Cameron Health, Inc.

Cardiac Pacemakers, Inc.

Cameraon Health, Inc.

Venugopal Allavatam

Rick Sanghera

Jay A Warren

Surekha Palreddy

Stephen John Hahn

Krzysztof Z Siejko

Alan H Ostroff

Keith L Herrmann

Gust H Bardy

William J Rissmann

Paul J Erlinger

Amy Jean Brisben

Mark Schroeder

Deepa Mahajan

Kevin G Wika

Benjamin Speakman

Mitchell D Lanz

Paul Freer

James William Phillips

LeAnne M Eberle

Wearable, automatic external, and implantable defibrillators, as well as methods of operation in such systems, are disclosed with shock delivery mitigations to avoid delivering a defibrillation shock on a T-wave. Prior to issuance of a defibrillation shock, one or more detected cardiac events are analyzed to characterize a detected event that is sensed for purposes of synchronizing the defibrillation shock. The detected event can be characterized as an R-wave or a T-wave, and the shock delivery protocol is then selected based on the characterization of the detected event to avoid shock-on-T and potential pro-arrhythmia.

Cardiac beat classification to avoid delivering shock during ventricular repolarization

Methods and/or device facilitating and selecting among multiple modes of filtering a cardiac electrical signal, in which one filtering mode includes additional high pass filtering of low frequency signals, relative to the other filtering mode. The selection filtering modes may include comparing sensed signal amplitude to one or more thresholds, using the multiple modes of filtering. In another example, an additional high pass filter is enabled, over and above a default or baseline filtering mode, and the detected cardiac signal is monitored for indications of possible undersensing, and/or for drops in amplitude toward a threshold, and the additional high pass filter may be disabled upon finding of possible undersensing or drop in signal amplitude.

Automatic determination and selection of filtering in a cardiac rhythm management device

Signal analysis in an implanted cardiac monitoring and treatment device such as an implantable cardioverter defibrillator. In some examples, detected events are analyzed to identify changes in detected event amplitudes. When detected event amplitudes are dissimilar from one another, a first set of detection parameters may be invoked, and, when detected event amplitudes are similar to one another, a second set of detection parameters may be invoked. Additional examples determine whether the calculated heart rate is 'high' or 'low,' and then may select a third set of detection parameters for use when the calculated heart rate is high.

Accurate cardiac event detection in an implantable cardiac stimulus device

New and alternative approaches to the monitoring of cardiac signal quality for external and/or implantable cardiac devices. In one example, signal quality is monitored continuously or in response to a triggering event or condition and, upon identification of a reduction in signal quality, a device may reconfigure its sensing state. In another example, one or more trends of signal quality are monitored by a device, either continuously or in response to a triggering event or condition, and sensing reconfiguration may be performed in response to identified trends and events. In yet another example, a device may use a looping data capture mode to track sensing data in multiple vectors while primarily relying on less than all sensing vectors to make decisions and, in response to a triggering event or condition, the looped data can be analyzed automatically, without waiting for additional data capture to reconfigure sensing upon identification of the triggering event or condition. In another example a device calculates a composite cardiac cycle by overlaying signal morphology for a number of cardiac cycles and analyzes the composite cardiac cycle to calculate signal quality metrics.

Signal quality monitoring for multiple sense vectors in cardiac devices

Methods, systems, and devices for signal analysis in an implanted cardiac monitoring and treatment device such as an implantable cardioverter defibrillator. In illustrative examples, captured data including detected events is analyzed to identify likely overdetection of cardiac events. In some illustrative examples, when overdetection is identified, data may be modified to correct for overdetection, to reduce the impact of overdetection, or to ignore overdetected data. New methods for organizing the use of morphology and rate analysis in an overall architecture for rhythm classification and cardiac signal analysis are also discussed.

Methods and devices for accurately classifying cardiac activity

Methods and devices for cardiac therapy. One example provides a subcutaneous anti-tachycardia pacing therapy. Another example provides a subcutaneous low energy cardioversion therapy. Yet another example provides a subcutaneous multiple pulse cardioversion therapy. In various examples, specific steps are taken to ensure synchronization of delivered therapy when provided in response to sensing and analysis of a subcutaneous signal. Some examples use a substernal device instead.

