Abstract: A method of determining signal quality in a patient monitoring device includes acquiring one or more signals using the patient monitoring device. One or more signal quality metrics are determined based on the one or more acquired signals. A noise condition is detected based on the one or more signal quality metrics, and a determination is made whether the noise condition should be classified as intermittent or persistent. One or more actions are taken based on the classification of detected noise as intermittent or persistent.

Abstract: Techniques are disclosed for detecting arrhythmia episodes for a patient. A medical device may receive one or more sensor values indicative of motion of a patient. The medical device may determine, based at least in part on the one or more sensor values, an activity level of the patient. The medical device may determine a heart rate threshold for triggering detection of an arrhythmia episode based at least in part on the activity level of the patient. The medical device may determine whether to trigger detection of the arrhythmia episode for the patient based at least in part on comparing a heart rate of the patient with the heart rate threshold. The medical device may, in response to triggering detection of the arrhythmia episode, collect information associated with the arrhythmia episode.

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 determining identification data and identifying implantable medical device (IMD) information, initiating an imaging device and determining an imaging program, receiving one or more frames of image data including image(s) of an implantation site, identifying an abnormality at the implantation site, triggering a supplemental image capture mode, receiving one or more supplemental images of the implantation site, and outputting the one or more supplemental images of the implantation site.

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: 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: Techniques for remote monitoring of a patient and corresponding medical device(s) are described. The remote monitoring comprises determining identification data and identifying implantable medical device (IMD) information, initiating an imaging device and determining an imaging program, receiving one or more frames of image data including image(s) of an implantation site, identifying an abnormality at the implantation site, triggering a supplemental image capture mode, receiving one or more supplemental images of the implantation site, and outputting the one or more supplemental images of the implantation site.

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.

Abstract: Techniques are disclosed for monitoring a patient for the occurrence of cardiac arrhythmias. A computing system obtains a cardiac electrogram (EGM) strip for a current patient. Additionally, the computing system may apply a first cardiac rhythm classifier (CRC) with a segment of the cardiac EGM strip as input. The first CRC is trained on training cardiac EGM strips from a first population. The first CRC generates first data regarding an aspect of a cardiac rhythm of the current patient. The computing system may also apply a second CRC with the segment of the cardiac EGM strip as input. The second CRC is trained on training cardiac EGM strips from a smaller, second population. The second CRC generates second data regarding the aspect of the cardiac rhythm of the current patient. The computing system may generate output data based on the first and/or second data.

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: Techniques are disclosed for monitoring a patient for the occurrence of cardiac arrhythmias. A computing system obtains a cardiac electrogram (EGM) strip for a current patient. Additionally, the computing system may apply a first cardiac rhythm classifier (CRC) with a segment of the cardiac EGM strip as input. The first CRC is trained on training cardiac EGM strips from a first population. The first CRC generates first data regarding an aspect of a cardiac rhythm of the current patient. The computing system may also apply a second CRC with the segment of the cardiac EGM strip as input. The second CRC is trained on training cardiac EGM strips from a smaller, second population. The second CRC generates second data regarding the aspect of the cardiac rhythm of the current patient. The computing system may generate output data based on the first and/or second data.

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: In some examples, a computing device may receive diagnostic data of a medical device implanted in a patient. The computing device may determine a use case associated with analyzing the diagnostic data out of a plurality of use cases for analyzing the diagnostic data. The computing device may determine, based at least in part on the use case, one or more device characteristics data to be compared against the diagnostic data. The computing device may analyze, based at least in part on comparing the diagnostic data with the one or more device characteristics data, the diagnostic data to determine an operating status of the medical device.

Abstract: A method of determining signal quality in a patient monitoring device includes acquiring one or more signals using the patient monitoring device. One or more signal quality metrics are determined based on the one or more acquired signals. A noise condition is detected based on the one or more signal quality metrics, and a determination is made whether the noise condition should be classified as intermittent or persistent. One or more actions are taken based on the classification of detected noise as intermittent or persistent.

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: A method of determining signal quality in a patient monitoring device includes acquiring one or more signals using the patient monitoring device. One or more signal quality metrics are determined based on the one or more acquired signals. A noise condition is detected based on the one or more signal quality metrics, and a determination is made whether the noise condition should be classified as intermittent or persistent. One or more actions are taken based on the classification of detected noise as intermittent or persistent.

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 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: 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: A medical device is utilized to monitor physiological parameters of a patient and capture segments of the monitored physiological parameters. The medical device includes circuitry configured to monitor one or more physiological parameters associated with the patient and an analysis module that includes a buffer and a processor. The buffer stores monitored physiological parameters and the processor analyzes the monitored physiological parameters and triggers capture of segments from the buffer in response to a triggering criteria being satisfied. The analysis module selects a pre-trigger duration based at least in part on the triggering criteria.