Abstract: A computer implemented method and system for detecting premature ventricular contractions (PVCs) are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains a cycle length (CL) distribution metric that plots a series of cardiac beats into one of a set of transition types based on R-R interval (RRI) difference pairs associated with the cardiac beats. The CL distribution metric plots the cardiac beats based on a comparison between combinations of the RRI difference pairs for corresponding combinations of the cardiac beats. The method calculates a distribution characteristic for the cardiac beats, from the series of cardiac beats that exhibit a first transition type from the set of transition types and calculates a discrimination score based on the distribution characteristic of the cardiac beats across the CL distribution metric.

Abstract: Embodiments described herein can reduce a burden associated with analyzing EGM segments obtained from an IMD that monitors for arrhythmic episodes. Respective EGM data and respective classification data is obtained for each arrhythmic episode detected by the IMD during a period of time. A representative R-R interval or HR for each of the arrhythmic episodes is also obtained, wherein a manner for determining the representative R-R interval or HR depends on the type of the arrhythmic episode, such that for at least two different types of arrhythmic episodes the manners differ. One or more arrhythmic episodes is/are selected for which corresponding EGM segments are to be displayed for each type of arrhythmic episode, wherein the selecting is performed based on the representative R-R intervals or HRs that are determined for the plurality of arrhythmic episodes. Additional and alternative embodiments are also described herein.

Abstract: An apparatus and method for characterizing a region of interest (ROI) including measuring position and orientation data within the ROI; and generating a geometric data set to include one or more of: length, bifurcation location, angle and curvature characteristics of the ROI. Also, sequentially taking an image of a tool within the ROI; comparing tool dimensions with ROI dimensions; and estimating diameter, length, take-off angle, and/or tortuosity characteristics based on the comparisons.

Abstract: A computer implemented method and system for confirming a device documented arrhythmia in cardiac activity are provided. The method is under control of one or more processors configured with executable instructions. The method obtains a cardiac activity (CA) data set that includes CA signals for a series of cardiac events and includes device documented (DD) markers within the series of cardiac events. The device documented markers are indicative of atrial fibrillation (AF) detected by the ICM utilizing an on-board R-R interval irregularity (ORI) process to analyze the CA signals. The method applies a feature enhancement function to the CA signals to form modified CA signals with enhanced sinus features and analyzes the enhanced sinus features in the modified CA signals. The method utilized a confirmatory feature detection process to identify false AF detection by the ORI process. The method records a result of the analysis identifying false AF detection by the ORI process.

Abstract: A system is provided that includes one or more processors, and a memory coupled to the one or more processors. The memory stores program instructions, and the program instructions are executable by the one or more processors. When executed, the one or more processors obtain cardiac activity (CA) signals for a series of beats, and identify whether a characteristic of interest (COI) from a first segment of the CA signals exceeds a COI limit. The one or more processors also analyze morphology of the CA signals for the series of beats responsive to the first segment of the CA signals exceeding the COI limit, and based on the analyze operation, identify a premature ventricular contraction (PVC) within the series of beats.

Abstract: A computer implemented method for detecting arrhythmias in cardiac activity including obtaining far field cardiac activity (CA) signals for a series of beats. For at least a portion of the beats, the one or more processors perform, on a beat by beat basis: a) identifying first and second feature of interests (FOI) from a segment of the CA signal that corresponds to a current beat; and b) classifying the current beat into one of first and second groups. The method also includes designating one of the first and second groups to be a primary group based on a relation between the first and second groups, and for the beats in the primary group, selecting one of the first and second FOIs as the R-wave FOI. The method also includes rejecting an arrhythmia detection based on the P-waves detected.

Abstract: Methods and systems are provided for managing presentation of cardiac activity signals. The methods and systems obtain device classified (DC) data sets generated by an implantable medical device (IMD), the DC data sets including a corresponding cardiac activity (CA) segment from an episode identified by the IMD; compare the CA segments, associated with different episodes, to one another to identify a level of similarity therebetween; separate the CA segments into at least first and second clusters based on the level of similarity; designate a first representative CA segment from the first cluster to be representative of the CA segments in the first cluster; and designate a second representative CA segment from the second cluster to be representative of the CA segments in the second cluster; and a display to present the first and second representative CA segments as representative of the first and second clusters.

