Invented by Paul V. Goode, James H. Brauker, Arpurv U. Kamath, James Patrick Thrower, Victoria Carr-Brendel, Dexcom Inc

The market for systems and methods for replacing signal artifacts in a glucose sensor data stream is rapidly growing, driven by the increasing prevalence of diabetes and the need for accurate and reliable glucose monitoring. This technology plays a crucial role in improving the quality of glucose data, which is essential for effective diabetes management. Glucose sensors are widely used by individuals with diabetes to monitor their blood sugar levels. These sensors work by measuring the glucose concentration in interstitial fluid and transmitting the data to a receiver device. However, the accuracy of glucose readings can be affected by various factors, including signal artifacts. Signal artifacts refer to any interference or distortion in the sensor data stream that can lead to inaccurate glucose readings. These artifacts can be caused by various sources, such as electromagnetic interference, temperature changes, motion artifacts, and sensor malfunctions. The presence of signal artifacts can significantly impact the reliability and usefulness of glucose data, making it difficult for individuals with diabetes to make informed decisions about their treatment. To address this issue, the market for systems and methods for replacing signal artifacts in a glucose sensor data stream has emerged. These technologies aim to identify and remove or correct signal artifacts, ensuring that the glucose data provided by the sensor is accurate and reliable. By improving the quality of glucose data, these systems and methods enable individuals with diabetes to make more informed decisions about their diet, medication, and overall diabetes management. One of the key drivers of this market is the increasing prevalence of diabetes worldwide. According to the International Diabetes Federation, approximately 463 million adults were living with diabetes in 2019, and this number is expected to rise to 700 million by 2045. As the number of individuals with diabetes continues to grow, the demand for accurate and reliable glucose monitoring solutions will also increase, driving the adoption of systems and methods for replacing signal artifacts. Furthermore, advancements in sensor technology and data processing algorithms have contributed to the growth of this market. Manufacturers are continuously developing more sophisticated sensors that are less prone to signal artifacts. Additionally, advanced algorithms are being developed to identify and correct signal artifacts in real-time, improving the accuracy and reliability of glucose data. The market for systems and methods for replacing signal artifacts in a glucose sensor data stream is highly competitive, with several key players offering innovative solutions. These players are investing in research and development to further enhance the performance of their products and gain a competitive edge. Additionally, collaborations between sensor manufacturers, software developers, and healthcare providers are becoming increasingly common to create comprehensive solutions that address the challenges associated with signal artifacts. In conclusion, the market for systems and methods for replacing signal artifacts in a glucose sensor data stream is witnessing significant growth due to the increasing prevalence of diabetes and the need for accurate and reliable glucose monitoring. This technology plays a crucial role in improving the quality of glucose data, enabling individuals with diabetes to make informed decisions about their treatment. With advancements in sensor technology and data processing algorithms, the market is expected to continue growing, offering more sophisticated solutions to address signal artifacts and enhance diabetes management.

The Dexcom Inc invention works as follows

Systems and Methods for Minimizing or Eliminating Transient Non-Glucose Related Signal Noise Due to Non-Glucose Rate Limiting Phenomena such as Ischemia, pH Changes, Temperature Changes, and the Like. The system monitors data from a glucose detector and detects artifacts with a higher amplitude compared to electronic or diffusion noise. The system continuously or intermittently replaces part or all of the data stream with signal estimation methods which specifically address transient artifacts. The system can also detect the severity and apply one or more factors to the signal estimation algorithm based on the severity. This includes applying different sets of parameters to an algorithm or applying distinct signal estimates algorithms.

Background for Systems and Methods for Replacing Signal Artifacts In A Glucose Sensor Data Stream

Diabetes mellitus occurs when the pancreas is unable to produce enough insulin (Type 1 or insulin-dependent) or if insulin is ineffective (Type 2 or not insulin-dependent). The diabetic sufferer has high blood sugar which can lead to a variety of physical derangements, such as kidney failure, skin ulcers or bleeding into vitreous in the eye, all of which are associated with the degradation of small blood vessel. Hypoglycemia (low blood sugar), also known as hypoglycemia, is caused by an overdose of insulin.

