Abstract: An example device of a patient includes an antenna configured to wirelessly receive communication from a medical device; and processing circuitry coupled to the antenna and configured to: determine that the received communication indicates that a patient is experiencing an acute health event; in response to the determination, determine one or more physical states of the patient based on sensed data from one or more sensors; confirm that the patient is not experiencing the acute health event based on the determined one or more physical states; and output information based on the confirmation that the patient is not experiencing the acute health event.

Abstract: Techniques and devices for implementing the techniques for adjusting atrial arrhythmia detection based on analysis of one or more P-wave sensing windows associated with one or more R-waves. An implantable medical device may determine signal characteristics of the cardiac signal within the P-wave sensing window, determine whether the cardiac signal within the sensing window corresponds to a P-wave based on the determined signal characteristics, determine a signal to noise ratio of the cardiac signal within the sensing window, update the arrhythmia score when the P-wave is identified in the sensing window and the determined signal to noise ratio satisfies a signal to noise threshold.

Abstract: An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine a previous RR interval of the cardiac signal and a current RR interval of the cardiac signal based on the determined R-wave. The processing circuitry is further configured to determine a search window based on one or more of the current RR interval or the previous RR interval, determine a T-wave of the cardiac signal in the search window, and determine a QT interval based on the determined T-wave and the determined R-wave.

Abstract: A method is disclosed for performing extension mobility via a headset connection. The method includes receiving a headset identifier from a headset. Also, the method includes, in response to receiving the headset identifier, sending the headset identifier to a device management server. Further, the method includes receiving a call control user identifier from the device management server, and sending, to a call control server, the call control user identifier. Still yet, the method includes receiving, from the call control server, a configuration profile associated with the call control user identifier. One or more settings of a desk phone are configured using the configuration profile.

Abstract: This disclosure is directed to systems and techniques for detecting change in patient health and if a change in patient health is detected, direct a medical device to generate for display output indicating the detection of the change in patient health. An example medical system or technique applies a model to values of configurable settings that are programmed into detection logic of a medical device; based on the application, determine whether modified values of the configurable settings, when implemented by the detection logic, would change a determination, by the medical device, regarding whether sensed physiological activity is indicative of cardiac episode for a patient; and in response to a determination that the modified values would change the determination regarding whether the sensed physiological activity is indicative of the cardiac episode for the patient, generate output data indicative of the modified values for the configurable settings for the medical device.

Abstract: An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine whether the R-wave is noisy. Based on the R-wave being noisy, the processing circuitry is configured to determine whether the cardiac signal around a determined T-wave is noisy. Based on the cardiac signal around the determined T-wave not being noisy, the processing circuitry is configured to determine a QT interval or a corrected QT interval based on the determined T-wave and the determined R-wave.

Abstract: This disclosure is directed to a medical system and technique for a filter-based approach to arrhythmia detection. In one example, the medical system comprises one or more sensors configured to sense physiological parameter(s); sensing circuitry configured to generate patient data based on the sensed physiological parameter(s), the patient data comprising signal data to represent cardiac activity of the patient; and processing circuitry configured to: detect a cardiac arrhythmia for the patient based on a classification of the signal data in accordance with a machine learning model, wherein the machine learning model comprises filter(s) for at least one portion of the signal data, wherein the at least one filter corresponds to a feature set that maps to the cardiac activity represented by the portion(s) of the signal data; and generate for display output data indicative of a positive detection of the cardiac arrhythmia.

Abstract: This disclosure describes techniques for bypassing an algorithm configured to determine a likelihood of episode data being a false indication of a cardiac episode. A medical device system includes processing circuitry configured to receive episode data and determine, based on satisfaction of one or more bypass conditions of a set of bypass conditions, whether to bypass the algorithm. Responsive to bypassing the algorithm, the processing circuitry stores the episode data as a true indication of the cardiac episode.

Abstract: This disclosure is directed to systems and techniques for determining an evidence level of an atrial tachycardia (AT) episode based on heart beat intervals in the cardiac activity data over a pre-determined time period. Based on a determination that the evidence level indicates relatively regular heart beat intervals, the example techniques apply a first set of AT detection criteria and indicate a detection of an AT episode based on satisfaction of at least one of the first set of AT detection criteria. Based on a determination that the evidence level indicates relatively irregular heart beat intervals, the example techniques apply a second set of AT detection criteria and indicate a detection of an AT episode based on based on satisfaction of at least one of the second set of AT detection criteria.

Abstract: Techniques and devices for implementing the techniques for adjusting atrial arrhythmia detection based on analysis of one or more P-wave sensing windows associated with one or more R-waves. An implantable medical device may determine signal characteristics of the cardiac signal within the P-wave sensing window, determine whether the cardiac signal within the sensing window corresponds to a P-wave based on the determined signal characteristics, determine a signal to noise ratio of the cardiac signal within the sensing window, update the arrhythmia score when the P-wave is identified in the sensing window and the determined signal to noise ratio satisfies a signal to noise threshold.

Abstract: A medical system includes communication circuitry configured to receive episode data for an episode sensed by a medical device of a patient, wherein the episode data comprises a cardiac electrogram sensed by the medical device during a period of time; and processing circuitry configured to generate an image based on the episode data, wherein the image is associated with an interval within the period of time; apply, by the processing circuitry, one or more machine learning models to the image, the one or more machine learning models configured to determine whether the image corresponds to an arrythmia type; and output an indication of whether the image corresponds to the arrythmia type.

