Systems and methods for detecting arrhythmias in cardiac activity are provided and include memory to store specific executable instructions. One or more processors are configured to execute the specific executable instructions for obtaining first and second far field cardiac activity (CA) data sets over primary and secondary sensing channels, respectively, in connection with a series of beats. The system detects candidate atrial features from the second CA data set, identifies ventricular features from the first CA data set and utilizes the ventricular features to separate beat segments within the second CA data set. The system automatically iteratively analyzes the beat segments by overlaying an atrial activity search window with the second CA data set and determines whether one or more of the candidate atrial features occur within the atrial activity search window. The system adjusts an atrial sensitivity profile based on whether the atrial activity search window includes the one or more of the candidate atrial features and detects atrial events based on the atrial sensitivity profile.

According to the present invention, a filtering apparatus includes an FIR filter that has multipliers arranged to multiply input digital data having their respective different input time points by respective variable tap coefficients. The variable tap coefficients are each switched from a first tap coefficient to a second tap coefficient sequentially for the input digital data from later to earlier input time points. The first tap coefficient is arranged to cause the FIR filter to serve as a low-pass filter with the cut-off frequency set at a first frequency. The second tap coefficient is arranged to cause the FIR filter to serve as a low-pass filter with the cut-off frequency set at a second frequency different from the first frequency.

Methods and computer systems are described that classify a cardiogram as being an atrial fibrillation (AF) or ventricular fibrillation (VF) cardiogram, automatically detect an AF epoch within an AF cardiogram, and automatically detect a VF epoch within a VF cardiogram. A classification and identification (C&I) system includes a classification system, an AF identification system, and a VF identification system. The C&I system processes cardiograms collected from patients to classify the cardiograms as being AF cardiograms or VF cardiograms and to identify AF epochs within the AF cardiograms or VF epochs within the VF cardiograms. The C&I system may then identify an AF source location of an AF based on the AF epochs and a VF source location of a VF based on the VF epochs. The C&I system may display a graphic of a heart that includes an indication of a source location.

Methods and computer systems are described that classify a cardiogram as being an atrial fibrillation (AF) or ventricular fibrillation (VF) cardiogram, automatically detect an AF epoch within an AF cardiogram, and automatically detect a VF epoch within a VF cardiogram. A classification and identification (C&I) system includes a classification system, an AF identification system, and a VF identification system. The C&I system processes cardiograms collected from patients to classify the cardiograms as being AF cardiograms or VF cardiograms and to identify AF epochs within the AF cardiograms or VF epochs within the VF cardiograms. The C&I system may then identify an AF source location of an AF based on the AF epochs and a VF source location of a VF based on the VF epochs. The C&I system may display a graphic of a heart that includes an indication of a source location.

A system and a method include an implantable medical device (IMD) having one or more inputs configured to receive one or more sensed signals from one or more electrodes. A plurality of filters are configured to filter the one or more sensed signals and output a plurality of filtered signals. Memory is configured to store program instructions. A processor, when executing the program instructions, is configured to receive the plurality of filtered signals, and analyze the plurality of filtered signals to determine a desired one of the plurality of filters.

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.

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.

A method includes a first acquisition step of acquiring exercise intensity of exercise done by a target person, a second acquisition step of acquiring an electrocardiographic waveform of the target person who does the exercise, a third acquisition step of acquiring a predetermined feature amount from the acquired electrocardiographic waveform, and an estimation step of estimating an AT of the target person based on a relationship between the predetermined feature amount and the acquired exercise intensity. The estimation step includes a step of estimating the AT of the target person based on exercise intensity corresponding to an inflection point in a change of the predetermined feature amount with respect to the acquired exercise intensity.

This disclosure is directed to systems and techniques for determining an evidence level of an atrial tachycardia (AT) episode based on heart beat intervals in the cardiac activity data over a pre-determined time period. Based on a determination that the evidence level indicates relatively regular heart beat intervals, the example techniques apply a first set of AT detection criteria and indicate a detection of an AT episode based on satisfaction of at least one of the first set of AT detection criteria. Based on a determination that the evidence level indicates relatively irregular heart beat intervals, the example techniques apply a second set of AT detection criteria and indicate a detection of an AT episode based on based on satisfaction of at least one of the second set of AT detection criteria.

This disclosure is directed to systems and techniques for determining an evidence level of an atrial tachycardia (AT) episode based on heart beat intervals in the cardiac activity data over a pre-determined time period. Based on a determination that the evidence level indicates relatively regular heart beat intervals, the example techniques apply a first set of AT detection criteria and indicate a detection of an AT episode based on satisfaction of at least one of the first set of AT detection criteria. Based on a determination that the evidence level indicates relatively irregular heart beat intervals, the example techniques apply a second set of AT detection criteria and indicate a detection of an AT episode based on based on satisfaction of at least one of the second set of AT detection criteria.