Abstract: An L2 virtual machine (VM) operating in a trust domain invokes a memory operation involving a virtual I/O device passed through from an L1 VM. Invocation of the memory operation causes an L2 virtual I/O device driver to make a hypercall to a trust domain management module. The hypercall comprises a memory-mapped I/O (MMIO) address of the virtual I/O device as seen by the L2 VM (L2 MMIO address), which matches the MMIO address of the virtual I/O device as seen by the L1 VM. The module passes hypercall information to an LO hypervisor, which forwards the information to an emulator operating in LO user space that emulates the back end of the virtual I/O device operating on the L1 VM. The emulator determines an emulated software response based on the L2 MMIO address and the memory operation is carried out.

Abstract: Some method examples may include pacing a heart with cardiac paces, sensing a physiological signal for use in detecting pace-induced phrenic nerve stimulation, performing a baseline level determination process to identify a baseline level for the sensed physiological signal, and detecting pace-induced phrenic nerve stimulation using the sensed physiological signal and the calculated baseline level. Detecting pace-induced phrenic nerve stimulation may include sampling the sensed physiological signal during each of a plurality of cardiac cycles to provide sampled signals and calculating the baseline level for the physiological signal using the sampled signals. Sampling the sensed physiological signal may include sampling the signal during a time window defined using a pace time with each of the cardiac cycles to avoid cardiac components and phrenic nerve stimulation components in the sampled signal.

Abstract: The disclosed computerized system and method facilitates predicting the onset of diabetes or symptom progression in those patients already suffering from the disease. The computerized system and method applies steps to segment the population by predefined member characteristics. Once segmented, the computerized system and method applies a plurality of prediction models to the segmented population data to provide a ranking of members of the population that indicates the likelihood of onset or progression of diabetes for each member.

Abstract: Methods and devices for classifying a cardiac response to pacing involve establishing a retriggerable cardiac response classification window. A first cardiac response classification window is established subsequent to delivery of a pacing pulse. A cardiac signal following the pacing stimulation is sensed in the first classification window. A second cardiac response classification may be triggered if a trigger characteristic is detected in the first classification window. The cardiac signal is sensed in the second classification window if the second classification window is established. The cardiac response to the pacing stimulation is determined based on characteristics of the cardiac signal. The cardiac response may be determined to be one of a captured response, a non-captured response, a non-captured response added to an intrinsic beat, and a fusion/pseudofusion beat, for example.

Abstract: Some method examples may include pacing a heart with cardiac paces, sensing a physiological signal for use in detecting pace-induced phrenic nerve stimulation, performing a baseline level determination process to identify a baseline level for the sensed physiological signal, and detecting pace-induced phrenic nerve stimulation using the sensed physiological signal and the calculated baseline level. Detecting pace-induced phrenic nerve stimulation may include sampling the sensed physiological signal during each of a plurality of cardiac cycles to provide sampled signals and calculating the baseline level for the physiological signal using the sampled signals. Sampling the sensed physiological signal may include sampling the signal during a time window defined using a pace time with each of the cardiac cycles to avoid cardiac components and phrenic nerve stimulation components in the sampled signal.

Abstract: An arrhythmia classification system receives cardiac data from an implantable medical device, performs automatic adjudication of each cardiac arrhythmia episode indicated by the cardiac data, and generates episode data representative of information associated with the episode. The episode data include at least an episode classification resulting from the automatic adjudication of the episode and a confidence level in the episode classification. In one embodiment, the episode data further include key features rationalizing the automatic adjudication of the episode.

Abstract: Some method examples may include pacing a heart with cardiac paces, sensing a physiological signal for use in detecting pace-induced phrenic nerve stimulation, performing a baseline level determination process to identify a baseline level for the sensed physiological signal, and detecting pace-induced phrenic nerve stimulation using the sensed physiological signal and the calculated baseline level. Detecting pace-induced phrenic nerve stimulation may include sampling the sensed physiological signal during each of a plurality of cardiac cycles to provide sampled signals and calculating the baseline level for the physiological signal using the sampled signals. Sampling the sensed physiological signal may include sampling the signal during a time window defined using a pace time with each of the cardiac cycles to avoid cardiac components and phrenic nerve stimulation components in the sampled signal.

Abstract: Methods and devices for classifying a cardiac response to pacing involve establishing a retriggerable cardiac response classification window. A first cardiac response classification window is established subsequent to delivery of a pacing pulse. A cardiac signal following the pacing stimulation is sensed in the first classification window. A second cardiac response classification may be triggered if a trigger characteristic is detected in the first classification window. The cardiac signal is sensed in the second classification window if the second classification window is established. The cardiac response to the pacing stimulation is determined based on characteristics of the cardiac signal. The cardiac response may be determined to be one of a captured response, a non-captured response, a non-captured response added to an intrinsic beat, and a fusion/pseudofusion beat, for example.

Abstract: Methods and devices for classifying a cardiac response to pacing involve establishing a retriggerable cardiac response classification window. A first cardiac response classification window is established subsequent to delivery of a pacing pulse. A cardiac signal following the pacing stimulation is sensed in the first classification window. A second cardiac response classification may be triggered if a trigger characteristic is detected in the first classification window. The cardiac signal is sensed in the second classification window if the second classification window is established. The cardiac response to the pacing stimulation is determined based on characteristics of the cardiac signal. The cardiac response may be determined to be one of a captured response, a non-captured response, a non-captured response added to an intrinsic beat, and a fusion/pseudofusion beat, for example.

