circuits. When multiple such circuits exist simultaneously, communication can be achieved through signal transmissions connecting the circuits (e.g., via appropriate circuits and buses). In embodiments in which multiple circuits are configured or instantiated at different times, communication between such circuits can be achieved, for example, through the storage and retrieval of information in a memory structure accessible to the multiple circuits. For example, one circuit can perform an operation and store the output of this operation in a communicatively coupled memory device. Further, another circuit can subsequently access the memory device and retrieve and process the stored output. In some examples, circuits can be configured to initiate or receive communication with input or output devices and can operate on resources (e.g., collections of information).

[0125] Various operations of example methods described herein may be performed at least in part by one or more processors that are temporarily or permanently configured (e.g., by software) to perform the associated operations. Such processors, whether temporarily or permanently configured, may include processor-implemented circuitry that operates to perform one or more operations or functions. In some examples, circuitry referred to herein may include processor-implemented circuitry.

[0126] Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of the methods may be performed by one or more processors or processor-implemented circuitry. Performance of certain operations may be distributed among one or more processors that reside within a single machine as well as deployed across multiple machines. In some examples, one or more processors may be located in a single location (e.g., in a home environment, in an office environment, or as a server farm), while in other examples, the processors may be distributed across multiple locations.

[0127] The one or more processors may also operate to support performance of the associated operations in a cloud computing environment or as software as a service (SaaS), which may run on a remote server and be accessible or usable by one or more client devices. For example, a cluster of computers (as examples of machines that include the processor) may perform at least some of the operations, which are accessible via a network (e.g., the Internet) and one or more appropriate interfaces (e.g., application program interfaces (APIs)).

[0128] Example embodiments (e.g., devices, systems, or methods) can be implemented in digital electronic circuitry, computer hardware, firmware, software, or any combination of these. Example embodiments can 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 control of the operation of, a data processing apparatus such as a programmable processor, a computer, or multiple computers).

[0129] A computer program can be written in any form of programming language, including compiled or interpreted languages, and 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 at one site, or distributed across multiple sites and interconnected by a communications network.

[0130] In some examples, operations may be performed by one or more programmable processors executing computer programs that perform functions by operating on input data to generate output. Example 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)).

[0131] The computer system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. It will be understood that embodiments deploying a programmable computing system need to consider both hardware and software architectures. In particular, it will be understood that the selection of whether to implement a particular function in permanently configured hardware (e.g., ASICs), temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware can be a design choice. The following describes hardware (e.g., machine 1300) and software architectures that may be deployed in example embodiments.

[0132] In some examples, machine 1300 may operate as a stand-alone device, or machine 1300 may be connected (eg, networked) to other machines.

[0133] In a network deployment, machine 1300 can operate in the capacity of either a server or a client machine in a server-client network environment. In some examples, machine 1300 can act as a peer machine in a peer-to-peer (or other distributed) network environment. Machine 1300 can be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web appliance, a network router, switch, or bridge, or any machine capable of executing instructions (series or otherwise) that specify actions to be taken (e.g., performed) by machine 1300. Furthermore, although only a single machine 1300 is shown, the term “machine” should also be interpreted to include any group of machines, individually or collectively, that execute an instruction set (or multiple instruction sets) to perform any one or more of the methodologies described herein.

[0134] The example machine (e.g., computer system) 1300 can include a processor 1302 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory 1304, and a static memory 1306, some or all of which can communicate with each other via a bus 1308. The machine 1300 can further include a display unit 1310, an alphanumeric input device 1312 (e.g., a keyboard), and a user interface (UI) navigation device 1311 (e.g., a mouse). In some examples, the display screen 1310, the input device 1312, and the UI navigation device 1314 can be touch screen displays. The machine 1300 can also include a memory 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, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or other sensors. In some embodiments, sensor 1321 can be the same as or similar to sensor 110 and / or sensor 410 .

[0135] Storage device 1316 may include machine-readable medium 1322 that stores 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 functions described herein. The instructions 1324 may reside, completely or at least partially, within main memory 1304, static memory 1306, or processor 1302 during execution of the instructions by machine 1300. In an example, one or any combination of processor 1302, main memory 1304, static memory 1306, or storage device 1316 may constitute a machine-readable medium.

