Abstract: A medical device system senses cardiac signals and generates and stores sensing data including sensed cardiac events. A processor receiving the sensing data is configured to detect undersensed and oversensed events. The processor generates an episode display comprising event identifying codes in response to the received sensing data and produces an adjusted episode display in response to an event being identified as an undersensed event or an oversensed event.

Abstract: A medical device system senses cardiac signals and generates and stores sensing data including sensed cardiac events. A processor receiving the sensing data is configured to detect undersensed and oversensed events. The processor generates an episode display comprising event identifying codes in response to the received sensing data and produces an adjusted episode display in response to an event being identified as an undersensed event or an oversensed event.

Abstract: A method and system of post-processing of sensing data generated by a medical device that includes transmitting a plurality of stored sensing data generated by the medical device to an access device, the stored sensing data including sensed atrial events and sensed ventricular events. The access device determines, in response to the transmitted data, instances where the medical device identified a cardiac event being detected in response to the sensing data, and determines, in response to the transmitted sensing data, whether one of the sensed ventricular events and the sensed atrial events is initiating conduction of a heart associated with the stored sensing data.

Abstract: A system including a communication module, a processor and a medical device configured to sense cardiac signals and detect cardiac rhythm episodes is configured to retrieve stored episode data accumulated by the medical device and generate truthed episode classifications from the retrieved episode data. The processor is configured to perform a detection simulation for detecting and classifying cardiac rhythm episodes included in the retrieved episode data to obtain simulated episode classifications. Sensitivity and specificity data is generated in response to the detection simulation, and recommended detection parameter settings are identified in response to the sensitivity and specificity data.

Abstract: The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data collected by an implantable medical device. The episode classification algorithm may classify may include a sinus template and a comparison of the electrogram signal to the sinus template. Possible classifications of the cardiac episode may include, for example, unknown, inappropriate, appropriate, supraventricular tachycardia, ventricular tachycardia, ventricular fibrillation or ventricular over-sensing.

Abstract: The present disclosure is directed to generating and displaying an electrogram (EGM) summary for use by physicians or other clinicians. An implantable medical device (IMD) transmits EGM signal data for a number of cardiac episodes to an external computing device. The external computing device selects a subset of the cardiac episodes for which information or images are displayed to the user. In various examples, cardiac episodes may be selected for display based at least in part on a retrospective analysis classification of the cardiac episode.

Abstract: The present disclosure is directed to an electrogram summary. In various examples, a subset of cardiac episodes are selected and displayed based on a set of summary rules. The subset of cardiac episodes includes at least one episode from each of a plurality of episode categories with at least one cardiac episode. In some examples, the order in which the cardiac episodes selected are displayed is based on the set of summary rules. The electrogram summary may include images or information regarding each of the selected cardiac episodes.

Abstract: The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data from a near-field channel and a far-field channel. The episode classification algorithm classifies the cardiac episode based on the evaluation of the electrogram signal data for at least one of the near-field and far-field channels. In some examples, a cardiac episode being classified may be an episode that resulted in treatment being provided by an implantable medical device. Possible classifications of the cardiac episode may include, for example, unknown, inappropriate, appropriate, supraventricular tachycardia, ventricular tachycardia, ventricular fibrillation or ventricular over-sensing.

Abstract: The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data collected by an implantable medical device. The episode classification algorithm may classify may include a sinus template and a comparison of the electrogram signal to the sinus template. Possible classifications of the cardiac episode may include, for example, unknown, inappropriate, appropriate, supraventricular tachycardia, ventricular tachycardia, ventricular fibrillation or ventricular over-sensing.

Abstract: A method and system of post-processing of sensing data generated by a medical device that includes transmitting a plurality of stored sensing data generated by the medical device to an access device, the stored sensing data including sensed atrial events and sensed ventricular events. The access device determines, in response to the transmitted data, instances where the medical device identified a cardiac event being detected in response to the sensing data, and determines whether there is an abrupt onset of the cardiac event in response to the transmitted data.

Abstract: In general, the disclosure describes techniques for storing data corresponding to sensed high-rate non-sustained episodes that occur close in time to detection of a lead integrity condition. A method comprises detecting a first high-rate non-sustained episode, activating a data storage operation for storing data associated with high rate non-sustained episodes in response to detecting the first episode, and storing data associated with the first episode in an episode log in response to activating the data storage operation. Another method comprises detecting a lead integrity condition, and activating a data storage operation for storing data associated with high rate non-sustained episodes in response to detecting the condition.