Conventionally a diabetic person will carry a self monitoring blood glucose (SMBG), which is usually uncomfortable finger pricking. A diabetic may only check their glucose levels two to four time per day due to lack of convenience and comfort. These intervals can be so long that a diabetic may not realize the condition until it is too late. This could lead to dangerous side effects. It is unlikely that a diabetes patient will ever take a SMBG test in a timely manner. They will also not be able to tell if the blood glucose level is increasing (higher), or decreasing (lower), based on traditional methods.

Consequently, various transdermal and implants electrochemical sensors for continuous detection and/or quantification of blood glucose values are being developed. Implantable glucose sensors are often plagued by complications and only provide a short-term, less accurate reading of blood sugar. Transdermal sensors also have problems with accurately reporting glucose values over long periods of time. Although some efforts have been made in order to collect blood glucose data and analyze blood glucose trends retrospectively, these efforts don’t help diabetics determine real-time information about blood sugar. There have been some attempts to get blood glucose data using transdermal devices, but similar problems have happened.

Data streams from glucose sensor are known to contain some noise. This noise is caused by unwanted electronic or diffusion-related system noise which degrades data quality. In conventional glucose sensors, some attempts have been made to smooth out the raw data stream that represents the concentration of blood sugar in the sample. This is done by smoothing and filtering Gaussian, random, white and/or low-amplitude noise.

Signal artifacts are signal noises that are caused by non-glucose reactions rate-limiting phenomena such as ischemia. pH changes, pressure changes, temperature, etc. By detecting and replacing the signal artifacts, a diabetic can receive accurate glucose estimates. This will allow them to take proactive measures to avoid hypoglycemic or hyperglycemic situations.

In a first embodiment, a method for analyzing glucose sensor data is provided. The method includes: monitoring data streams from the sensor and detecting transient signal artifacts that are not related to glucose but have a greater amplitude than system noise. At least some of these signal artifacts can be replaced by estimated glucose signal values.

In a first embodiment aspect, data signals are obtained from a non-invasive, minimally-invasive or invasive glucose sensor.

In one aspect of the first embodiment the step of obtaining data signals includes receiving data from an enzymatic or chemical glucose sensor, as well as data from an electrochemical, spectrophotometric (polarimetric), calorimetric (iontophoretic), iontophoretic and radiometric glucose sensors.

In one aspect of the first embodiment the step for obtaining data signals includes receiving data from an entirely implantable glucose detector.

In one aspect of the first embodiment the step for signal artifacts includes testing for ischemia in or near the glucose sensor.

In one aspect of the first embodiment the oxygen concentration is measured using a glucose sensor or an oxygen sensor located near the sensor.

In one aspect of the first embodiment the ischemia test step includes comparing the measurement from an air sensor located near or inside the glucose detector with the measurement from a sensor distal to the glucosesensor.

In one aspect of the first embodiment the glucose sensor is an electrochemical device with a working and a reference electrode. The ischemia testing step also includes pulsed-amperometric detection.

The glucose sensor is an electrochemical device that includes working, counter, and reference electrodes. Ischemia testing includes monitoring the counter-electrode.

In a first embodiment aspect, the glucose-testing procedure includes monitoring of the reference electrode.

In one aspect of the first embodiment the glucose sensor is an electrochemical device with an anode (anode) and a cathode (cathode), and the ischemia testing step includes monitoring the cathode.

In one aspect of the first embodiment the detection of signal artifacts includes monitoring the level of pH near the sensor.

In one aspect of the first embodiment the step for detecting signal artifacts includes monitoring the temperature near the sensor.

In one aspect of the first embodiment the detection of signal artifacts includes comparing the level of pH distal and proximal the sensor.

In one aspect of the first embodiment the detection of signal artifacts includes comparing the temperature distal and proximal the sensor.

In one aspect of the first embodiment the step for detecting signal artifacts includes monitoring the pressure or stress inside the glucose sensor.

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