Abstract: Implantable medical devices and methods that utilize a sensing circuit to sense cardiac activity (CA) signals and an accelerometer to obtain accelerometer data. Memory stores program instructions and device parameters associated with a collection of ventricular arrhythmia (VA) therapies. A processor analyzes the CA signals over one or more cardiac beats, determines a VA episode based on the analysis of the CA signals, determines the VA episode to be hemodynamically stable or unstable based on at least one of the accelerometer data or the CA signals, selects a first VA therapy from the collection of VA therapies based on whether the VA episode is hemodynamically stable or unstable, and apply the first VA therapy using at least one of the at least two coils.

Abstract: A system for verifying a candidate pathologic episode of a patient is provided that includes an accelerometer configured to be implanted in the patient. The accelerometer is 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 that, when executing the program instructions, are configured to obtain accelerometer data. The one or more processors are also configured to determine a plurality of control three-dimensional point vectors related to the accelerometer data, and obtain a biological signal and identify a candidate pathologic episode based on the biological signal. The one or more processors are also configured to analyze the plurality of control three-dimensional point vectors to identify a physical action experienced by the patient, and verify the candidate pathologic episode based on the physical action.

Abstract: A healthcare system and method are provided that comprise first and second medical devices (MD). The first MD has sensing circuitry to obtain IEGM and/or ECG signals for a patient that include a cardiac activity (CA) component and an electrical respiratory component. A filter receives the IEGM and/or ECG signals, block the cardiac activity component and pass the electrical respiratory component. A second medical device collects and outputs second MD data that includes body generated analyte (BGA), second IEGM and/or second ECG data associated with the patient. One or more processors receive respiration data and the second MD data and analyze the respiration data and the second MD data utilizing an application specific model (ASM) to calculate a health risk index associated with the patient.

Abstract: Described herein are apparatuses and methods for classifying a patient as being asleep or awake. Such an apparatus can include an accelerometer and a processor. The accelerometer, alone or in combination with the processor, is used to determine an activity level of the patient and a posture of the patient. The processor is configured to classify the patient as being asleep in response to both (i) the posture of the patient being recumbent or reclined for at least a sleep latency duration, and (ii) the activity level of the patient not exceeding an activity threshold for at least the sleep latency duration; and classify the patient as being awake in response to at least one of (iii) the posture of the patient being upright for at least an awake latency duration, or (iv) the activity level of the patient exceeding the activity threshold for at least the awake latency duration.

Abstract: Methods and systems for monitoring heart valve operation using signals detected by a heart sound sensor includes memory to store program instructions and one or more processors that, when executing the program instructions receive seismocardiography (SCG) signals detected by a heart sound sensor along an axis. The SCG signals include heart sound signals for a series of heartbeats over a first time period. The SCG signals are segmented into SCG segments for corresponding heartbeats within the series of heartbeats over the first time period. A template is calculated based on a first subset of the SCG segments. At least a portion of a second subset of the SCG segments are compared to the template to determine matching scores, and an alert is generated in response to a select number of the second subset of SCG segments having the matching scores that satisfy a matching threshold.

Abstract: A method for temperature-based diagnosis and treatment using an implantable medical device (IMD) is provided. The method includes collecting treatment temperature data from a temperature sensor of the implantable medical device (IMD) at a first rate in a first mode and collecting diagnostic temperature data from the temperature sensor in a second mode at a second rate that is less than the first rate. The method also includes utilizing one or more processors to perform: in the first mode analyzing the treatment temperature data and delivering a treatment of the IMD based on the treatment temperature data. The method also includes utilizing the one or more processors in the second mode to analyze the diagnostic temperature data and communicate an alert based on the diagnostic temperature data.

Abstract: An implantable medical device and computer implemented methods comprise a sensing circuit configured to sense cardiac activity (CA) signals. An accelerometer is configured to be implanted in a patient and obtain accelerometer data along at least one axis. A memory is configured to store program instructions and device parameters associated with each ventricular arrhythmia (VA) therapy in a collection of VA therapies with different levels of intensity. One or more processors execute the program instructions and are configured to analyze the CA signals over one or more cardiac beats, determine a VA episode based on the analysis of the CA signals, determine a posture of the patient based on the accelerometer data in response to the determination of the VA episode, and select a first VA therapy from the collection of VA therapies based on the posture.