[0136] Although machine-readable medium 1322 is depicted as a single medium, the term "machine-readable medium" can include a single medium or multiple media (e.g., centralized or distributed databases and / or associated caches and servers) configured to store one or more instructions 1324. The term "machine-readable medium" can also be interpreted to include any tangible medium that can store, encode, or carry instructions that are executed by a machine to cause the machine to perform any one or more of the methodologies of the present disclosure, or that can store, encode, or carry data structures used by or associated with such instructions. Accordingly, the term "machine-readable medium" can be interpreted to include, but is not limited to, solid-state memory, and optical and magnetic media. Specific examples of machine-readable media include, by way of example only, non-volatile memory including semiconductor memory devices (e.g., Electronically Programmable Read-Only Memory (EPROM), Electronically 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.

[0137] The instructions 1324 may be further transmitted or received over a communications network 1326 using a transmission medium via the network interface device 1320, utilizing any one of a number of transport protocols (e.g., Frame Relay, IP, TCP, UDP, HTTP, etc.). Example communications networks may include local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile phone networks (e.g., cellular networks), voice (Plain Old Telephone (POTS)) networks, wireless data networks (e.g., the IEEE 802.11 family of standards known as Wi-Fi®, the IEEE 802.16 family of standards known as WiMax®), peer-to-peer (P2P) networks, etc. The term “transmission medium” should be interpreted to include any intangible medium capable of storing, encoding, or carrying machine-executable instructions, as well as digital or analog communications signals or other intangible media that facilitate the communication of such software.

[0138] Any processor disclosed herein can be part of or in communication with a machine (e.g., a computing device, logic device, circuit, operational module (hardware, software, and / or firmware), etc.). A processor can be hardware (e.g., a processor, integrated circuit, central processing unit, microprocessor, core processor, computing device, etc.), firmware, software, etc. configured to perform operations by execution of instructions embodied 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. A processor can receive, process, and / or store data.

[0139] Any processor disclosed herein may be a scalable processor, a parallelizable processor, a multithreaded processor, or the like. A processor may be a computer whose processing power is selected as a function of expected network traffic (e.g., data flow). A processor may include an integrated circuit or other electronic device (or group of devices) capable of performing operations based 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), or the like. The hardware of such a device may be integrated on a single substrate (e.g., a silicon "die") or distributed across two or more substrates. Various functional aspects of a processor may be implemented solely as software or firmware associated with the processor.

[0140] The processor may include one or more processing or operating modules. The processing or operating modules may be software or firmware operating modules configured to implement any of the functions disclosed herein. The processing or operating modules may be embodied as software and stored in memory operatively associated with the processor. The processing modules may be embodied as web applications, desktop applications, console applications, etc.

[0141] 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 described herein may be a computer-readable memory configured to store data. The memory may include volatile or non-volatile memory, temporary or non-transitory memory, and may be embodied as in-memory, active memory, cloud memory, 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), electronically erasable programmable read-only memory (EEPROM), FLASH-EPROM, compact disc (CD)-ROM, digital optical disc (DVD), optical storage, optical media, carrier waves, magnetic cassettes, magnetic tapes, magnetic disk storage, other magnetic storage devices, or any other medium usable to store desired information and accessible by a processor.

[0142] The memory can be a non-transitory computer-readable medium. As used herein, the term "computer-readable medium" (or "machine-readable medium") is an expansive term that refers to any medium or any memory that participates in providing instructions to a processor 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 executed by processing elements and / or control logic, as well as data operated on by processing elements and / or control logic, and can take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. A computer or machine-readable medium can be configured to store one or more instructions or computer programs. The instructions or computer programs can be in the form of an algorithm, program logic, etc. that cause a processor to perform any of the functions disclosed herein.

