Abstract: Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrythmia.

Abstract: Techniques are disclosed for explaining and visualizing an output of a machine learning system that detects cardiac arrythmia in a patient. In one example, a computing device receives cardiac electrogram data sensed by a medical device. The computing device applies a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia has occurred in the patient and a level of confidence in the determination that the episode of arrhythmia has occurred in the patient. In response to determining that the level of confidence is greater than a predetermined threshold, the computing device displays, to a user, a portion of the cardiac electrogram data, an indication that the episode of arrhythmia has occurred, and an indication of the level of confidence that the episode of arrhythmia has occurred.

Abstract: A method of non-invasively monitoring hematocrit levels includes monitoring a first emission response to the light provided at the first excitation wavelength, wherein the first emission response is monitored at a first wavelength and monitoring a second emission response to the light provided at the first excitation wavelength, wherein the second emission response is monitored at a second wavelength. A ratiometric value is calculated based on a ratio of the first emission response to the second emission response, wherein the ratiometric value corresponds with hematocrit level of the patient.

Abstract: A method of non-invasively monitoring advanced glycation end-product (AGE) concentrations includes providing incident light to patient tissue at one or more excitation wavelengths and monitoring the one or more emission responses at one or more emission wavelengths. Based on the emission responses monitored, a ratio is calculated based on a ratio of the first emission response to the second emission response.

Abstract: A method of non-invasively monitoring hemoglobin concentration includes providing incident light to patient tissue at a first excitation wavelength. The method further includes monitoring a first emission response at a first emission wavelength, wherein the first emission wavelength is selected to correspond with a maximum of the emission response, and monitoring a second emission response at a second emission wavelength, wherein the second emission wavelength is selected to correspond with a minimum of the emission response. A hemoglobin concentration is calculated based on a ratio of the first emission response to the second emission response.

Abstract: In some examples, a medical system includes a medical device. The medical device may include a housing configured to be implanted in a target site of a patient, a light emitter configured to emit a signal configured to cause a fluorescent marker to emit a fluoresced signal into the target site, and a light detector that may be configured to detect the fluoresced signal. The medical system may include processing circuitry configured to determine a characteristic of the fluorescent marker based on the emitted signal and the fluoresced signal. The characteristic of the fluorescent marker may be indicative of a presence of a compound in the patient, and the processing circuitry may be configured to track the presence of the compound of the patient based on the characteristic of the fluorescent marker.

Abstract: In various examples, an apparatus is configured for subcutaneously inserting an implantable device within a patient. The apparatus includes a dilator portion including a dilator including a dilator length. The dilator portion is configured to separate tissue to create a subcutaneous pocket within the patient sized and shaped to accommodate an implantable device within the subcutaneous pocket. A sheath portion includes a sheath sized and shaped to accommodate the dilator within a sheath lumen. The sheath is configured to accommodate an antenna of the implantable device with the dilator removed from within the sheath. The sheath includes a sheath length that is at least substantially as long as an antenna length. The sheath is configured to separate to allow removal of the sheath around the implantable device to remove the sheath from and leave the implantable device within the subcutaneous pocket within the patient.

Abstract: In various examples, an apparatus is configured for subcutaneously inserting an implantable device within a patient. The apparatus includes a dilator portion including a dilator including a dilator length. The dilator portion is configured to separate tissue to create a subcutaneous pocket within the patient sized and shaped to accommodate an implantable device within the subcutaneous pocket. A sheath portion includes a sheath sized and shaped to accommodate the dilator within a sheath lumen. The sheath is configured to accommodate an antenna of the implantable device with the dilator removed from within the sheath. The sheath includes a sheath length that is at least substantially as long as an antenna length. The sheath is configured to separate to allow removal of the sheath around the implantable device to remove the sheath from and leave the implantable device within the subcutaneous pocket within the patient.

Abstract: A method of non-invasively monitoring advanced glycation end-product (AGE) concentrations includes providing incident light to patient tissue at one or more excitation wavelengths and monitoring the one or more emission responses at one or more emission wavelengths. Based on the emission responses monitored, a ratio is calculated based on a ratio of the first emission response to the second emission response.

Abstract: A method of non-invasively monitoring hemoglobin concentration includes providing incident light to patient tissue at a first excitation wavelength. The method further includes monitoring a first emission response at a first emission wavelength, wherein the first emission wavelength is selected to correspond with a maximum of the emission response, and monitoring a second emission response at a second emission wavelength, wherein the second emission wavelength is selected to correspond with a minimum of the emission response. A hemoglobin concentration is calculated based on a ratio of the first emission response to the second emission response.

