Medtronic, Inc.

Whoop, Inc.

David E Ritscher

Shantanu Sarkar

Douglas A Hettrick

Eduardo N Warman

Rahul Mehra

Mark L Brown

Behnoosh Tavakoli

Mostafa Ghannad-Rezaie

Jeffrey M Gillberg

Karen J Kleckner

Paul Gordon Krause

Michael F Hess

Michael R Ujhelyi

Kevin T Ousdigian

Michael T Hemming

Paul D Ziegler

David E Euler

Katherine H Anderson

Steven N Lu

Nirav Vijay Sheth

Cameron J Kaszas

Shailesh Kumar V Musley

Anne M Gillis

Charles E Distad

Steven Ning Lu

Jeffrey M Gillberg Gillberg

A physiological monitoring device controls an optical signal acquisition system within a number of discrete operating states, each providing values for controllable parameters such as illumination intensity for a light source and the gain for an optical detector. Using this technique, a small number of operating states may be defined, such as operating states that are known to work well within expected use scenarios. This approach advantageously facilitates optimal or near optimal operation across a range of most likely use cases while avoiding complex or continuous optimization problems. The list of operating states may further be prioritized so that a best operating state can be selected based on, e.g., signal quality or environmental conditions.

Variable brightness and gain for optimizing signal acquisition

A physiological signal such as a heart rate acquired from a monitoring device is processed to reduce interference, ambiguity, or artifacts arising during various activities. For example, the system can process a physiological signal to account for motion artifacts in the physiological signal and, thus, reduce the impact of movement on the physiological signal. Additionally, or alternatively, the system can process a physiological signal based on one or more measurement contexts associated with a wearable device. In general, the physiological signal processed as described herein can be useful as a reliable, continuous indication of a physiological parameter and, thus, can serve as the basis for other physiological assessments (e.g., heart rate variability) derived from the physiological parameter.

Probability-based usage of multiple estimators of a physiological signal

An implantable medical device and associated method classify therapy outcomes and heart rhythms in association with therapy outcome. A therapy success time interval is started in response to delivering an arrhythmia therapy. If normal sinus rhythm is detected after the therapy success time interval expires, the delivered therapy is classified as unsuccessful and the detected arrhythmia is classified as a self-terminating rhythm.

Time based arrhythmia therapy efficacy criteria

An implantable medical device and associated method monitor a physiological signal for sensing physiological events and detecting a physiological condition in response to the sensed physiological events. The device senses a first event from the physiological signal, senses a noise signal in the physiological signal and senses a next event from the physiological signal wherein the first event and the next event define a signal interval. The signal interval is declared as a noisy interval in response to the sensed noise signal.

Method and apparatus for detecting noise in an implantable medical device

A method and apparatus for detecting atrial arrhythmias and discriminating atrial fibrillation (AF) and organized atrial tachycardia (OAT) that includes defining a threshold detection criteria for a cluster signature evidence metric corresponding to a Lorenz distribution of ventricular cycle lengths representative of AF or OAT. Using a signal containing VCL information, a number of consecutive ventricular cycle lengths are determined during a selected time interval for generating a one-dimensional or a two-dimensional histogram as a numerical representation of a Lorenz plot of VCLs. A number of cluster signature metrics are computed using the stored ventricular cycle length information, and a cluster signature evidence metric is computed from the cluster signature metrics. AF or OAT is detected if a comparative analysis of a corresponding cluster signature evidence metric meets a respective threshold detection criteria.

Method and apparatus for detection of tachyarrhythmia using cycle lengths

A multi-layer method for detecting atrial arrhythmias using ventricular cycle length information that includes performing a base layer algorithm for detecting the onset and offset of an atrial tachyarrhythmia. The multi-layer method further includes one or more higher layer algorithms executed in response to a base layer detection to confirm or reject the base layer detection. The base layer is designed to operate with high sensitivity to atrial fibrillation and/or organized atrial tachycardia and the higher layer is designed to operate with high sensitivity and high specificity to atrial fibrillation and/or organized atrial tachycardia.

A system and method are provided for controlling atrial anti-tachycardia pacing (ATP) delivery based on detection of ventricular pro-arrhythmia during or immediately after atrial ATP. Ventricular pro-arrhythmia is detected based on one or more criteria relating to pro-arrhythmic changes including, but not limited to, ventricular rate changes, R-wave morphology changes, and/or 1:1 or nearly 1:1 atrial-ventricular conduction patterns persisting at high ventricular rates. Upon detecting ventricular pro-arrhythmia, a current atrial ATP sequence is aborted. Atrial ATP therapies may subsequently be temporarily or permanently disabled.

Method for elimination of ventricular pro-arrhythmic effect caused by atrial therapy

Control of defibrillation therapy delivered by implantable medical devices (IMDs) using hemodynamic sensor feedback is disclosed. The hemodynamic sensor feedback allows for increased control over application of atrial defibrillation therapy. Specifically, the therapy is delivered when a fibrillation episode results in a discrete loss of hemodynamic function. Defibrillation therapy is thus withheld for hemodynamically benign arrhythmias.

Control of atrial defibrillation therapy based on hemodynamic sensor feedback

In general, the invention is directed towards techniques for adaptively prioritizing cardiac episode data in a memory of an implanted medical device (IMD). More specifically, the IMD receives new cardiac episode data, assigns each piece of data a priority value, and stores the data in a memory of the IMD. The IMD can further recalculate initial priority values assigned to stored cardiac episode data in response to subsequent cardiac episode data. In this manner, the prioritization scheme used by the IMD is adaptive, i.e., changes as more contextual information regarding the cardiac episode and subsequent cardiac episodes becomes available. Upon exceeding a memory capacity threshold, the IMD identifies the stored cardiac episode data with a lowest priority from the hierarchical priority relationship, and overwrites the identified portion of the stored cardiac episode data with the new cardiac episode data.

Adaptive memory prioritization for implanted medical devices

Detection of arrhythmias is facilitated using irregularity of ventricular beats measured by delta-RR (&#x394;RR) intervals that exhibit discriminatory signatures when plotted in a Lorenz scatter-plot. An 'AF signature metric' is established characteristic of episodes of AF that exhibit highly scattered (sparse) distributions or formations of 2-D data points. An 'AFL signature metric' is established characteristic of episodes of AFL that exhibit a highly concentrated (clustered) distribution or formation of 2-D data points. A set of heart beat interval data is quantified to generate highly scattered (sparse) formations as a first discrimination metric and highly concentrated (clustered) distributions or formations as a second discrimination metric. The first discrimination metric is compared to the AF signature metric, and/or the second discrimination metric is compared to the AFL signature metric. AF or HFL is declared if the first discrimination metric satisfies either one of the AF signature metric.

Algorithms for detecting atrial arrhythmias from discriminatory signatures of ventricular cycle lengths

The invention relates to the use of atrial pacing therapies to treat atrial tachycardia (AT). When an AT episode is detected, an implantable medical device applies an ATP therapy. If the AT episode persists, the ATP therapy may be automatically reapplied at a later time during the course of the same AT episode. In particular, previously used ATP therapies are reapplied when episodic conditions, such as cycle length or cycle regularity, change. Although a particular ATP therapy initially may be unsuccessful in terminating the AT, it may prove successful when the cycle length or regularity of the atrial rhythm changes. As the rhythm slows down, the AT may be more responsive to ATP therapies that were previously unsuccessful. As a result, potentially efficacious ATP therapies can be reapplied to terminate AT episodes, and reduce the number of episodes that require more aggressive termination by painful, atrial shocks.

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Minneapolis, MN, United States of America

David E Ritscher