[0143] Embodiments of the memory may include processor modules and other circuitry that enable transfer of data to and from the memory, which may include transfer to and from other components of the communication system. This transfer may occur via wired or wireless transmission. The communication system may include transceivers that may be used in combination with switches, receivers, transmitters, routers, gateways, waveguides, etc. to facilitate communication via a communication approach or protocol for controlled, coordinated signal transmission and processing to any other component or combination of components of the communication system. The transmission may occur via a communication link. The communication link may be electronic-based, optical-based, optoelectronic-based, quantum-based, etc. The communication may occur via Bluetooth, near field communication, cellular communication, telemetry communication, internet communication, etc.

[0144] Data and signals may be transmitted over a transmission medium which may include coaxial cables, copper wire, fiber optics, etc. Transmission media may also take the form of acoustic 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.).

[0145] Any processor may communicate with other processors of other devices (e.g., computing devices, computer systems, laptop computers, desktop computers, etc.). For example, a processor of the system configuration 100 may communicate with a processor of another computing device 104, which in turn may communicate with a processor of a display, sensor 110, etc. Any processor may include a transceiver or other communications device / circuitry to facilitate the transmission and reception of wireless signals. Any processor may include an application programming interface (API) as a software intermediary that allows two or more applications to communicate with each other. The use of an API may allow software on one processor to communicate with software on another processor on another device(s).

[0146] Any data or communication transfer between two components can be a push and / or pull operation. For example, data transfers such as between processor 106 and memory 108 or between processor 106 and sensor 110 can be a push operation (e.g., data can be pushed from memory) and / or a pull operation (e.g., the processor can pull data from memory), data transfers between another system and computing device 104 can be a push and / or pull operation, as can other data transfers.

[0147] As the components receive data, they can process it in real time, store it in memory for later processing, or a combination of both. After processing the data, the components can use the processed data in real time, store it in memory for later use, or a combination of both. The pre-processed data and / or the processed data can be encoded, tagged, or labeled before, during, or after storage in memory.

[0148] As described herein, the system configuration 100 can include a memory 108 containing a computer program (e.g., software instructions for the compression detection system 102) that, when executed, can cause the processor 106 to perform any of the functions / operations disclosed herein.

[0149] The computer program can cause the processor to execute one or more machine learning models (e.g., a linear regression model, a tree-based model, a perceptron-based model, a Gaussian-based model, etc.) It is envisioned that at least one of the machine learning models is a random forest or an AdaBoost machine learning model. [Example]

[0150] CGM sensors, widely used in diabetes treatment, are vulnerable to so-called "compression artifacts" or pressure-induced sensor attenuation (PISA). Compression artifacts frequently occur when the sensor is compressed, such as when a person sleeps with the sensor on its arm, and are characterized by a sudden drop in sensor readings followed by an eventual recovery. These deviations have adverse effects on various aspects of diabetes treatment.

[0151] The inventors hypothesize that (i) under nominal conditions, when there is no pressure on the sensor, there is equilibrium between the interstitial fluid (ISF) in which the CGM sensor measures glucose and the local sensor compartment (LSC) in the tissue immediately surrounding the CGM sensor needle, and (ii) when pressure is applied to the sensor, the LSC is compressed, upsetting the balance by decreasing glucose influx, increasing glucose efflux, or both, which results in a decay in the sensor's electrochemical signal and causes the sensor to read a low value until equilibrium is restored.

[0152] Based on this logic, in one embodiment, a method and system for detecting CGM sensor compression artifacts includes three components: (i) a physiological compartmental model of glucose transport between ISF and LSC, (ii) distributions of model parameters that are fixed by observation under nominal conditions, i.e., in the absence of compression artifact, and (iii) a method for tracking the model parameters in real time from CGM sensor data and detecting deviations from nominal conditions (parameters) that are interpreted as compression artifacts.

[0153] Aspects of embodiments of the methods and systems of the present invention aim to detect CGM sensor compression lows in real time to prevent compression lows from adversely affecting diabetes treatment, such as false hypoglycemia alarms or insulin blockage by the insulin delivery system. In aspects of embodiments of the present invention, the insulin delivery system can be (i) a sensor-augmented pump (SAP) therapy, (ii) a low glucose suspend (LGS) system or a predictive low glucose suspend system (PLGS), or (iii) an automated insulin delivery (AID) also known as an "artificial pancreas."