Low energy conversion of ventricular tachycardia in a subcutaneous defibrillator

Implantable medical device systems and methods configured to use a detection profile selected from among a plurality of detection profiles to define a detection threshold for identifying cardiac events, in which a close call definition is used to determine which of the plurality of detection profiles is to be chosen. Upon identifying a close call, in which an overdetection nearly occurred but did not actually take place, a relatively less sensitive detection profile is chosen.

Use of detection profiles in an implantable medical device

Methods, systems, and devices for signal analysis in an implanted cardiac monitoring and treatment device such as an implantable cardioverter defibrillator. In some examples, captured data including detected events is analyzed to identify likely overdetection of cardiac events. In some illustrative examples, when overdetection is identified, data may be modified to correct for overdetection, to reduce the impact of overdetection, or to ignore overdetected data. Several examples emphasize the use of morphology analysis using correlation to static templates and/or inter-event correlation analysis.

Methods, systems, and devices for signal analysis in an implantable cardiac device such as an implantable cardioverter defibrillator. In illustrative examples, captured data including detected events is analyzed to identify likely overdetection of cardiac events. Analysis of the apparent width of detected events is used to determine whether overdetection is occurring. If overdetection is identified, data may be modified to correct for overdetection, to reduce the impact of overdetection, or to ignore overdetected data.

Methods and devices that identify overdetection in implantable cardiac systems

Methods and devices for sensing vector analysis in an implantable cardiac stimulus system. In an illustrative example, a first sensing vector is analyzed to determine whether it is suitable, within given threshold conditions, for use in cardiac event detection and analysis. If so, the first vector may be selected for detection and analysis. Otherwise, one or more additional vectors are analyzed. A detailed example illustrates methods for analyzing sensing vectors by the use of a scoring system. Devices adapted to perform these methods are also discussed, including implantable medical devices adapted to perform these methods, and systems comprising implantable medical devices and programmers adapted to communicate with implantable medical devices, the systems also being adapted to perform these methods. Another example includes a programmer configured to perform these methods including certain steps of directing operation of an associated implanted or implantable medical device.

Systems and methods for sensing vector selection in an implantable medical device

In some examples, cardiac cycle detection may be used as a more or less default approach to cardiac activity tracking. Additional rate measurement relying on different sources or analyses may require extra power consumption over the cycle detection methods. Therefore, new methods and devices are disclosed that selectively activate a second cardiac rate measurement when needed. In some illustrative methods and devices, decisions are made as to whether and which previously collected data, if any, is to be discarded, replaced, or corrected upon activation of the second cardiac rate measurement.

Methods and devices combining multiple cardiac rate measurements with activation and arrhythmia analysis correction

Methods and devices for combining multiple signals from multiple sensing vectors for use in wearable or implantable cardiac devices. Signals from multiple vectors may be combined using weighting factors and/or by conversion to different coordinate systems than the original inputs, which may or may not be normalized to patient anatomy. Signals from multiple sensing vectors may be combined prior to or after several analytical steps or processes including before or after filtering, and before or after cardiac cycle detection. Cardiac cycle detection information may be combined across multiple sensing vectors before or after analysis of individual vectors for noise or overdetection. Cardiac cycle detection information may also be combined across multiple sensing vectors to identify noise and/or overdetection.

Multi-vector sensing in cardiac devices with detection combinations

Methods and devices for combining multiple signals from multiple sensing vectors for use in wearable or implantable cardiac devices. A preferred sensing configuration may be selected at a given point in time, for example under clinical conditions. Signal quality for the preferred sensing configuration is then monitored, and if the signal quality degrades under selected conditions, re-analysis may be performed to select a different sensing vector configuration for at least temporary use. If signal quality increases for the preferred sensing configuration, temporary use of the different sensing vector configuration may cease and reversion to the preferred sensing configuration takes place if certain conditions are met. The conditions for reversion may depend in part of a history of sensing signal quality for the preferred sensing configuration.

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Maple Grove, MN, United States of America

Venugopal Allavatam