Abstract: System for arrhythmia detection and confirmation includes implantable medical device (IMD) having a sensing circuit for sensing cardiac activity (CA) for one or more cardiac cycles and generating one or more CA signals. An implantable pressure sensor (IPS) includes IPS sensing circuit for sensing pressure during the one or more cardiac cycles and generating one or more pressure signals. IMD and IPS include communications circuits for communicating with each other and/or an external device. One or both of IMD or IPS includes memory for storing program instructions and processor(s) for analyzing one of the CA or pressure signals, for one or more cardiac cycles, to detect a candidate arrhythmia. The processor(s) obtain another one of CA or pressure signals for cardiac cycles corresponding to the one or more cardiac cycles, and confirm or deny candidate arrhythmia based on the other one of the signals.

Abstract: A method for controlling an implantable medical device (IMD) is provided. The method can include obtaining, with a monitoring device, cardiac activity signals, and obtaining, with an accelerometer, candidate heart sound (HS) signals. The method can also include analyzing the cardiac activity signals based on an initial criteria and eliminating candidate HS signals based on the analyzing to provide remainder candidate HS signals. The method also can include analyzing the remainder candidate HS signals based on HS quality criteria related to the remainder candidate HS signals, eliminating additional candidate HS signals from the remainder candidate HS signals based on analyzing the remainder candidate HS signals to provide a HS ensemble, and controlling the IMD based on the HS ensemble. An IMD is also provided.

Abstract: A system for monitoring a physiologic condition of a patient includes an implantable medical device (IMD) comprising: an accelerometer configured to output an accelerometer signal; sensing circuitry configured to sense a cardiac activity (CA) signal; and a memory configured to store program instructions. One or more processors that, when executing the program instructions, are configured to: determine a heart rate (HR) at a point in time based on the CA signal; determine an activity level (AL) at the point in time based on accelerometer data that is based on the accelerometer signal, the HR and AL forming an HR-AL event; compare the HR-AL event to a heart condition (HC) metric that defines combinations of HRs and ALs representing a physiologically normal heart condition and a physiologically abnormal heart condition; and transmit the HR-AL event or a result of the comparison to a second device for use in diagnosing and/or treating a heart condition.

Abstract: Described herein are methods, devices and systems for classifying an R-wave detection as a false R-wave detection due to T-wave oversensing (TWO) or P-wave oversensing (PWO), by determining whether at least one of first TWO or PWO temporal criteria or second TWO or PWO temporal criteria are met for R-wave detections in a window leading up to an arrhythmic episode detection, and based on an extent of the R-wave detections classified as being false R-wave detections due to TWO or PWO selectively preventing or aborting delivery of therapy intended to treat the arrhythmic episode, selectively preventing transmission by the IMD to an external device of data corresponding to the arrhythmic episode that can be used for diagnostic purposes, or selectively adjusting at least one parameter of the R-wave detection threshold that can be used by the IMD for detecting further R-waves and thereby detecting a further arrhythmic episode.

Abstract: System for arrhythmia detection and confirmation includes implantable medical device (IMD) having a sensing circuit for sensing cardiac activity (CA) for one or more cardiac cycles and generating one or more CA signals. An implantable pressure sensor (IPS) includes IPS sensing circuit for sensing pressure during the one or more cardiac cycles and generating one or more pressure signals. IMD and IPS include communications circuits for communicating with each other and/or an external device. One or both of IMD or IPS includes memory for storing program instructions and processor(s) for analyzing one of the CA or pressure signals, for one or more cardiac cycles, to detect a candidate arrhythmia. In response to detecting candidate arrhythmia, the processor(s) obtain another one of CA or pressure signals for cardiac cycles corresponding to the one or more cardiac cycles, and confirm or deny candidate arrhythmia based on the other one of the signals.

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: Described herein are methods, devices and systems 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.

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: 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: 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 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: 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 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.