[0154] Aspects of embodiments of the present invention generally relate to pharmaceuticals and medical devices such as those used in monitoring blood glucose levels in the treatment of diabetes and other metabolic disorders, 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 another embodiment, the present invention improves the accuracy of continuous glucose monitoring (CGM) devices by detecting the compression artifact inherent in these sensors. This in turn improves the performance of subcutaneous insulin infusion therapy and related systems such as sensor-augmented pumps (SAPs), low glucose suspends (LGSs), predictive low glucose suspends (PLGSs), or automated insulin delivery (AIDs), also known as "artificial pancreases."

[0155] In another embodiment, aspects of the present invention provide, 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). Widely used CGM sensors for diabetes treatment have been vulnerable to compression low artifacts ever since their introduction over 20 years ago, and this problem remains unresolved. Compression artifacts frequently occur when the sensor is compressed, such as when a person sleeps with the sensor on its arm, and are characterized by a sudden drop in sensor readings followed by an eventual recovery. Such artifacts have adverse effects on various aspects of diabetes treatment, including, but not limited to, false hypoglycemic alarms and erroneous therapeutic actions taken by low glucose suspend (LGS), predictive low glucose suspend (PLGS), or automated insulin delivery (AID) systems.

[0156] Aspects of embodiments of the present invention are based on a physiological model of glucose concentration in the LSC and the glucose flux occurring during both normal and pressure conditions in the sensing area. The Compression Low Hypothesis posits that (1) in the nominal state, when there is no pressure on the sensor, the interstitial fluid (ISF) where the CGM sensor measures glucose and the local sensor compartment (LSC) in the tissue immediately surrounding the needle of the CGM sensor are in equilibrium, thus balancing glucose flux into and out of the LSC; and (2) when pressure is applied to the sensor, the LSC is compressed, disrupting the balance by reducing the influx of glucose and possibly oxygen (not considered in this model), increasing glucose efflux, or both. This disruption in the balance between glucose influx and efflux results in a decay in the sensor's electrochemical signal, causing the sensor to read low until equilibrium is restored.

[0157] A compartmental model can be used to describe the glucose diffusion phenomenon between the ISF and LSC, where the balance of flux between the ISF and LSC is governed by two parameters: influx and efflux. A formal compartmental block diagram of ISF-LSC fluid exchange can be represented by the following equation: TIFF2025538984000009.tif13150(1) where G LSC and G ISF are the glucose concentrations in the LSC and ISF, respectively, k1 is the clearance, and k0 is The glucose transport rate into the LSC is set at TIFF2025538984000010.tif13150.

[0158] In different embodiments of the method, model inputs are obtained at a fixed frequency, for example, every 30 seconds when 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). ISF The model output is the sensor reading G LSC , i.e., a function of the glucose concentration in the local sensor compartment. The clearance parameter k1 is identified by solving equation (1) above in real time using the inputs and outputs. Under nominal conditions, k1 has a stable value that increases when a compression low occurs. As a result, the clearance parameter k1 is determined by G ISF and G LSC In fact, the estimate of the clearance parameter k1 is G ISF Therefore, clearance depends strictly on the difference between ISF and LCS glucose concentrations and not on interstitial glucose fluctuations. This is an important design feature of the method, assuring that compression low detection is independent of normal physiologic fluctuations in ISF glucose.

[0159] One of the key elements of this embodiment of the present invention is the nominal distribution of the clearance parameter k1. Once this is determined, deviations from the nominal distribution of the real-time estimate of the clearance parameter k1 indicate the occurrence of a compression low. The nominal distribution of the clearance parameter k1 is obtained using simultaneous data from multiple (up to four) sensors inserted into the same individual at the same time. In this multi-sensor environment, one, and very rarely two, of the sensors may exhibit a compression low, while the true ISF glucose concentration is measured by the other sensors. Therefore, the model estimates (1) Glucose concentration from data from the sensor(s) not affected by the compression low over a 2.5-minute window; ISF (2) obtain model inputs such as G from the sensor data affected by the compression low over the same time window. LSC (3) quantifying the outflow from the LSC by identifying the model using the prescribed input and output signals to obtain a real-time estimate of the clearance parameter k1; and (4) sliding the time window over small increments of time, such as 1 minute, to generate a real-time track of any clearance changes.