Abstract: A system comprises processing circuitry and memory comprising program instructions that, when executed by the processing circuitry, cause the processing circuitry to: apply a first set of rules to first patient parameter data for a first determination of whether sudden cardiac arrest of a patient is detected; determine that a one or more context criteria of the first determination are satisfied; and in response to satisfaction of the context criteria, apply a second set of rules to second patient parameter data for a second determination of whether sudden cardiac arrest of the patient is detected. At least the second set of rules comprises a machine learning model, and the second patient parameter data comprises at least one patient parameter that is not included in the first patient parameter data.

Abstract: This disclosure is directed to systems and techniques for detecting change in patient health based upon patient data. In one example, a medical system comprising processing circuitry communicably coupled to a glucose sensor and configured to generate continuous glucose sensor measurements of a patient. The processing circuitry is further configured to: extract at least one feature from the continuous glucose sensor measurements over at least one time period, wherein the at least one feature comprises one or more of an amount of time within a pre-determined glucose level range, a number of hypoglycemia events, a number of hyperglycemia events, or one or more statistical metrics corresponding to the continuous glucose sensor measurements; apply a machine learning model to the at least one extracted feature to produce data indicative of a risk of a cardiovascular event; and generate output data based on the risk of the cardiovascular event.

Abstract: In some examples, determining a heart failure status includes using an implantable medical device configured for subcutaneous implantation and comprising a plurality of electrodes and an optical sensor. Processing circuitry of a system comprising the device may determine, for a patient, a current tissue oxygen saturation value based on a signal received from the at least one optical sensor, a current tissue impedance value based on a subcutaneous tissue impedance signal received from the electrodes, and a current pulse transit time value based on a cardiac electrogram signal received from the electrodes and at least one of the signal received from the optical sensor and the subcutaneous tissue impedance signal. The processing circuitry may further compare the current tissue oxygen saturation value, current tissue impedance value, and current pulse transit time value to corresponding baseline values, and determine the heart failure status of the patient based on the comparison.

Abstract: An example device of a patient includes an antenna configured to wirelessly receive communication from a medical device; and processing circuitry coupled to the antenna and configured to: determine that the received communication indicates that a patient is experiencing an acute health event; in response to the determination, determine one or more physical states of the patient based on sensed data from one or more sensors; confirm that the patient is not experiencing the acute health event based on the determined one or more physical states; and output information based on the confirmation that the patient is not experiencing the acute health event.

Abstract: This disclosure is directed to techniques for identifying a medical condition, such as an infection and/or a disease, from sensor data indicative of physiological parameters. In some examples, one example technique for identifying the medical condition includes process sensor data comprising data indicative of a plurality of physiological parameters for a patient comprising an impedance parameter, computing an index based upon values corresponding to at least two of the physiological parameters and based upon a comparison between the index and prediction criterion, generating, for display, output data corresponding to the comparison results, wherein the output data indicates a prediction of the medical condition in the patient if the comparison results indicate satisfaction of the prediction criterion.

Abstract: A method of monitoring a patient using a system includes a medical device, a peripheral device configured to wirelessly communicate with the medical device, and processing circuitry. The method includes, by the processing circuitry, receiving sensor data collected by the medical device and evaluating the sensor data. The method further includes, based on the evaluation of the sensor data, outputting for display via the peripheral device at least one question relating to the sensor data collected by the medical device for a patient to answer. The method further includes receiving at least one answer via the peripheral device and determining, based on a combination of the sensor data and the at least one answer, a risk-level of the patient's health associated with at least one condition such as at least one of infection, stroke, sepsis, chronic obstructive pulmonary disease, cardiac arrhythmia, or myocardial infarction.

Abstract: Techniques for using multiple physiological parameters to provide an early warning for worsening heart failure are described. A system is provided that monitors a multiple diagnostic parameters indicative of worsening heart failure. The parameters preferably include are least one parameter acquired from an implanted device, such as intrathoracic impedance. The system device derives an index of the likelihood of worsening heart failure based upon the parameters using a Bayesian approach and displays the resultant index for review by a physician.

Abstract: This disclosure is directed to techniques for detecting and mitigating inaccurate sensing in a medical system. In some examples, one or more sensors of the medical system may include at least one electrode configured to sense an impedance of a portion of a patient's body proximate to the electrode and processing circuitry of the medical system may detect an inaccuracy in the data corresponding to the one or more patient physiological parameters based upon data including at least the sensed impedance of the portion of the patient body; correct at least a portion of the inaccuracy in the data corresponding to the one or more patient physiological parameters; and generate, for display on a display device, output data indicating the inaccuracy in the data corresponding to the one or more patient physiological parameters.

Abstract: A system and method for detecting and verifying bradycardia/asystole episodes includes sensing an electrogram (EGM) signal. The EGM signal is compared to a primary threshold to sense events in the EGM signal, and at least one of a bradycardia or an asystole is detected based on the comparison. In response to detecting at least one of a bradycardia or an asystole, the EGM signal is compared to a secondary threshold to sense events under-sensed by the primary threshold. The validity of the bradycardia or the asystole is determined based on the detected under-sensed events.