Cardiac Pacemakers, Inc.

Other

Yanting Dong

Scott A Meyer

Kevin John Stalsberg

Deepa Mahajan

David L Perschbacher

Dan Li

Eric Keith Enrooth

Derek Daniel Bohn

Clayton Scott Foster

Sunipa Saha

John M Voegele

Shibaji Shome

Yinghong Yu

Holly Elizabeth Rockweiler

Jiang Ding

Aaron R McCabe

David W Yost

Kenneth N Hayes

Quan Ni

Shantha Arcot-Krishnamurthy

Jeffrey E Stahmann

Allan Charles Shuros

Qingsheng Zhu

Xuan K Wei

Amy Jean Brisben

Yayun Lin

Haresh G Sachanandani

Jeremy J Maniak

Doug Birholz

Shijie Zhang

Krzysztof Z Siejko

James O Gilkerson

Stephen John Hahn

Donald Lee Hopper

Douglas R Daum

Kenneth Paul Hoyme

Christopher L Pulliam

Gary Thomas Seim

Alok S Sathaye

Chenguang Liu

David L Whitehouse

Julie Stephenson

Yi Zhang

Kent Lee

Abhilash Patangay

M Jason Brooke

Shelley Marie Cazares

Arjun D Sharma

Ankur Garg

Michael J Stucky

LeAnne M Eberle

Benjamin Ettori

Theodore J Schulte

Scott Walczak

Qian Zhong

Vijay Aditya Tadipatri

Muhammad A Ahmad

Kevin John Stahlsberg

Michael John Stucky

Aaron Mccabe

Clayton Foster

Some method examples may include pacing a heart with cardiac paces, sensing a physiological signal for use in detecting pace-induced phrenic nerve stimulation, performing a baseline level determination process to identify a baseline level for the sensed physiological signal, and detecting pace-induced phrenic nerve stimulation using the sensed physiological signal and the calculated baseline level. Detecting pace-induced phrenic nerve stimulation may include sampling the sensed physiological signal during each of a plurality of cardiac cycles to provide sampled signals and calculating the baseline level for the physiological signal using the sampled signals. Sampling the sensed physiological signal may include sampling the signal during a time window defined using a pace time with each of the cardiac cycles to avoid cardiac components and phrenic nerve stimulation components in the sampled signal.

Baseline determination for phrenic nerve stimulation detection

Methods and devices for classifying a cardiac response to pacing involve establishing a retriggerable cardiac response classification window. A first cardiac response classification window is established subsequent to delivery of a pacing pulse. A cardiac signal following the pacing stimulation is sensed in the first classification window. A second cardiac response classification may be triggered if a trigger characteristic is detected in the first classification window. The cardiac signal is sensed in the second classification window if the second classification window is established. The cardiac response to the pacing stimulation is determined based on characteristics of the cardiac signal. The cardiac response may be determined to be one of a captured response, a non-captured response, a non-captured response added to an intrinsic beat, and a fusion/pseudofusion beat, for example.

Cardiac response classification using retriggerable classification windows

An arrhythmia classification system receives cardiac data from an implantable medical device, performs automatic adjudication of each cardiac arrhythmia episode indicated by the cardiac data, and generates episode data representative of information associated with the episode. The episode data include at least an episode classification resulting from the automatic adjudication of the episode and a confidence level in the episode classification. In one embodiment, the episode data further include key features rationalizing the automatic adjudication of the episode.

Method and apparatus for automatic arrhythmia classification with confidence estimation

An apparatus comprises an implantable cardiac signal sensing circuit configured to provide a sensed depolarization signal from a ventricle and a processor. The processor includes a signal analyzer module and a tachyarrhythmia discrimination module. The signal analyzer module is configured to determine a measure of stability of ventricular (V-V) depolarization intervals using the depolarization signal, and determine a rate of change of the measure of stability. The tachyarrhythmia discrimination module is configured to detect an episode of tachyarrhythmia using the depolarization signal, determine whether the detected tachyarrhythmia is indicative of atrial tachyarrhythmia using the determined rate of change, and provide the determination to a user or process.

Methods for detecting atrial tachyarrhythmia in implantable devices without dedicated atrial sensing

Monitoring physiological parameter using an implantable physiological monitor in order to detect a condition predictive of a possible future pathological episode and collecting additional physiological data associated with the condition predictive of a possible future pathological episode. Monitoring another physiological parameter in order to detect a condition indicative of the beginning of a present pathological episode and collecting additional pathological data in response to the condition. Determining that the condition predictive of a future episode and the condition indicative of a present episode are associated and, in response thereto, storing all the collected physiological data.

Adaptive event storage in a medical device

Various techniques are disclosed for quickly and efficiently determining cardiac pacing vectors that minimize phrenic nerve stimulation.

System and method for selection of pacing vectors

Adaptive event storage in implantable device

Approaches for rate initialization and overdrive pacing used during capture threshold testing are described. Cardiac cycles are detected and the cardiac events of a cardiac chamber that occur during the cardiac cycles are monitored. The number of intrinsic beats in the cardiac events is counted. Initialization for a capture threshold test involves maintaining a pre-test pacing rate for the capture threshold test if the number of intrinsic beats in the cardiac events is less than a threshold. The pacing rate is increased for the capture threshold test if the number of intrinsic beats in the cardiac events is greater than the threshold.

Rate initialization and overdrive pacing for capture threshold testing

In an example, a system includes a cardiac pulse generator configured to generate cardiac paces to pace the heart, a sensor configured to sense a physiological signal for use in detecting pace-induced phrenic nerve stimulation where the pace-induced phrenic nerve stimulation is phrenic nerve stimulation induced by electrical cardiac pace signals, and a phrenic nerve stimulation detector configured to analyze the sensed physiological signal to detect PS beats where the PS beats are cardiac paces that induce phrenic nerve stimulation. The detector may be configured to correlate signal data for sensed beat signals to a PS template to detect PS beats, or may be configured to analyze morphological features of sensed beat signals to detect PS beats, or may be configured to detect PS beats using a combination that both correlates signal data for sensed beat signals to a PS template and analyzes morphological features of sensed beat signals.

Phrenic nerve stimulation detection

An improved technique is described for dealing with the detection of fusion beats when capture verification is performed by a cardiac pacing device such as during a capture threshold determination procedure. Schemes for classifying heart beats may misclassify beats as fusion beats due to feature/morphology changes in the test electrogram waveform that may occur even when capture is achieved.

Management of fusion beat detection during capture threshold determination

Cardiac devices and methods discriminate non-captured intrinsic beats during evoked response detection and classification by comparing the features of a post-pace cardiac signal with expected features associated with a non-captured response with intrinsic activation. Detection of a non-captured response with intrinsic activation may be based on the peak amplitude and timing of the cardiac signal. The methods may be used to discriminate between a fusion or capture beat and a non-captured intrinsic beat. Discriminating between possible cardiac responses to the pacing pulse may be useful, for example, during automatic capture verification and/or a capture threshold test.

Non-captured intrinsic discrimination in cardiac pacing response classification

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Shoreview, MN, United States of America

Yanting Dong