Abstract: An apparatus comprises an implantable cardiac signal sensing circuit configured to provide a sensed depolarization signal from a ventricle and a processor. The processor includes a signal analyzer module and a tachyarrhythmia discrimination module. The signal analyzer module is configured to determine a measure of stability of ventricular (V-V) depolarization intervals using the depolarization signal, and determine a rate of change of the measure of stability. The tachyarrhythmia discrimination module is configured to detect an episode of tachyarrhythmia using the depolarization signal, determine whether the detected tachyarrhythmia is indicative of atrial tachyarrhythmia using the determined rate of change, and provide the determination to a user or process.

Abstract: Monitoring physiological parameter using an implantable physiological monitor in order to detect a condition predictive of a possible future pathological episode and collecting additional physiological data associated with the condition predictive of a possible future pathological episode. Monitoring another physiological parameter in order to detect a condition indicative of the beginning of a present pathological episode and collecting additional pathological data in response to the condition. Determining that the condition predictive of a future episode and the condition indicative of a present episode are associated and, in response thereto, storing all the collected physiological data.

Abstract: Various techniques are disclosed for quickly and efficiently determining cardiac pacing vectors that minimize phrenic nerve stimulation.

Abstract: The present invention is a method of predicting the likelihood that chronic kidney disease will result in end stage renal disease requiring dialysis. The method uses various indicators comprising information specific to an individual as well as information representing characteristics of a population including demographic information, health care and prescription insurance claims, and involvement in various programs designed to improve the health of a user. The method applies a predictive algorithm to these indicators in order to derive a risk score indicating an individual's risk of dialysis.

Abstract: The disclosed computerized system and method facilitates predicting the onset of diabetes or symptom progression in those patients already suffering from the disease. The computerized system and method applies steps to segment the population by predefined member characteristics. Once segmented, the computerized system and method applies a plurality of prediction models to the segmented population data to provide a ranking of members of the population that indicates the likelihood of onset or progression of diabetes for each member.

Abstract: An apparatus comprises an implantable cardiac signal sensing circuit configured to provide a sensed depolarization signal from a ventricle and a processor. The processor includes a signal analyzer module and a tachyarrhythmia discrimination module. The signal analyzer module is configured to determine a measure of stability of ventricular (V-V) depolarization intervals using the depolarization signal, and determine a rate of change of the measure of stability. The tachyarrhythmia discrimination module is configured to detect an episode of tachyarrhythmia using the depolarization signal, determine whether the detected tachyarrhythmia is indicative of atrial tachyarrhythmia using the determined rate of change, and provide the determination to a user or process.

Abstract: Monitoring physiological parameter using an implantable physiological monitor in order to detect a condition predictive of a possible future pathological episode and collecting additional physiological data associated with the condition predictive of a possible future pathological episode. Monitoring another physiological parameter in order to detect a condition indicative of the beginning of a present pathological episode and collecting additional pathological data in response to the condition. Determining that the condition predictive of a future episode and the condition indicative of a present episode are associated and, in response thereto, storing all the collected physiological data.

Abstract: An arrhythmia classification system receives cardiac data from an implantable medical device, performs automatic adjudication of each cardiac arrhythmia episode indicated by the cardiac data, and generates episode data representative of information associated with the episode. The episode data include at least an episode classification resulting from the automatic adjudication of the episode and a confidence level in the episode classification. In one embodiment, the episode data further include key features rationalizing the automatic adjudication of the episode.

Abstract: An apparatus comprises an implantable cardiac signal sensing circuit configured to provide a sensed depolarization signal from a ventricle and a processor. The processor includes a signal analyzer module and a tachyarrhythmia discrimination module. The signal analyzer module is configured to determine a measure of stability of ventricular (V?V) depolarization intervals using the depolarization signal, and determine a rate of change of the measure of stability. The tachyarrhythmia discrimination module is configured to detect an episode of tachyarrhythmia using the depolarization signal, determine whether the detected tachyarrhythmia is indicative of atrial tachyarrhythmia using the determined rate of change, and provide the determination to a user or process.

Abstract: Monitoring physiological parameter using an implantable physiological monitor in order to detect a condition predictive of a possible future pathological episode and collecting additional physiological data associated with the condition predictive of a possible future pathological episode. Monitoring another physiological parameter in order to detect a condition indicative of the beginning of a present pathological episode and collecting additional pathological data in response to the condition. Determining that the condition predictive of a future episode and the condition indicative of a present episode are associated and, in response thereto, storing all the collected physiological data.

Abstract: An arrhythmia classification system receives cardiac data from an implantable medical device, performs automatic adjudication of each cardiac arrhythmia episode indicated by the cardiac data, and generates episode data representative of information associated with the episode. The episode data include at least an episode classification resulting from the automatic adjudication of the episode and a confidence level in the episode classification. In one embodiment, the episode data further include key features rationalizing the automatic adjudication of the episode.