[0160] An example of the procedure in a multi-sensor environment is shown in Figure 6. CGM readings from three different sensors are shown (solid blue, black, and red lines). The dashed purple line represents the G calculated according to step 1. ISF In Figure 6, sensor Abd1 exhibits a series of two compression lows, and the corresponding identified clearance (second panel from the top) shows a high value of k1 during the compression lows. Sensors Arm1 and Arm2 do not exhibit compression lows, and the corresponding identified clearance for each sensor (third and fourth panels, respectively) shows a stable value. In Panel B, sensor Arm2 exhibits a compression low flagged by an increase in the clearance parameter k1, while the other two sensors remain stable.

[0161] Using a data set of N=44 individuals each wearing up to four sensors and following steps 1-4 above, the nominal distribution of the clearance parameter k1 and the distribution of this parameter during compression lows are determined to have the following properties: Table 1: Nominal distribution of k1 vs. compression low value distribution TIFF2025538984000011.tif51153

[0162] As can be seen in Table 1, these characteristics are sufficiently different between the nominal and low compression states that low compression values ​​can be identified by measuring whether the real-time estimate of the clearance parameter k1 is within the normal range.

[0163] Once it is determined that the nominal distribution of the clearance parameter k1 differs from the distribution of k1 during a compression low state, the logic for flagging compression lows in real time may be (1) derived from data of a sensor(s) that is not affected by a compression low over a time window such as 1 minute, 2.5 minutes, 5 minutes, etc., or through delay, extrapolation, prediction, or smoothing of single sensor data using a sensor working period that is not affected by a compression low; ISF (2) G LSC(3) using the defined input and output signals to discriminate the model to obtain a real-time estimate of the clearance parameter k1; (4) sliding a time window over small time increments, such as 1 minute, to generate a real-time track of changes in any clearance parameter k1; (5) flagging the onset of a compression low when the value of k1 exceeds some predetermined threshold, such as about 1.1, corresponding to a cutoff point that better distinguishes between the nominal distribution of the clearance parameter k1 and the compression low distribution; and (6) signaling the end of a compression low when the value of k1 falls below some predetermined threshold, such as about 0.9, corresponding to a cutoff point below the nominal distribution of the clearance parameter k1. As described in the next section, step (6) can be applicable only to a single-sensor solution when using the sensor trace as both input and output to the physiological model.

[0164] As an alternative to steps (5) and (6) above, the time series of k1 values ​​can be used as input to a compression low detection procedure using time series prediction, machine learning, or other approaches. An example implementation of this is described below.

[0165] Input G ISF and output G ISF A single-sensor solution, where both G and G are obtained from the same sensor data stream and used to flag low pressure values ​​experienced by this sensor, is an expected practical implementation of this method. In this approach, following steps 1-6 above, a model input G is derived from a short-term (minutes ahead) delay, extrapolation, or prediction of the sensor values ​​using, for example, linear regression, autoregression, moving average, or other standard time series prediction methods. ISF Get.

[0166] The extrapolation / prediction horizon is determined by the frequency of data acquisition and is typically about 5 times larger than the time interval between successive data points, e.g., 2.5 to 5 minutes for a 30- to 60-second data acquisition rate. For example, the lower panel of Figure 8 shows the expected characteristic behavior of the clearance parameter k1 described in steps (5) and (6): (i) at the beginning of the compression low, the delayed trace (input to the method) remains higher than the sensor data, which is interpreted as an increased glucose efflux from the LSC, leading to a rapid increase in the clearance parameter k1; and (ii) at the end of the compression low, the delayed trace remains lower than the sensor data, which is interpreted as an influx of glucose into the LSC, leading to a decrease in the clearance parameter k1 below the nominal limit. A possible third compression low is not flagged because the amplitude of the clearance parameter k1 does not exceed the threshold predefined in steps (5) and (6).

