A pacemaker system includes a drive-sense circuit (DSC) operably coupled to a pacemaker lead. The DSC generates a pace signal including electrical impulses based on a reference signal. The DSC provides the pace signal via the pacemaker lead to an electrically responsive portion of a cardiac conductive system of a subject to facilitate cardiac operation of a cardiovascular system of the subject. The DSC senses, via the pacemaker lead, cardiac electrical activity of the cardiovascular system of the subject that is generated in response to the pace signal and electrically coupled into the pacemaker lead and generates a digital signal that is representative of the cardiac electrical activity of the cardiovascular system of the subject that is sensed via the pacemaker lead. The DSC provides digital information to one or more processing modules that includes and/or is coupled to memory and that provide the reference signal to the DSC.

Systems and methods for managing cardiac arrhythmias are discussed. A data management system receives a first detection algorithm including a detection criterion for detecting a cardiac arrhythmia. An arrhythmia detector detects arrhythmia episodes from a physiologic signal using a second detection algorithm that is different from and has a higher sensitivity for detecting the cardiac arrhythmia than the first detection algorithm. The arrhythmia detector assigns a detection indicator to each of the detected arrhythmia episodes. The detection indicator indicates a likelihood that the detected arrhythmia episode satisfies the detection criterion of the first detection algorithm. The system prioritizes the detected arrhythmia episodes according to the assigned detection indicators, and outputs the arrhythmia episodes to a user or a process according to the episode prioritization.

Computer implemented methods, devices and systems for monitoring a trend in heart failure (HF) progression are provided. The method comprises sensing left ventricular (LV) activation events at multiple LV sensing sites along a multi-electrode LV lead. The activation events are generated in response to an intrinsic or paced ventricular event. The method implements program instructions on one or more processors for automatically determining a conduction pattern (CP) across the LV sensing sites based on the LV activation events, identifying morphologies (MP) for cardiac signals associated with the LV activation events and repeating the sensing, determining and identifying operations, at select intervals, to build a CP collection and an MP collection. The method calculates an HF trend based on the CP collection and MP collection and classifies a patient condition based on the HF trend to form an HF assessment.

Systems and methods for pacing cardiac conductive tissue are described. An embodiment of a medical system includes an electrostimulation circuit to generate His-bundle pacing (HBP) pulses to stimulate a His bundle, and a cardiac event detector to detect a His-bundle activity within a time window following an atrial activity. The cardiac event detector may use a cross-chamber blanking, or an adjustable His-bundle sensing threshold, to avoid or reduce over-sensing of far-field atrial activity and inappropriate inhibition of HBP therapy. The electrostimulation circuit may deliver HBP in the presence of the His-bundle activity. The system may further recognize the detected His-bundle activity as either a FFPW or a valid inhibitory event, and deliver or withhold HBP therapy based on the recognition of the His-bundle activity.

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.

A medical device system, such as an extra-cardiovascular implantable cardioverter defibrillator ICD, senses R-waves from a first cardiac electrical signal by a first sensing channel and stores a time segment of a second cardiac electrical signal in response to each sensed R-wave. The medical device system determines a morphology parameter correlated to signal noise from time segments of the second cardiac electrical signal, detects a noisy signal segment based on the signal morphology parameter; and withholds detection of a tachyarrhythmia episode in response to detecting a threshold number of noisy signal segments.

Methods, devices and program products are provided for under control of one or more processors within an implantable medical device (IMD). Sensing near field (NF) and far field (FF) signals are between first and second combinations of electrodes coupled to the IMD. The method applies an arrhythmia detection algorithm to the NF signals for identifying events within the NF signal and designates events marker based thereon and monitors the event markers to detect a candidate arrhythmia condition in the NF signals. The candidate under-detected condition comprises at least one of an under-detected arrhythmia or over-sensing. In response to detection of the candidate arrhythmia condition, the method analyzes the FF signals for a presence of an under-detected arrhythmia indicator. The method delivers an arrhythmia therapy based on the presence of the under-detected arrhythmia indicator in the FF signals and the candidate under-detected arrhythmia condition in the NF signals.

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.

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.

An intracardiac ventricular pacemaker having a motion sensor, a pulse generator and a control circuit coupled to the pulse generator and the motion sensor is configured to identify a ventricular systolic event, detect a ventricular passive filling event signal from the motion signal, and determine a time interval from the ventricular systolic event to the ventricular passive filling event. The pacemaker establishes a minimum pacing interval based on the time interval.