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 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: 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 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 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: 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: 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: Systems, methods, and computer-readable media for predicting policy changes in a network environment through a data serialization language. Running configurations of one or more network devices in a network environment can be collected. Further, a master device template of a new policy for the network environment can be generated through a data serialization language based on the running configurations of the one or more network devices. The master device template can include a directory-based tree structure of a simulated implementation of the new policy in the network environment. Policy changes in the network environment for the new policy can be predicted before implementing the new policy in the network environment based on simulated packet traversal through the directory-based tree structure of the simulated implementation of the policy in the network environment.

Abstract: Systems, methods, and computer-readable media for predicting policy changes in a network environment through a data serialization language. Running configurations of one or more network devices in a network environment can be collected. Further, a master device template of a new policy for the network environment can be generated through a data serialization language based on the running configurations of the one or more network devices. The master device template can include a directory-based tree structure of a simulated implementation of the new policy in the network environment. Policy changes in the network environment for the new policy can be predicted before implementing the new policy in the network environment based on simulated packet traversal through the directory-based tree structure of the simulated implementation of the policy in the network environment.

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: 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: 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: 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 comparing.

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: 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 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.