[0167] To validate these multiple embodiments, we used training and test datasets containing manually annotated "reference ground truth" compression lows. There was CGM data for a total of 111 subjects, with 94,610 hours of collected sensor data. 67 subjects (60.4%) were assigned to the training dataset, and 44 subjects (39.6%) were assigned to a separate test dataset. The training dataset was used to train and cross-validate method components, while the test dataset was used solely to test the method once the method components were finalized and locked.

[0168] Using sensor data from all 111 subjects, a review of the time series was used to annotate each time series containing compression lows (e.g., hypo-compression lows or near hypo-compression lows) where the minimum BG value was less than 85 mg / dL, resulting in 1,623 hypo-compression lows or near hypo-compression lows. Of the 1,623 annotations, 1,089 were present in the training dataset from 58,403 hours of sensor data. As shown in later sections, these annotations were used as references to test the performance of the method in different embodiments. Specifically, steps 1-4 described above in Section 4.5 result in a time series of values ​​for parameter k1, which can be used to determine the presence of compression lows in several different ways (method or related system embodiments).

[0169] An embodiment of this method (or related system) directly follows steps (5) and (6) described above, i.e., using a specific threshold crossing of k1 to determine the onset of compression lows. Figure 9A shows the ROC performance curve for the test set. In this case, a moving average is used to generate the k1 time series; as noted above, the k1 time series can be generated using other short-term (minutes ahead) forecasting approaches, such as lagging, extrapolating, or forecasting sensor values ​​using linear regression, autoregression, or other standard time series forecasting methods. The area under the ROC curve is 96%, which generally indicates very good performance. The model in Figure 9B offers another view of the same performance through a precision-recall (PR) curve, which summarizes the trade-off between true positive rate and positive predictive value. In this case, the mean precision (AP) score of the PR curve is 0.38, meaning that the weighted average precision across all thresholds, using the recall at these thresholds as weights, is 0.38, a result typically considered "good." It should be noted that although the area under the ROC curve and the AP score of the PR curve are related, the AP score may be considered more sensitive in distinguishing between algorithms with similar (usually good) performance.

[0170] An embodiment of this 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 thereof. These models utilize the k1 time series in combination with other in-sensor data such as temperature, but do not utilize sensor glucose readings as input. In other words, the k1 time series is the only glucose-related input to these models.

[0171] An embodiment of this method (or related system) uses the k1 time series generated by moving average extrapolation and sensor glucose data as inputs to the same two standard machine learning models described: Random Forest and AdaBoost.

[0172] From the various embodiments described, it can be concluded that the method works well as a simple, single-sensor, stand-alone compression low threshold detector, but its performance can be improved by using advanced machine learning models and additional inputs such as sensor glucose, etc. This trade-off can require data processing power.

[0173] It will be understood that the embodiments disclosed herein can be modified to meet a particular set of design criteria. For example, any of the components described herein can be of any suitable number or type to meet a particular purpose. Thus, while several exemplary embodiments of the systems, and methods of making and using the same, disclosed herein have been shown and described, it is to be clearly understood that the disclosure is not limited to these embodiments, but can be variously embodied and practiced within the scope of the following claims.

[0174] Although some components, features, and / or configurations may be described in connection with only one particular embodiment, it will be understood that, unless otherwise stated, or to the extent that such components, features, and / or configurations are technically possible for use with other embodiments, these components, features, and / or configurations can also be applied or used with many other embodiments and should be considered applicable to other embodiments. Accordingly, the components, features, and / or configurations of the various embodiments can be combined in any manner, and such combinations are expressly contemplated and disclosed by this description.

