Abstract: An example system includes a first lead configured to be positioned in or beside a left internal jugular vein (IJV) of a patient to deliver a first stimulation signal to a first vagus nerve, the first lead including one or more first segmented electrodes positioned on a distal portion of the first lead and a first anchoring mechanism; a second lead configured to be positioned in or beside a right IJV of the patient to deliver a second stimulation signal to a second vagus nerve, the second lead including one or more second segmented electrodes positioned on a distal portion of the second lead and a second anchoring mechanism; and circuitry configured to deliver electrical energy to the first lead to deliver the first stimulation signal and the second lead to deliver the second stimulation signal to provide bilateral stimulation to the first vagus nerve and the second vagus nerve.

Abstract: An example implantable medical device includes a stimulating lead includes receive one or more signals indicative of one or more physiologic parameters; deliver electrical therapy to stimulate a muscle wrapped around a heart via one or more electrodes of a stimulating lead; and adjust an amount of the electrical therapy delivered, via the stimulating electrodes, based on the one or more physiologic parameters.

Abstract: A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

Abstract: A system may measure, by one or more sensors, a biometric parameter associated with a subject. The system may determine values of a control parameter based on measuring the biometric parameter. The control parameter may include blood pressure of the subject. The system may perform a control measure based on a comparison of the values of the control parameters to a threshold. Performing the control measure may include delivering therapy treatment to the subject or outputting a notification indicating an action associated with treating a medical condition. Measuring the biometric parameter, determining the values of the control parameter, and performing the control measure may be in response to one or more trigger criteria.

Abstract: Novel tools and techniques are provided for presenting patient information to a user. In some embodiments, a computer system may: receive device data associated with one or more devices configured to perform a cardiac shunting procedure to change a cardiac blood flow pattern to improve cardiac blood flow efficiency or cardiac pumping efficiency; receive one or more imaging data associated with one or more imaging devices configured to generate images of one or more internal portions of the patient; analyze the device data and the imaging data; map the device data and the imaging data to a multi-dimensional representation of the one or more internal portions of the patient; generate one or more image-based outputs based at least in part on the mapping; and present, using a user experience (“UX”) device, the generated one or more image-based outputs.

Abstract: A system includes harvester circuitry configured to charge a battery for a medical device using a displacement of a harvester mass, one or more accelerometers configured to detect a motion associated with the harvester mass, and processing circuitry. The processing circuitry is configured to determine, with the one or more accelerometers, motion information for the implanted medical device during a time range that occurs when the harvester circuitry charges the battery using the displacement of the harvester mass. The processing circuitry are further configured to determine a harvester output generated by the harvester circuitry during the time range and output an indication of a potential failure of the harvester mechanism based on the motion information and the harvester output.

Abstract: A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

Abstract: A medical system includes an implantable cardiac monitoring device (ICMD) configured to monitor one or more physiological signals of a patient and in response to detecting a current or imminent arrhythmia in the patient, transmit first data to an external user device. The external user device is configured to: in response to receiving the first data from the ICMD, outputting a notification of the current or imminent arrythmia; confirm the presence of the current or imminent arrhythmia in the patient; and in response to confirming the current or imminent arrhythmia in the patient, cause an external defibrillator device to deliver a shock to the patient.

Abstract: Disclosed herein are techniques for implementing an intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem for soft tissue luminal applications. In various embodiments, a computing system analyzes first layer input data (indicating movement, position, and/or relative distance for a person(s) and object(s) in a room) and second layer input data. The second layer input data includes sensor and/or imaging data of a patient. Based on the analysis, the computing system generates one or more recommendations for guiding a medical professional in navigating a surgical device(s) with respect to one or more soft tissue luminal portions of the patient. The recommendation(s) include at least one mapped guide toward, in, and/or around the one or more soft tissue luminal portions. The mapped guide can include data corresponding to at least three dimensions, e.g., a 3D image/video. The computing system can present the recommendation(s) as image-based output, using a user experience device.

Abstract: A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

Abstract: A system comprises electrocardiogram sensing, glucose sensing circuitry, and processing circuitry. The sensing circuitry is configured to sense an electrocardiogram of a patient. The glucose sensing circuitry is configured to sense glucose levels of the patient. The processing circuitry configured to detect atrial fibrillation of the patient during a time unit based on the electrocardiogram of the patient, determine a first metric, wherein the first metric is associated with atrial fibrillation the patient experiences during the time unit, determine a second metric, wherein the second metric is associated with glucose levels of the patient during the time unit, and generate a health metric, wherein the health metric is determined based on the first and second metrics.

