Abstract: A leadless implantable medical device (IMD) and method of using same are provided. The IMD comprises: a housing, a fixation element, electrodes configured to sense electrical cardiac activity (CA) signals over a period of time, an HS sensor configured to sense HS signals over the period of time, memory to store specific executable instructions, and one or more processors. The one or more processors and method: identify a characteristic of interest (COI) of a heartbeat from the CA signals, calculate a center of mass (COM) for at least one HS based on the HS signals to obtain a corresponding at least one HS COM, and calculate at least one of a therapy-related (TR) delay or a sensing-related (SR) blanking interval (BI) based on the at least one HS COM.

Abstract: A computer implemented method and system for detecting an arrhythmia are provided. The method is under control of one or more processors configured with executable instructions. The method obtains far field cardiac activity (CA) signals sensed at electrodes located remote from a heart over a period of time and applies a feature attenuation filter to the CA signals to form modified CA signals. The feature attenuation filter reduces potential T-waves as a feature not of interest. The method calculates a duty cycle (DC) characteristic of the modified CA signals with respect to duty cycle boundaries and detects an arrhythmia based on the DC characteristic.

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: 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: 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: 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: An implantable cardiac monitor (ICM) and a computer implemented method are provided. The ICM includes electrodes that are configured to sense analog cardiac activity (CA) signals when engaged with tissue at a location remote from the heart. One or more of the electrodes transition between an engaged state and disengaged state with respect to the tissue. An analog to digital (A/D) converter communicates with the electrodes along a data signal path. The A/D converts the analog CA signals to digital CA signal. An electrode-tissue feedback (ETF) circuit is provided along the data signal path between the electrodes and the A/D converter. The ETF circuit generates a feedback signal component into the data signal path when one or more of the electrodes is in the disengaged state. The feedback signal component is superimposed onto the analog CA signals.

Abstract: Methods, devices and program products are provided for determining an early battery depletion condition for a battery powered device. The method determines a charge consumption drawn externally from a battery cell by the device for a select period of time, obtains a measured cell voltage for the battery cell of the medical device, calculates a projected cell voltage based on the charge consumption and usage conditions, and declares an early depletion condition based on a relation between the measured and projected cell voltages.

Abstract: A system for verifying a candidate pathologic episode of a patient is provided. The system includes an accelerometer configured to be implanted in the patient, the accelerometer configured to obtain accelerometer data along at least one axis. The system also includes a memory configured to store program instructions and one or more processors. When executing the program instructions, the one or more processors are configured to obtain a biological signal and identify a candidate pathologic episode based on the biological signal, analyze the accelerometer data to identify a physical action experienced by the patient, and verify the candidate pathologic episode based on the physical action.

Abstract: A system for monitoring a physiologic condition of a patient including an accelerometer configured to be implanted in the patient, the accelerometer configured to obtain multi-dimensional (MD) accelerometer data along at least two axes. When executing program instructions, the one or more processors may be configured to initiate a data collection interval in connection with patient activity, obtain the MD accelerometer data during the patient activity for at least one of a select period of time or until receiving a data collection halt instruction, and calculate a travel-related (TR) parameter based on the MD accelerometer data. The one or more processors may also be configured to correlate the TR parameter with physiologic data obtained during the patient activity, and store the TR parameter and physiologic data as indicators of a current physiologic state of the patient.

Abstract: 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.

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: A system and method for accelerating confirmation of cardiac arrhythmias is provided. The system includes memory to store specific executable instructions. One or more processors are configured to execute the specific executable instructions for obtaining a far field cardiac activity (CA) data set that includes far field CA signals for a series of beats. The CA data set includes a first CA data subset and a remainder CA data subset. The system analyzes the CA data set for an arrhythmia of interest utilizing a primary detection process having primary criteria. During a first phase of the primary detection process the system analyzes the first CA data subset to determine whether the first CA data subset satisfies at least a portion of the primary criteria. When the first CA data subset satisfies at least the portion of the primary criteria, the system initiates a secondary confirmation 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: The present invention is directed to a method for combining assessment of different factors of dyssynchrony into a comprehensive, non-invasive toolbox for treating patients with a CRT therapy device. The toolbox provides high spatial resolution, enabling assessment of regional function, as well as enabling derivation of global metrics to improve patient response and selection for CRT therapy. The method allows for quantitative assessment and estimation of mechanical contraction patterns, tissue viability, and venous anatomy from CT scans combined with electrical activation patterns from Body Surface Potential Mapping (BSPM). This multi-modal method is therefore capable of integrating electrical, mechanical, and structural information about cardiac structure and function in order to guide lead placement of CRT therapy devices. The method generates regional electro-mechanical properties overlaid with cardiac venous distribution and scar tissue.

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: An implantable cardiac monitor (ICM) and a computer implemented method are provided. The ICM includes electrodes that are configured to sense analog cardiac activity (CA) signals when engaged with tissue at a location remote from the heart. One or more of the electrodes transition between an engaged state and disengaged state with respect to the tissue. An analog to digital (A/D) converter communicates with the electrodes along a data signal path. The A/D converts the analog CA signals to digital CA signal. An electrode-tissue feedback (ETF) circuit is provided along the data signal path between the electrodes and the A/D converter. The ETF circuit generates a feedback signal component into the data signal path when one or more of the electrodes is in the disengaged state. The feedback signal component is superimposed onto the analog CA signals.

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.