An implantable medical device and medical device system for delivering a bi-ventricular pacing therapy that includes a plurality of electrodes to sense a cardiac signal, an emitting device to emit a trigger signal to control delivery of the bi-ventricular pacing, and a processor configured to compare the sensed cardiac signal associated with the delivered bi-ventricular pacing to at least one of an intrinsic beat template and an RV template associated with a morphology of RV-only pacing therapy, determine whether an offset interval associated with the bi-ventricular pacing therapy is set to a maximum offset interval level in response to the comparing, adjust the offset interval in response to the offset interval not being set to the maximum offset interval level, and generate the trigger signal to be emitted by the emitting device to subsequently deliver the bi-ventricular pacing therapy having the adjusted offset interval.

A medical device and medical device system for determining capture during delivery of a ventricular pacing therapy that includes a subcutaneous sensing device comprising a subcutaneous electrode to sense a subcutaneous cardiac signal and to emit a trigger signal in response to the sensed cardiac signal, an intracardiac therapy delivery device capable of being implanted within a left ventricle of a heart to receive the trigger signal and deliver the ventricular pacing therapy to the left ventricle in response to the emitted trigger signal, and a processor positioned within the subcutaneous sensing device, the processor configured to compare a beat of the subcutaneous cardiac signal sensed by the sensing device subsequent to the ventricular pacing therapy being delivered to a baseline template associated with a non-paced beat, and determine whether the delivered ventricular pacing therapy captures the left ventricle in response to the comparing.

Approaches for characterizing a phrenic stimulation threshold, a cardiac capture threshold, a maximum device parameter, and a minimum device parameter are described. A plurality of cardiac pacing pulses can be delivered by using a cardiac pacing device, a pacing parameter of the plurality of cardiac pacing pulses being changed between delivery of at least some of the pulses. One or more sensor signals can be evaluated to detect stimulation of the phrenic nerve by one or more of the plurality of cardiac pacing pluses. The evaluation of the one or more sensor signals and the pacing parameter can be compared to determine if a phrenic stimulation threshold is at least one of higher than a maximum device parameter and lower than a minimum device parameter.

A medical device system and method for determining pacing threshold data that includes a cardiac capture sensor, a phrenic nerve stimulation sensor, a pulse generator selectively coupled to a plurality of electrode vectors to deliver a phrenic nerve stimulation pulse, and a processor coupled to the cardiac capture sensor, the phrenic nerve stimulation sensor and the pulse generator and configured to deliver the phrenic nerve stimulation pulse along the plurality of electrode vectors, determine, for each vector of the plurality of vectors, whether phrenic nerve stimulation is detected in response to the delivered phrenic nerve stimulation, deliver a pacing pulse along only the vectors of the plurality of vectors that phrenic nerve stimulation is determined not to be detected, and determine pacing capture thresholds in response to the delivered pacing pulse.

This disclosure is directed to devices and techniques for classifying of pacing captures to evaluate effectiveness of pacing by a pacing device, such as an implantable medical device (IMD). An example system includes stimulation circuitry to generate a pacing stimulus, sensing circuitry to sense an evoked response after the pacing stimulus, and processing circuitry. The processing circuitry determines classification features from the evoked response and applies the classification features to a classification model, the classification model generated by a machine learning algorithm using one or more test sets comprising a plurality of sample evoked responses for each of a plurality of classifications. Based on the output of the model, the processing circuitry classifies the evoke response as one of the plurality of classifications.

Methods and systems for dynamically modifying pacing timing and backup pacing delivery in cardiac stimulation devices include applying pacing impulses, measuring corresponding responses, and, based on such responses, automatically modifying timing or operational settings of the stimulation device to improve pacing functionality. Among other things, the approaches described herein reduce unnecessary backup pacing impulses in HIS bundle pacing applications, facilitate fusion in bundle branch block applications, and automatically enable or disable backup pacing in response to achieving QRS complex correction.

Disclosed herein are methods for use with an IMD configured to deliver pacing pulses to cardiac tissue, and related systems for use with and/or including an IMD. A method includes determining a pacing impedance of the cardiac tissue, a first capture threshold of the cardiac tissue, and an estimate of a maximum membrane response for the cardiac tissue. Additionally, the method includes using the maximum membrane response to determine an iso-safety factor strength duration curve. The method also includes determining a current or charge drain curve, and determining, based on the iso-safety factor strength duration curve and the current or charge drain curve, a preferred pacing parameter set that includes a preferred pulse width and a preferred pacing amplitude, which provides a specified safety margin.

In a medical device system, a computer apparatus is configured to receive body surface electrical signals from an electrode apparatus including multiple external electrodes. The computing apparatus generates electrical dyssynchrony data from the body surface electrical signals during delivery of His bundle pacing pulses and identifies effective His bundle capture based on the electrical dyssynchrony data. The computing apparatus generates an indication of His bundle capture in response to identifying the effective His bundle capture.

A computer implemented method and system for monitoring types of capture within a distributed implantable system having a leadless implantable medical device (LIMD) to be implanted entirely within a local chamber of the heart and having a subcutaneous implantable medical device (SIMD) to be located proximate the heart are provided. The method is under control of one or more processors of the SIMD configured with program instructions. The method collects far field (FF) evoked cardiac signals following the pacing pulses delivered by the LIMD for an event and analyzes the FF evoked cardiac signals to identify a type of HIS capture as loss of capture (LOC), selective capture, myocardial tissue-only (MT-only) capture, or a non-selective (NS) capture and records a label for the event based on the type of HIS capture identified.

This disclosure is directed to devices and techniques for classifying of pacing captures to evaluate effectiveness of pacing by a pacing device, such as an implantable medical device (IMD). An example system includes stimulation circuitry to generate a pacing stimulus, sensing circuitry to sense an evoked response after the pacing stimulus, and processing circuitry. The processing circuitry determines classification features from the evoked response and applies the classification features to a classification model, the classification model generated by a machine learning algorithm using one or more test sets comprising a plurality of sample evoked responses for each of a plurality of classifications. Based on the output of the model, the processing circuitry classifies the evoke response as one of the plurality of classifications.