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.

An implantable medical device which performs the following steps during operation: a) performing a detection of whether the implantable medical device is in an implanted state; b) if it is detected that the implantable medical device is in an implanted state, activating a first diagnostic or therapeutic function of the implantable medical device, and subsequently activating a second diagnostic or therapeutic function of the implantable medical device, wherein the second diagnostic or therapeutic function is activated only after the fulfillment of at least one activation criterion selected from the group consisting of an elapse of a first time period from the activation of the first diagnostic or therapeutic function, an elapse of a second time period from the detection that the implantable medical device is in an implanted state, and a passing of a function test.

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.

Systems and methods for managing machine-generated alert notifications of medical events detected from one or more patients are described herein. An embodiment of a data management system may receive an adjudication of a medical event episode including an episode characterization. A storage unit stores an association between one or more episode characterizations and corresponding detection algorithms for detecting a medical event having respective episode characterizations. An episode management circuit may detect from a subsequent episode, using the stored association, a medical event having an episode characterization of at least one medical event episode presented for adjudication, and schedule presenting at least a portion of the subsequent episode based on the detection.

Systems and methods for managing machine-generated alert notifications of medical events detected from one or more patients are described herein. An embodiment of a data management system may receive an adjudication of a medical event episode including an episode characterization. A storage unit stores an association between one or more episode characterizations and corresponding detection algorithms for detecting a medical event having respective episode characterizations. An episode management circuit may detect from a subsequent episode, using the stored association, a medical event having an episode characterization of at least one medical event episode presented for adjudication, and schedule presenting at least a portion of the subsequent episode based on the detection.

An extra-cardiovascular implantable cardioverter defibrillator (ICD) having a low voltage therapy module and a high voltage therapy module is configured to select, by a control module of the ICD, a pacing output configuration from at least a low-voltage pacing output configuration of the low voltage therapy module and a high-voltage pacing output configuration of the high voltage therapy module. The high voltage therapy module includes a high voltage capacitor having a first capacitance and the low voltage therapy module includes a plurality of low voltage capacitors each having up to a second capacitance that is less than the first capacitance. The ICD control module controls a respective one of the low voltage therapy module or the high voltage therapy module to deliver extra-cardiovascular pacing pulses in the selected pacing output configuration via extra-cardiovascular electrodes coupled to the ICD.

This disclosure is directed to a curvilinear medical electrical lead. For example, a medical electrical lead includes a lead body, a high voltage electrode positioned on the lead body, the high voltage electrode comprising a proximal coated portion, a distal coated portion, and an uncoated portion. Additionally, the medical electrical lead includes a first low voltage electrode and a second low voltage electrode distal to the first low voltage electrode, wherein a first line passes through the first low voltage electrode and the second low voltage electrode, wherein a second line passes through the first low voltage electrode and the uncoated portion, the second line forming a first angle with the first line, and wherein a third line passes through the second low voltage electrode and the uncoated portion, the third line forming a second angle with the first line.

A medical device is configured to sense first ventricular event signals from a first cardiac electrical signal and sense second ventricular event signals from a second cardiac electrical signal. The medical device is configured to determine sensed event data in response to the first ventricular event signals and the second ventricular event signals. The medical device may select one of the first cardiac electrical signal or the second cardiac electrical signal for providing input for tachyarrhythmia detection based on the sensed event data.

A quantum cardiac electrophysiology device comprising an array of consumable half-ferromagnetic active electrodes connected to an array of semiconductor of half-ferromagnetic selector switches over an array of half-ferromagnetic resistors to a neutral charges out of the heart, by casting and/or inking the arrhythmic substrate of an arrhythmia by the electrophysiology quantum entan- glement of said arrhythmic substrate.

A quantum spin liquid (QSL) electrophysiology device comprising a spontaneous and an induced quantum arrhythmia vacuum states, switchable between them through at least one entangled measurement of one negative differential resistance.