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: Computer implemented methods and systems for detecting noise in cardiac activity are provided. The method and system obtain a far field cardiac activity (CA) data set that includes far field CA signals for a series of beats, overlay a segment of the CA signals with a noise search window, and identify turns in the segment of the CA signals. The method and system determine whether the turns exhibit a turn characteristic that exceed a turn characteristic threshold, declare the segment of the CA signals as a noise segment based on the determining operation, shift the noise search window to a next segment of the CA signal and repeat the identifying, determining and declaring operations; and modify the CA signals based on the declaring the noise segments.

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: Computer implemented methods and systems for detecting noise in cardiac activity are provided. The method and system obtain a far field cardiac activity (CA) data set that includes far field CA signals for a series of beats, overlay a segment of the CA signals with a noise search window, and identify turns in the segment of the CA signals. The method and system determine whether the turns exhibit a turn characteristic that exceed a turn characteristic threshold, declare the segment of the CA signals as a noise segment based on the determining operation, shift the noise search window to a next segment of the CA signal and repeat the identifying, determining and declaring operations; and modify the CA signals based on the declaring the noise segments.

Abstract: Methods and systems are provided for detecting arrhythmias in cardiac activity. The methods and systems declare a current beat, from the CA signals, to be a candidate beat or an ineligible beat based on whether the current beat satisfies the rate based selection criteria. The determining and declaring operations are repeated for multiple beats to form an ensemble of candidate beats. The method and system calculate a P-wave segment ensemble from the ensemble of candidate beats, perform a morphology-based comparison between the P-wave segment ensemble and at least one of a monophasic or biphasic template, declare a valid P-wave to be present within the CA signals based on the morphology-based comparison, and utilize the valid P-wave in an arrhythmia detection process to determine at least one of an arrhythmia entry, arrhythmia presence or arrhythmia exit.

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 apparatuses and methods to detect tachycardias and selectively reject false tachycardia detections due to T-wave oversensing or noise. An apparatus includes electrodes, a sensing circuit coupled to at least two of the electrodes and configured to sense a signal indicative of cardiac electrical activity, and a smoothing filter configured to filter to the sensed signal indicative of cardiac electrical activity to thereby produce a filtered signal indicative of cardiac electrical activity. The apparatus produces a difference signal indicative of cardiac electrical activity by determining a difference between the sensed and filtered signals indicative of cardiac electrical activity. The apparatus also includes at least one processor configured to detect a tachycardia, or to determine whether or not to reject a tachycardia detection, based on the difference signal. The smoothing filter and/or difference circuitry can be implemented by the at least one processor, and/or other circuitry.

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, systems and devices are provided to monitor for potential heart failure (HF). Cardiac activity (CA) data is obtained and filtered to obtain respiration data indicative of a respiration pattern. The respiration data is analyzed to determine one or more respiration characteristics of interest (COI) that are recorded along with collection time information to form an HF monitoring log. Additionally or alternatively, the CA data is analyzed to detect an event of interest. The cardiac activity data is filtered to obtain respiration data indicative of a respiration pattern, and the respiration data is analyzed for respiration induced under detection of the event of interest from the CA data.

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: 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.

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: Computer implemented methods and systems for detecting noise in cardiac activity are provided. The method and system obtain a far field cardiac activity (CA) data set that includes far field CA signals for a series of beats, overlay a segment of the CA signals with a noise search window, and identify turns in the segment of the CA signals. The method and system determine whether the turns exhibit a turn characteristic that exceed a turn characteristic threshold, declare the segment of the CA signals as a noise segment based on the determining operation, shift the noise search window to a next segment of the CA signal and repeat the identifying, determining and declaring operations; and modify the CA signals based on the declaring the noise segments.

