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: Systems and methods are provided for detecting arrhythmias in cardiac activity is provided. The systems and methods include measuring conduction delays between an atria (A) and multiple left ventricular (LV) electrodes to obtain multiple intrinsic A/LV intervals, measuring conduction delays between a right ventricular (RV) and the multiple LV electrodes to obtain multiple intrinsic VV intervals. The systems and methods include calculating a first atrial ventricular (AV) delay based on at least one of the intrinsic A/LV intervals, and calculating a second AV delay based on at least one of the intrinsic VV intervals. The systems and methods include selecting a biventricular (BiV) pacing mode or an LV only pacing mode based on a relation between the first and second AV delays, and delivering a pacing therapy based on the selecting operation.

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: 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 that use electrogram (EGM) or electrocardiogram (ECG) data for sleep apnea detection. An apparatus and method detect potential apnea events (an apnea or hypopnea event) using a signal indicative of cardiac electrical activity of a patient's heart, such as an EGM or ECG. Variations in one or more morphological or temporal features of the signal over several cardiac cycles are determined and used to detect a potential apnea event in a measurement period. Checks can then be made for a number of factors which could result in a false detection of an apnea event and if such factors are not present, an apnea event is recorded. Described herein are also methods, devices, and systems for classifying a patient as being asleep or awake, which can be used to selectively enable and disable sleep apnea detection monitoring, as well as in other manners.

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: A computer implemented method for determining heart arrhythmias based on cardiac activity that includes under control of one or more processors of an implantable medical device (IMD) configured with specific executable instructions to obtain far field cardiac activity (CA) signals at electrodes located remote from the heart, and obtain acceleration signatures, at an accelerometer of the IMD, indicative of heart sounds generated during the cardiac beats. The IMD is also configured with specific executable instructions to declare a candidate arrhythmia based on a characteristic of at least one R-R interval from the cardiac beats, and evaluate the acceleration signatures for ventricular events (VEs) to re-assess a presence or absence of at least one R-wave from the cardiac beats and based thereon confirming or denying the candidate arrhythmia.

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: A medical device and method are provided and include a sensing circuitry configured to obtain cardiac activity (CA) signals indicative of cardiac activity over one or more beats. The medical device includes a filter configured to separate, from the CA signals, a respiratory component that varies based on at least one of respiration rate or respiration depth. The medical device includes memory that is configured to store program instructions. The medical device includes a processor that, when executing the program instructions, is configured to analyze the respiratory component to identify a respiration characteristic of interest (COI). The respiration COI is based on at least one of variations in an amplitude of the respiratory component or an interval within the respiratory component and identifies a respiration anomaly based on the respiration COI.

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 computer implemented method and system to detect P-waves in cardiac activity 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 far field cardiac activity (CA) signals for a series of beats, applying a P-wave template to at least one sub-segment of the CA signals to obtain an alignment indicator and calculating an amplitude dependence (AD) indicator based at least in part on the P-wave template and the at least one sub-segment. The system analyzes the alignment indicator based on a first criteria, compares the AD indicator with a second criteria, designates a candidate P-wave to be an actual P-wave based on the analyzing and comparing and records results of the designating.

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: A computer implemented method for determining heart arrhythmias based on cardiac activity that includes under control of one or more processors of an implantable medical device (IMD) configured with specific executable instructions to obtain far field cardiac activity (CA) signals at electrodes located remote from the heart, and obtain acceleration signatures, at an accelerometer of the IMD, indicative of heart sounds generated during the cardiac beats. The IMD is also configured with specific executable instructions to declare a candidate arrhythmia based on a characteristic of at least one R-R interval from the cardiac beats, and evaluate the acceleration signatures for ventricular events (VEs) to re-assess a presence or absence of at least one R-wave from the cardiac beats and based thereon confirming or denying the candidate arrhythmia.

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.

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 lens assembly includes a lens including an optical portion refracting light and a flange portion extended along a periphery of at least a portion of the optical portion, and a lens barrel accommodating the lens. The lens includes a first D-cut portion on one side surface of the flange portion spaced apart from the lens barrel and a second D-cut portion on another side surface of the flange portion spaced apart from the lens barrel, wherein the first D-cut portion and the second D-cut portion each include first inclined surfaces, and the first inclined surfaces are spaced apart from respective ends of the first D-cut portion and the second D-cut portion by a predetermined interval.

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.