Abstract: Computer implemented methods and systems are provided for automatically determining capture thresholds for an implantable medical device equipped for cardiac stimulus pacing using a multi-pole left ventricular (LV) lead. The methods and systems measures a base capture threshold for a base pacing vector utilizing stimulation pulses varied over at least a portion of an outer test range. The base pacing vector is defined by a first LV electrode provided on the LV lead and a second electrode located remote from an LV chamber. The methods and systems designate a secondary pacing vector that includes the first LV electrode and a neighbor LV electrode provided on the LV lead. The methods and systems further define an inner test range having secondary limits based on the base capture threshold, wherein at least one of the limits for the inner test range differs from a corresponding limit for the outer test range.

Abstract: A system and method for declaring arrhythmias in cardiac activity are provided. The system includes memory to store specific executable instructions and a machine learning (ML) model. One or more processors are configured to execute the specific executable instructions to obtain device classified arrhythmia (DCA) data sets generated by an implantable medical device (IMD) for corresponding candidate arrhythmias episodes declared by the IMD. The DCA data sets include cardiac activity (CA) signals for one or more beats sensed by the IMD and one or more device documented (DD) markers that are generated by the IMD. The system applies the ML model to the DCA data sets to identify a valid sub-set of the DCA data sets that correctly characterize the corresponding CA signals and to identify an invalid sub-set of the DCA data sets that incorrectly characterize the corresponding CA signals.

Abstract: Embodiments disclosed herein use multiple AF discriminators to determine whether to classify an AF detection as a false positive. One method includes detecting R-waves within an EGM or ECG signal, determining R?R intervals based on the R-waves, detecting AF based on the R?R intervals, and using one or more time-based AF discriminators to analyze one or more temporal features of the EGM or ECG signal within a window leading up to the AF detection to thereby determine whether to classify the AF detection as a false positive. In response to not classifying the AF detection as a false positive using the one or more time-based AF discriminators, one or more morphology-based AF discriminators are used to analyze one or more morphological features of the EGM or ECG signal within the window leading up to the AF detection to thereby determine whether to classify the AF detection as a false positive.

Abstract: A computer implemented method for detecting arrhythmias in cardiac activity including obtaining far field cardiac activity (CA) signals for a series of beats. For at least a portion of the beats, the one or more processors perform, on a beat by beat basis: a) identifying first and second feature of interests (FOI) from a segment of the CA signal that corresponds to a current beat; and b) classifying the current beat into one of first and second groups. The method also includes designating one of the first and second groups to be a primary group based on a relation between the first and second groups, and for the beats in the primary group, selecting one of the first and second FOIs as the R-wave FOI. The method also includes rejecting an arrhythmia detection based on the P-waves detected.

Abstract: A computer implemented method for detecting pocket stability for an implantable cardiac monitor, including under control of one or more processors in the ICM, collecting impedance data over at least one cardiac cycle. The impedance data is processed to separate an impedance waveform that varies over the at least one cardiac cycle in a manner representative of cardiac functionality over the at least one cardiac cycle. A characteristic of interest is analyzed from the impedance waveform over the at least one cardiac cycle. A pocket stability state of the ICM is identified and recorded based on the analyzing operation.

Abstract: A computer implemented method for detecting arrhythmias in cardiac activity including obtaining far field cardiac activity (CA) signals for a series of beats. For at least a portion of the beats, the one or more processors perform, on a beat by beat basis: a) identifying first and second feature of interests (FOI) from a segment of the CA signal that corresponds to a current beat; and b) classifying the current beat into one of first and second groups. The method also includes designating one of the first and second groups to be a primary group based on a relation between the first and second groups, and for the beats in the primary group, selecting one of the first and second FOIs as the R-wave FOI. The method also includes rejecting an arrhythmia detection based on the P-waves detected.

Abstract: A computer implemented method for detecting pocket stability for an implantable cardiac monitor, including under control of one or more processors in the ICM, collecting impedance data over at least one cardiac cycle. The impedance data is processed to separate an impedance waveform that varies over the at least one cardiac cycle in a manner representative of cardiac functionality over the at least one cardiac cycle. A characteristic of interest is analyzed from the impedance waveform over the at least one cardiac cycle. A pocket stability state of the ICM is identified and recorded based on the analyzing operation.

