Abstract: Techniques are described for initiating a change to rules used by a medical device to identify a plurality of episodes based on a determination that an amount of the plurality of episodes classified as a classification for which transmission of the episode data from the medical device to the computing device was unnecessary satisfies at least one criterion.

Abstract: A method comprises applying, by processing circuitry of a system comprising a medical device, an ensemble of classifiers to episode data for a ventricular tachyarrhythmia episode detected by the medical device based on electrocardiogram sensed by the medical device. The method further comprises classifying, by the processing circuitry, the ventricular tachyarrhythmia episode as one of a plurality of classifications based on the application of the ensemble of classifiers to the episode data, wherein the plurality of classifications include two or more of noise, oversensing, supraventricular tachycardia, polymorphic ventricular tachycardia, monomorphic ventricular tachycardia, and ventricular fibrillation.

Abstract: Devices, systems, and techniques are disclosed for determining the likelihood that a cardiac event will self-terminate. An example technique includes determining, by processing circuitry and based on current sensed physiological parameters of a patient, that a cardiac event is occurring in the patient. The example technique includes determining, by the processing circuitry- and based on the current sensed physiological parameters of the patient, that the cardiac event is unlikely to self-terminate within a predetermined period of time. The example technique includes, in response to determining that the cardiac event is unlikely- to self-terminate, deliver therapy to the patient or issue an alert.

Abstract: A medical device may be configured to determine heart rates of the patient based on the cardiac signal sensed during a first period. The medical device may detect one or more sleep apnea episodes of the patient occurring during the first period. The medical device may determine whether one or more verification conditions are satisfied. Responsive to determining that the one or more verification conditions are satisfied, the medical device may measure impedances of the patient during a second period subsequent to the first period, and use impedance to the measured impedances to detect one or more sleep apnea episodes of the patient occurring during the second period.

Abstract: An example system includes an implantable medical device (IMD), processing circuitry, and communication circuitry. The IMD is configured to receive signals from sensors, determine a plurality of detected patient metrics based on the received signals, and store the plurality of determined patient metrics in the memory. The processing circuitry is to generate higher resolution diagnostic information based on the plurality of patient metrics during an initiated period of time, wherein the initiated period of time is initiated based on an intervention being provided to the patient and/or receiving a user input. The higher resolution diagnostic information is of at least one of the plurality of patient metrics and indicative of heart recovery. The communication circuitry is configured to transmit the higher resolution diagnostic information to indicate a degree of recovery for patient metrics indicative of heart recovery.

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: This disclosure is directed to techniques for identifying false detection of pause-triggered episode in cardiac ECG data. In some examples, a medical system is configured to receive a cardiac electrogram of a pause-triggered episode, the cardiac electrogram sensed by a medical device via a plurality of electrodes, determine whether one or more of false pause detection criteria are satisfied based on the cardiac electrogram, wherein the one or more of false pause detection criteria comprise: at least one criterion for relative flatness of amplitude values of the cardiac electrogram in a time interval between a last pre-pause beat and a pause detection time, classify the pause-triggered episode as one of a plurality of classifications based on the determination of whether the false pause detection criterion is satisfied, and output an indication of the classification of the pause-triggered episode to a user display.

Abstract: In some examples, an IMD provides CRT data that indicates an amount of time that CRT was not delivered by the IMD, such as the % CRT. In some examples, a CRT loss diagnosis module apportions the amount of time that CRT was not delivered amongst predetermined reasons for loss of CRT based on the CRT data and sensed cardiac data from the IMD. An external computing device may present a representation of the apportionment to a user, e.g., a clinician. The external computing device may also analyze the apportionment, and recommend programming changes for the delivery of CRT by the IMD based on the analysis.

Abstract: Cardiac resynchronization therapy (CRT) performance data for a number of patients in which an implantable medical device (IMD) is implanted is aggregated and reports of the aggregated data are generated, e.g., for review by organizations or individual clinicians treating the patients. In one example, a method includes collecting CRT performance data correlated to cardiac rhythm event data for a first group of patients in which an IMD configured to deliver CRT is implanted, aggregating, with a computing device, the CRT performance data correlated to the cardiac rhythm event data for a second group of patients from among the first group of patients, and generating, with the computing device, a report comprising the aggregation of the CRT performance data correlated to the cardiac rhythm event data for the second group of patients.

Abstract: Cardiac resynchronization therapy (CRT) performance data for a number of patients in which an implantable medical device (IMD) is implanted is aggregated and reports of the aggregated data are generated, e.g., for review by organizations or individual clinicians treating the patients. In one example, a method includes collecting CRT performance data correlated to cardiac rhythm event data for a first group of patients in which an IMD configured to deliver CRT is implanted, aggregating, with a computing device, the CRT performance data correlated to the cardiac rhythm event data for a second group of patients from among the first group of patients, and generating, with the computing device, a report comprising the aggregation of the CRT performance data correlated to the cardiac rhythm event data for the second group of patients.

Abstract: In some examples, an IMD provides CRT data that indicates an amount of time that CRT was not delivered by the IMD, such as the % CRT. In some examples, a CRT loss diagnosis module apportions the amount of time that CRT was not delivered amongst predetermined reasons for loss of CRT based on the CRT data and sensed cardiac data from the IMD. An external computing device may present a representation of the apportionment to a user, e.g., a clinician. The external computing device may also analyze the apportionment, and recommend programming changes for the delivery of CRT by the IMD based on the analysis.