Abstract: A method for identifying and classifying various types of oversensing in implantable medical devices (IMDs), such as implantable cardioverter defibrillators (ICDs), to assist a physician in choosing corrective action to reduce the likelihood of oversensing and inappropriate therapy delivery. Far-field electrogram (EGM) signals are analyzed to detect the occurrence of R-waves, and the result is compared to the number and pattern of R-waves sensed by the IMD and indicated on the marker channel. A marker channel with more sensed R-waves than indicated by analysis of the far-field EGM indicates the presence of oversensing, including double-counting of R-waves, T-wave oversensing, lead malfunction or failure, poor lead connections, noise associated with electromagnetic interference, non-cardiac myopotentials, etc.

Abstract: Techniques for determining whether a lead related condition exists based on analysis of a cardiac electrical signal associated with a non-sustained tachyarrhythmia (NST) are described. In some examples, the techniques include determining the duration of intervals between consecutive cardiac events, e.g., R-R intervals, during an NST. The techniques may further include determining one or more metrics based on the durations of the intervals during the NST. Examples of metrics include an average, a minimum, a maximum, a range, a median, a mode, or a mean. A lead related condition is identified based on the values of the one or more metrics, e.g., by comparison to respective thresholds. In some examples, an alert is provided or a therapy modification is suggested if a lead related condition is identified.

Abstract: Techniques for determining whether a lead related condition exists based on analysis of a cardiac electrical signal associated with a non-sustained tachyarrhythmia (NST) are described. In some examples, the techniques include determining the duration of intervals between consecutive cardiac events, e.g., R-R intervals, during an NST. The techniques may further include determining one or more metrics based on the durations of the intervals during the NST. Examples of metrics include an average, a minimum, a maximum, a range, a median, a mode, or a mean. A lead related condition is identified based on the values of the one or more metrics, e.g., by comparison to respective thresholds. In some examples, an alert is provided or a therapy modification is suggested if a lead related condition is identified.

Abstract: Techniques for diagnosing lead fractures and lead connection problems are described. One or more medical leads may be coupled to an implantable medical device (IMD) to position electrodes or other sensors at different locations within a patient than the IMD. The IMD may include a lead diagnostic module configured to diagnose problems with a coupled lead and automatically select between a lead fracture problem and a lead connection problem based on the diagnosis. The diagnosis of either lead fracture problems or lead connection problems may be based on a timing of an increased impedance value with respect to connection of the lead to the IMD, a return to baseline impedance values after the increased impedance value, an abrupt rise of the increased impedance value, maximum impedance values, or oversensing. An external device may present the diagnosis to a user to facilitate appropriate corrective action.

Abstract: Techniques for storing electrograms (EGMS) that are associated with sensed episodes or events that may be non-physiological and, instead, associated with a sensing integrity condition are described. In some examples, a device or system identifies suspected non-physiological NSTs, and stores an EGM for the suspected non-physiological NSTs within an episode log. In some examples, a device or system determines whether to store an EGM for a suspected non-physiological episode or event based on whether an impedance integrity criterion has been satisfied. For example, a device or system may store an EGM for a detected short interval if the impedance integrity criterion has been met. In some examples, a device or system determines whether to buffer EGM data based on whether an impedance integrity criterion or other sensing integrity criterion has been met.

Abstract: Various techniques for detecting cardiac capture in response to a detected lead related condition are described. One example method described includes delivering a pacing therapy to a heart of a patient, periodically determining whether the pacing therapy captures the heart of the patient, detecting a lead related condition, and, in response to the detected lead related condition, increasing a frequency of determining whether the pacing therapy captures the heart.

Abstract: Methods and/or devices for initiating an automatic adjustment of arrhythmia detection parameters (e.g., upon delivery of cardiac therapy after detection of VT/VF).

Abstract: In general, the disclosure is directed to techniques for identification and remediation of oversensed cardiac events using far-field electrograms (FFEGMs). Identification of oversensed cardiac events can be used in an ICD to prevent ventricular fibrillation (VF) detection, and thereby avoid delivery of an unnecessary defibrillation shock. Alternatively, or additionally, identification of oversensed cardiac events can be used in an ICD to support delivery of bradycardia pacing during an oversensing condition. In some cases, bradycardia pacing delivered in response to detection of oversensed cardiac events may include pacing pulses from multiple vectors to provide redundancy in the event the oversensing may be due to a lead-related condition.

Abstract: In general, the disclosure is directed to techniques for identification and remediation of oversensed cardiac events using far-field electrograms (FFEGMs). Identification of oversensed cardiac events can be used in an ICD to prevent ventricular fibrillation (VF) detection, and thereby avoid delivery of an unnecessary defibrillation shock. Alternatively, or additionally, identification of oversensed cardiac events can be used in an ICD to support delivery of bradycardia pacing during an oversensing condition. In some cases, bradycardia pacing delivered in response to detection of oversensed cardiac events may include pacing pulses from multiple vectors to provide redundancy in the event the oversensing may be due to a lead-related condition.