[0175] Those skilled in the art will understand that the present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. Accordingly, the embodiments disclosed herein are to be considered in all respects as illustrative and not restrictive. The scope of the present disclosure is indicated by the appended claims, rather than the foregoing description, and all changes that come within the spirit, scope, and equivalents thereof are intended to be embraced therein. Furthermore, the disclosure of a range of values ​​is a disclosure of all numerical values ​​within that range, including the endpoints. [Explanation of symbols]

[0176] 100 System Configuration 102 PISA Detection System 104 Computer Equipment 106 processors 108 memory 110 Sensors

Claims

1. 1. A system for automatic real-time detection of sensor compression in continuous glucose monitoring, comprising: at least one sensor; at least one processor in communication with the at least one sensor and executing program code; wherein the at least one processor collecting first measurement data including at least one time series of blood glucose (BG) measurements measured by the at least one sensor while the at least one sensor is not receiving compression; receiving second measurement data from the at least one sensor, the second measurement data including at least one BG measurement measured by the at least one sensor; determining a clearance value between BG measurement values ​​based on the first measurement data and the second measurement data; generating a signal output indicating that the at least one sensor is experiencing compression based on the clearance value between BG measurements being above a predetermined threshold. programmed or configured to cause the processor to A system characterized by:

2. The at least one processor in combination with at least one memory device configured to store the first measurement data and configured to retrieve the first measurement data comprises: retrieving the first measurement data from the memory device; The system of claim 1 , programmed or configured to cause the processor to:

3. at least one time series of BG measurements of the first measurement data includes a plurality of timestamps, each timestamp being associated with a BG measurement; The system of claim 1 .

4. The at least one processor configured to determine a clearance value between BG measurements comprises: determining a clearance value between the at least one BG reading of the second measurement data and a first BG reading associated with a first timestamp of the first measurement data; The system of claim 3 , programmed or configured to cause the processor to:

5. The at least one processor configured to receive second measurement data including at least one BG measurement, receiving the second measurement data from the at least one sensor, the second measurement data including successive BG measurements, as the BG measurements are being taken in real time by the at least one sensor; The system of claim 1 , programmed or configured to cause the processor to:

6. The at least one processor configured to determine a clearance value between BG measurements comprises: determining in real time, as each successive BG measurement is received, each clearance value of a plurality of clearance values ​​between each successive BG measurement and each BG measurement of the first measurement data; 6. The system of claim 5, wherein a first BG reading in the series of BG readings is associated with a first timestamp of the first measurement data.

7. The clearance value is where G LSC is the glucose concentration in the local sensor compartment of at least one sensor, and G ISF is the glucose concentration in the interstitial fluid, is the rate of change of glucose concentration in the local sensor compartment, and k 1 is the clearance value, and k 0 is the glucose transport rate, That is, The system of claim 1 .

8. At least one processor configured to generate a signal output includes: indicating in real time via outputting an indication that the at least one sensor is receiving compression while the at least one sensor is obtaining a BG measurement. The system of claim 1 , programmed or configured to cause the processor to:

9. in combination with an insulin delivery system in communication with the at least one processor; the at least one processor is programmed or configured to cause the processor to send a signal output to the insulin delivery system indicative of the at least one sensor being compressed; the signal output causes the insulin delivery system to do at least one or more of: start insulin delivery; continue insulin delivery; disable an alarm; and / or any combination thereof; The system of claim 1 .

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

11. The predetermined threshold value is 1.05 to 1.

3. The system of claim 1 .

12. In combination with at least one additional sensor, the at least one processor: receiving first measurement data from the at least one additional sensor, the first measurement data including at least one time series of BG measurements; storing the first measurement data in the at least one memory device; The system of claim 2 , programmed or configured to cause the processor to:

13. the at least one time series of BG measurements is measured by the at least one sensor before the second measurement data; The system of claim 1 .

14. the first measurement data includes at least one BG measurement extrapolated from the at least one time series of BG measurements; The system of claim 1 .

15. 1. A system for automatic real-time detection of end of sensor compression in continuous glucose monitoring, comprising: at least one sensor; at least one processor in communication with the at least one sensor and executing program code; wherein the at least one processor receiving first measurement data from the at least one sensor, the first measurement data including at least one time series of blood glucose (BG) measurements measured by the at least one sensor while the at least one sensor is undergoing compression; receiving second measurement data from the at least one sensor, the second measurement data including at least one BG measurement measured by the at least one sensor after the at least one time series of BG measurements is measured by the at least one sensor; determining a clearance value between BG measurement values ​​based on the first measurement data and the second measurement data; generating a signal output indicating that the at least one sensor is no longer experiencing compression based on the clearance value between BG measurements being less than a predetermined threshold. programmed or configured to cause the processor to A system characterized by:

16. at least one time series of BG measurements of the first measurement data includes a plurality of timestamps, each timestamp being associated with a BG measurement; 16. The system of claim 15.