Abstract: Computer implemented methods and systems are provided that comprise, under control of one or more processors of a medical device, where the one or more processors are configured with specific executable instructions. The methods and systems obtain motion data indicative of at least one of a posture or a respiration cycle; obtain cardiac activity (CA) signals for a series of beats; identify whether a characteristic of interest (COI) from at least a first segment of the CA signals exceeds a COI limit; analyze the motion data to determine whether at least one of the posture or respiration cycle at least in part caused the COI to exceed the COI limit. Based on the analyzing operation, the methods and systems automatically adjust a CA sensing parameter utilized by the medical device to detect R-waves in subsequent CA signals; and detect an arrhythmia based on a presence or absence of one or more of the R-waves in at least a second segment of the CA signals.

Abstract: Computer implemented methods and systems for detecting noise in cardiac activity are provided. The method and system obtain a far field cardiac activity (CA) data set that includes far field CA signals for a series of beats, overlay a segment of the CA signals with a noise search window, and identify turns in the segment of the CA signals. The method and system determine whether the turns exhibit a turn characteristic that exceed a turn characteristic threshold, declare the segment of the CA signals as a noise segment based on the determining operation, shift the noise search window to a next segment of the CA signal and repeat the identifying, determining and declaring operations; and modify the CA signals based on the declaring the noise segments.

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.

Abstract: Computer implemented methods and systems are provided that comprise, under control of one or more processors of a medical device, where the one or more processors are configured with specific executable instructions. The methods and systems obtain motion data indicative of at least one of a posture or a respiration cycle; obtain cardiac activity (CA) signals for a series of beats; identify whether a characteristic of interest (COI) from at least a first segment of the CA signals exceeds a COI limit; analyze the motion data to determine whether at least one of the posture or respiration cycle at least in part caused the COI to exceed the COI limit. Based on the analyzing operation, the methods and systems automatically adjust a CA sensing parameter utilized by the medical device to detect R-waves in subsequent CA signals; and detect an arrhythmia based on a presence or absence of one or more of the R-waves in at least a second segment of the CA signals.

Abstract: A computer implemented method and system 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 far field cardiac activity (CA) signals and applies a direction related responsiveness (DRR) filter to the CA signals to produce DRR filtered signals. The method compares a current sample from the CA signals to a prior sample from the DRR filtered signals to identify a direction characteristic of the CA signals and defines the DRR filter based on a timing constant that is set based on the direction characteristic identified. The method analyzes the CA signals in connection with the DRR filtered signals to identify a peak characteristic of the CA signals and determines peak to peak intervals between successive peak characteristic. The method detects at least one of noise or an arrhythmia based on the peak to peak intervals and records results of the detecting.

Abstract: A computer implemented method and system are provided for detecting premature ventricular contractions (PVCs) in cardiac activity. The method and system obtain cardiac activity (CA) signals for a series of beats, and, for at least a portion of the series of beats, calculate QRS scores for corresponding QRS complex segments from the CA signals. The method and system calculate a variability metric for QRS scores across the series of beats, calculate a QRS complex template using QRS segments from the series of beats, calculate correlation coefficients between the QRS complex template and the QRS complex segments, compare the variability metric to a variability threshold and the correlation coefficients to a correlation threshold, and designate the CA signals to include a predetermined level of PVC burden based on the determining.

Abstract: Described herein are methods, devices and system for determining whether an R-wave detection should be classified as a false R-wave detection due to T-wave oversensing (TWO) or P-wave oversensing (PWO). One such method includes comparing a specific morphological characteristic (e.g., peak amplitude) associated with the R-wave detection to the specific morphological characteristic associated with each R-wave detection in a first set of earlier detected R-wave detections to thereby determine whether first TWO or PWO morphological criteria are met, and in a second set of earlier detected R-wave detections to thereby determine whether second TWO or PWO morphological criteria are met, wherein the second set differs from the first set but may have some overlap with the first set. The method also includes determining whether to classify the R-wave detection as a false R-wave detection, based on whether one of the first or second TWO or PWO morphological criteria are met.