Abstract: In some examples, a medical system includes a medical device. The medical device may include a housing configured to be implanted in a target site of a patient, a light emitter configured to emit a signal configured to cause a fluorescent marker to emit a fluoresced signal into the target site, and a light detector that may be configured to detect the fluoresced signal. The medical system may include processing circuitry configured to determine a characteristic of the fluorescent marker based on the emitted signal and the fluoresced signal. The characteristic of the fluorescent marker may be indicative of a presence of a compound in the patient, and the processing circuitry may be configured to track the presence of the compound of the patient based on the characteristic of the fluorescent marker.

Abstract: An apparatus, system, and method directed to detecting a physiological signal during discrete time separated detection windows, deriving one or more respiratory disturbance indices from the physiological signal, detecting a respiratory disturbance state in response to the one or more respiratory disturbance indices deviating from a threshold value, interpolating the one or more respiratory disturbance indices between adjacent time separated detection windows, and declaring a respiratory disturbance episode based on the detected respiratory disturbance state during the detection windows and the interpolation between detection windows.

Abstract: Embodiments relate to a method of monitoring physiological parameters of a patient with renal dysfunction. The method includes electrically connecting one or more medical device electrodes with a measurement site of a patient, generating one or more first stimulation signals sufficient to provide input physiological parameters specific to the patient, measuring one or more first bioimpedance values from the generated signals, analyzing at least one of the input physiological parameters within the one or more first bioimpedance values and generating a personalized dialysis program. The systems and methods can further provide essentially real-time data of patient undergoing treatment and control of treatment to a patient.

Abstract: Techniques are disclosed for explaining and visualizing an output of a machine learning system that detects cardiac arrhythmia in a patient. In one example, a computing device receives cardiac electrogram data sensed by a medical device. The computing device applies a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia has occurred in the patient and a level of confidence in the determination that the episode of arrhythmia has occurred in the patient. In response to determining that the level of confidence is greater than a predetermined threshold, the computing device displays, to a user, a portion of the cardiac electrogram data, an indication that the episode of arrhythmia has occurred, and an indication of the level of confidence that the episode of arrhythmia has occurred.

Abstract: Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrhythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrhythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrhythmia.

Abstract: Techniques for remote monitoring of a patient and corresponding medical device(s) are described. The remote monitoring comprises providing an interactive session configured to allow a user to navigate a plurality of sub sessions, determining a first set of data items in accordance with a first subsession, the first set of data items including the image data, determining a second set of data items in accordance with a second subsession of the interactive session, determining, based at least in part on the first set of data items and the second set of data items, an abnormality, and outputting a post-implant report of the interactive session.

Abstract: Techniques that include applying machine learning models to episode data, including a cardiac electrogram, stored by a medical device are disclosed. In some examples, based on the application of one or more machine learning models to the episode data, processing circuitry derives, for each of a plurality of arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over a period of time associated with the episode. The processing circuitry may display a graph of the varying likelihoods of the arrhythmia type classifications over the period of time. In some examples, processing circuitry may use arrhythmia type likelihoods and depolarization likelihoods to identify depolarizations, e.g., QRS complexes, during the episode.

Abstract: Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.

Abstract: Techniques are disclosed for using a computing system to selectively implement different review workflows for different categories of episodes, e.g., arrhythmia episodes, stored by medical devices. The different workflows may include different combinations of one or more human and/or machine reviewers, and different decision logic for determining whether and when to present an episode to the reviewers. Machine reviewers may utilize one or more machine learning models to annotate, e.g., classify, episodes.

Abstract: Techniques are disclosed for monitoring a patient for the occurrence of a cardiac arrhythmia. A computing system generates sample probability values by applying a machine learning model to sample patient data. The machine learning model determines a respective probability value that indicates a probability that the cardiac arrhythmia occurred during each respective temporal window. The computing system outputs a user interface comprising graphical data based on the sample probability values and receives, via the user interface, an indication of user input to select a probability threshold for a patient. The computing system receives patient data for the patient and applies the machine learning model to the patient data to determine a current probability value. In response to the determination that the current probability exceeds the probability threshold for the patient, the computing system generates an alert indicating the patient has likely experienced the occurrence of the cardiac arrhythmia.