17. The at least one processor configured to receive second measurement data including at least one BG measurement, receiving the second measurement data from the at least one sensor, the second measurement data including successive BG measurements, as the BG measurements are being taken in real time by the at least one sensor; 16. The system of claim 15, programmed or configured to cause the processor to:

18. The at least one processor configured to determine a clearance value between BG measurements comprises: determining in real time, as each successive BG measurement is received, each clearance value of a plurality of clearance values ​​between each successive BG measurement and each BG measurement of the first measurement data; 20. The system of claim 17, wherein a first BG reading in the successive BG readings is associated with a first timestamp of the first measurement data.

19. The clearance value is where G LSC is the glucose concentration in the local sensor compartment of at least one sensor, and G ISF is the glucose concentration in the interstitial fluid, is the rate of change of glucose concentration in the local sensor compartment, and k 1 is the clearance value, and k 0 is the glucose transport rate, That is, 16. The system of claim 15.

20. At least one processor configured to generate a signal output includes: outputting an indication in real time that the at least one sensor is not experiencing compression while the at least one sensor is obtaining BG measurements; 16. The system of claim 15, programmed or configured to cause the processor to:

21. in combination with an insulin delivery system in communication with the at least one processor; the at least one processor is programmed or configured to cause the processor to send a signal output to the insulin delivery system indicative of the at least one sensor being compressed; the signal output causes the insulin delivery system to do at least one or more of: start insulin delivery; continue insulin delivery; disable an alarm; and / or any combination thereof; 16. The system of claim 15.

22. The predetermined threshold value is 0.9 to 1.

0.

16. The system of claim 15.

23. the at least one time series of BG measurements is measured by the at least one sensor before the second measurement data; 16. The system of claim 15.

24. the first measurement data includes at least one BG measurement extrapolated from the at least one time series of BG measurements; 16. The system of claim 15.

25. 1. A computer-implemented method for accurately detecting sensor compression in continuous glucose monitoring, comprising: receiving first measurement data including at least one time series of blood glucose (BG) measurements measured by a first sensor that is not under compression; receiving second measurement data including a plurality of BG measurements taken consecutively by a second sensor under compression; determining a plurality of clearance values ​​between BG measurements based on the first measurement data and the second measurement data; detecting that a third sensor is under compression based on a distribution of the plurality of clearance values; 10. A computer-implemented method comprising:

26. at least one time series of BG measurements of the first measurement data includes a plurality of timestamps, each timestamp being associated with a BG measurement; 26. The computer-implemented method of claim 25.

27. determining the plurality of clearance values ​​between the plurality of BG measurements of the second measurement data and the at least one time series of the first measurement data.

26. The computer-implemented method of claim 25.

28. the plurality of BG measurements are measured continuously in real time by the second sensor while the second sensor continuously determines each clearance value of the plurality of clearance values ​​along with each BG measurement; 26. The computer-implemented method of claim 25.

29. The plurality of clearance values ​​are: where G LSC is the glucose concentration in the local sensor compartment of at least one sensor, and G ISF is the glucose concentration in the interstitial fluid, is the rate of change of glucose concentration in the local sensor compartment, and k 1 is the clearance value, and k 0 is the glucose transport rate, That is, 26. The computer-implemented method of claim 25.

30. indicating that the third sensor is experiencing compression while the at least one sensor is taking a BG measurement.

26. The computer-implemented method of claim 25.

31. sending a signal output to the insulin delivery system indicative of the third sensor being compressed; the signal output causes the insulin delivery system to do at least one or more of: start insulin delivery; continue insulin delivery; disable an alarm; and / or any combination thereof; 26. The computer-implemented method of claim 25.

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