Abstract: System and methods are provided for determining a stimulation threshold for closed loop spinal cord stimulation (SCS). The system and methods provide a lead coupled to an implantable pulse generator (IPG). The system and methods deliver SCS pulses from the IPG to the lead electrodes in accordance with an SCS therapy and determine an evoked compound action potential (ECAP) amplitude based on an ECAP waveform resulting from the SCS therapy. The system and methods increase the SCS therapy by increasing at least one of an amplitude, a duration, and number of the SCS pulses associated with the SCS therapy. The system and methods also include iteratively repeat the delivering, determining and increasing operations until the ECAP amplitude exhibits a downward trend divergence. The system and methods define a stimulation threshold based on the ECAP amplitude at the trend divergence.

Abstract: Described herein are methods, devices, and systems for identifying false R-R intervals, and false arrhythmia detections, resulting from R-wave undersensing or intermittent AV conduction block. Each of one or more of the R-R intervals is classified as being a false R-R interval in response to a duration the R-R interval being greater than a first specific threshold, and the duration the R-R interval being within a second specified threshold of being an integer multiple of at least X other R-R intervals for which information is obtained, wherein the integer multiple is at least 2, and wherein X is a specified integer that is 1 or greater. When performed for R-R intervals in a window leading up to a detection of a potential arrhythmic episode, results of the classifying can be used to determine whether the potential arrhythmic episode was a false positive detection.

Abstract: Described herein are methods, devices, and systems for improving R-wave detection sensitivity and positive predictive value, and for improving arrhythmia detection accuracy. Certain embodiments involve determining whether to classify a potential R-wave as a false R-wave (or more specifically, an over-sensed P-wave) by determining a measure of magnitude of a first portion of the signal corresponding to a first window following the potential R-wave, determining the measure of magnitude of a second portion of the signal corresponding to a second window following the first window, and classifying the potential R-wave as a false R-wave if the measure of magnitude of the second portion of the signal is at least a specified extent larger (e.g., at least 3 times larger) than the measure of magnitude of the first portion of the signal. Certain embodiments also involve adjusting an R-wave marker for a potential R-wave that is classified as a false R-wave.

Abstract: Described herein are methods, devices, and systems that improve arrhythmia episode detection specificity, such as, but not limited to, atrial fibrillation (AF) episode detection specificity. Such a method can include obtaining an ordered list of R-R intervals within a window leading up to a detection of a potential arrhythmia episode, determining a measure of a dominant repeated R-R interval pattern within the window, and comparing the measure of the dominant repeated R-R interval pattern to a pattern threshold. If the measure of the dominant repeated R-R interval pattern is below the pattern threshold, that is indicative of a regularly irregular pattern being present, and there is a determination that the detection of the potential arrhythmia episode does not correspond to an actual arrhythmia episode. Such embodiments can beneficially be used to significantly reduce the number of false positive arrhythmia detections.

Abstract: Computer implemented methods and systems for detecting arrhythmias in cardiac activity are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains a far field cardiac activity (CA) data set that includes far field CA signals for beats. The method applies a feature enhancement function to the CA signals to form an enhanced feature in the CA data set. The method calculates an adaptive sensitivity level and sensitivity limit based on the enhanced feature from one or more beats within the CA data set and automatically iteratively analyzes a beat segment of interest by comparing the beat segment of interest to the current sensitivity level to determine whether one or more R-waves are present within the beat segment of interest.

Abstract: Described herein are methods, devices, and systems that monitor heart rate and/or for arrhythmic episodes based on sensed intervals that can include true R-R intervals as well as over-sensed R-R intervals. True R-R intervals are initially identified from an ordered list of the sensed intervals by comparing individual sensed intervals to a sum of an immediately preceding two intervals, and/or an immediately following two intervals. True R-R intervals are also identified by comparing sensed intervals to a mean or median of durations of sensed intervals already identified as true R-R intervals. Individual intervals in a remaining ordered list of sensed intervals (from which true R-R intervals have been removed) are classified as either a short interval or a long interval, and over-sensed R-R intervals are identified based on the results thereof. Such embodiments can be used, e.g., to reduce the reporting of and/or inappropriate responses to false positive tachycardia detections.