Abstract: A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

Abstract: A system comprises a sensor device and processing circuitry. The sensor device comprises a housing configured to be disposed above shoulders of a patient, a plurality of electrodes on the housing, a motion sensor, and sensing circuitry configured to sense a brain electrical signal and a cardiac electrical signal via the electrodes, and a motion signal via the motion sensor. The processing circuitry is configured to determine values over time of one or more parameters from the brain electrical signal, determine values over time of one or more parameters from the cardiac electrical signal, and generate at least one of a detection, prediction, or a classification a condition of the patient based on the values and the motion signal.

Abstract: In some examples, a device includes at least three electrodes a first pair of electrodes and a second pair of electrodes. The device also includes circuitry configured to generate a first cardiac signal based on a first differential signal received across the first pair, generate a first brain signal based on the first differential signal received across the first pair, generate a second cardiac signal based on a second differential signal received across the second pair, and generate a second brain signal based on the second differential signal received across the second pair. The circuitry is also configured to output a composite cardiac signal based on the first cardiac signal and the second cardiac signal and to output a composite brain signal based on the first brain signal and the second brain signal.

Abstract: An implantable medical device, such as a sensor for monitoring a selected internally detectable physiological parameter of a patient, is attached to a fixation assembly that is deployable within the patient to position and orient the sensor to enable it to perform its function. The fixation assembly is formed having at least one flexible asymmetric connector where each fixation member includes a plurality of loops, wherein a first loop of the plurality of loops has a maximum pitch that is different from a maximum pitch of a second loop of the plurality of loops. The attachment of the housing and the fixation assembly includes providing a tubular member that is welded to the housing and crimped over a section of the fixation assembly.

Abstract: An implantable medical device, such as a sensor for monitoring a selected internally detectable physiological parameter of a patient, is attached to a fixation assembly that is deployable within the patient to position and orient the sensor to enable it to perform its function. The fixation assembly is formed having at least one flexible asymmetric connector where each fixation member includes a plurality of loops, wherein a first loop of the plurality of loops has a maximum pitch that is different from a maximum pitch of a second loop of the plurality of loops. The attachment of the housing and the fixation assembly includes providing a tubular member that is welded to the housing and crimped over a section of the fixation assembly.

Abstract: In some examples, the disclosure describes an implantable medical device comprising a plurality of electrodes, sensing circuitry configured to sense a physiological electrical signal via the plurality of electrodes, and communication circuitry configured to transmit and/or receive a transconductance communication signal via the plurality of electrodes, wherein at least one electrode of the plurality of electrodes comprises a lower-capacitance portion and a higher-capacitance portion.

Abstract: A rechargeable cell of an implantable medical device is recharged by an external charging unit that includes a transmitting coil configured to emit an electromagnetic field and a near-field focusing plate having a subwavelength structure pattern that focuses the emitted electromagnetic field to a focal pattern. The implantable medical device includes a housing having a window of an electromagnetically transparent material and a receiving coil enclosed by the housing. The receiving coil extends adjacent to and is aligned with the window. The rechargeable cell is enclosed by the housing and is configured to be charged by current induced in the receiving coil when the receiving coil is exposed to the focal pattern. In some examples, the near-field focusing plate is configured to produce the focal pattern to have a size that is less than an outer dimension of the window and a focal length from the near-field focusing plate that at least reaches the window.

Abstract: The exemplary systems and methods may monitor one or more signals to be used to assess the hemodynamic status of a patient. The one or more signals may be used to calculate, or determine, a plurality of pulse transit times. The plurality of pulse transit times may be used to determine hemodynamic status values that may be indicative of a patient's aggregate hemodynamic status.

Abstract: In some examples, the disclosure describes an implantable medical device comprising a plurality of electrodes, sensing circuitry configured to sense a physiological electrical signal via the plurality of electrodes, and communication circuitry configured to transmit and/or receive a transconductance communication signal via the plurality of electrodes, wherein at least one electrode of the plurality of electrodes comprises a lower-capacitance portion and a higher-capacitance portion.