Abstract: A method of detecting a cardiac event in a medical device that includes sensing cardiac signals from a plurality of electrodes forming a first sensing vector and a second sensing vector, determining whether a signal energy content metric of the sensed cardiac signals is within predetermined limits, determining whether a noise to signal ratio of the sensed cardiac signals is less than a signal to noise threshold, determining whether the sensed cardiac signals are associated with muscle noise, and determining whether a mean frequency corresponding to the sensed cardiac signals is less than a mean frequency threshold.

Abstract: A method of detecting a cardiac event in a medical device that includes sensing cardiac signals from a plurality of electrodes forming a first sensing vector and a second sensing vector, determining inflections of the sensed cardiac signals, generating a pulse amplitude threshold in response to the determined inflections, determining whether the sensed cardiac signals are corrupted by noise in response to the determined inflections and the generated pulse amplitude threshold, and determining, in response to the sensed cardiac signals being corrupted by noise, whether the sensed cardiac signals are both corrupted by noise and shockable.

Abstract: A method of detecting a cardiac event in a medical device that includes sensing cardiac signals from a plurality of electrodes forming a first sensing vector and a second sensing vector, determining inflections of the sensed cardiac signals, generating a pulse amplitude threshold in response to the determined inflections, and determining whether the inflections are indicative of noise in response to the determined inflections and the generated pulse amplitude threshold.

Abstract: A method of detecting a cardiac event in a medical device that includes sensing cardiac signals from a plurality of electrodes forming a first sensing vector and a second sensing vector, and determining whether the sensing of cardiac signals along the first sensing vector and along the second sensing vector is corrupted by noise. If the cardiac signals sensed along both the first sensing vector and the second sensing vector are not corrupted by noise, a determination is made as to whether the cardiac signals sensed along the first sensing vector and along the second sensing vector are one of a first cardiac event and a second cardiac event. The determination of whether the cardiac signals sensed along the first sensing vector and along the second sensing vector are one of a first cardiac event and a second cardiac event is then confirmed.

Abstract: A method of detecting a cardiac event in a medical device that includes sensing cardiac signals from a plurality of electrodes forming a first sensing vector and a second sensing vector, determining whether the first sensing vector and the second sensing vector is corrupted by noise, and determining, in response to one of the first sensing vector and the second sensing vector being corrupted by noise, whether the other of the first sensing vector and the second sensing vector is one of a first cardiac event and a second cardiac event different from the first cardiac event.

Abstract: A method of detecting a cardiac event that includes sensing cardiac signals from a plurality of electrodes, determining rates of change of the sensed cardiac signals, and determining a range of the sensed cardiac signals. The sensed cardiac signals are detected as being associated with the cardiac event in response to the determined rates of change and the determined range.

Abstract: A system and method are provided for assessing T-wave alternans (TWA) using cardiac EGM signals received from implanted electrodes. A T-wave signal parameter is measured from signals received by an automatic gain control sense amplifier. A TWA measurement is computed from a beat-by-beat comparison of T-wave parameter measurements or using frequency spectrum techniques. The TWA measurement magnitude and measurement conditions are used in detecting a clinically important TWA. TWA assessment further includes discriminating concordant and discordant TWA in a multi-vector TWA assessment, and determining the association of a TWA measurement with QRS alternans, mechanical alternans, and other physiological events. A prediction of a pathological cardiac event is made in response to a TWA assessment. A response to a cardiac event prediction is provided.

Abstract: A system and method are provided for assessing T-wave alternans (TWA) using cardiac EGM signals received from implanted electrodes. A T-wave signal parameter is measured from signals received by an automatic gain control sense amplifier. A TWA measurement is computed from a beat-by-beat comparison of T-wave parameter measurements or using frequency spectrum techniques. The TWA measurement magnitude and measurement conditions are used in detecting a clinically important TWA. TWA assessment further includes discriminating concordant and discordant TWA in a multi-vector TWA assessment, and determining the association of a TWA measurement with QRS alternans, mechanical alternans, and other physiological events. A prediction of a pathological cardiac event is made in response to a TWA assessment. A response to a cardiac event prediction is provided.

Abstract: Techniques for detection and treatment of myocardial ischemia are described that monitor both the electrical and dynamic mechanical activity of the heart to detect and verify the occurrence of myocardial ischemia in a more reliable manner. The occurrence of myocardial ischemia can be detected by monitoring changes in an electrical signal such as an ECG or EGM, and changes in dynamic mechanical activity of the heart. Dynamic mechanical activity can be represented, for example, by a heart acceleration signal or pressure signal. The electrical signal can be obtained from a set of implanted or external electrodes. The heart acceleration signal can be obtained from an accelerometer or pressure sensor deployed within or near the heart. The techniques correlate contractility changes detected by an accelerometer or pressure sensor with changes in the ST electrogram segment detected by the electrodes to increase the reliability of ischemia detection.

Abstract: A method and apparatus for detection of changes in physiologic parameters of a patient that includes generating measured physiologic parameters, generating an adaptive baseline trend of the measured physiologic parameters corresponding to a first time period, generating a short term trend of the measured physiologic parameters corresponding to a second time period less than the first time period, and generating a metric of physiologic parameter change between the adaptive baseline trend and one of a most recent measured physiologic parameter and the short term trend of the measured physiologic parameters.

Abstract: An implantable cardiac stimulation device capable of delivering ESS, monitoring for myocardial ischemia and responding to the detection of myocardial ischemia by modifying the delivery of ESS. Modification of ESS delivery may include disabling ESS, initiating ESS, and/or modifying ESS control parameters.

Abstract: In using electrogram signals to determine physiologic conditions like ischemia, the bad cardiac cycle information due to noise, axis shifts in the cardiac electrical axis, and the like must be removed if the electrogram signal can be made to be a good indicator. If this is accomplished through the adaptive filtering techniques shown here, the signal can be used to drive a closed loop therapy system responsive to those physiologic conditions discernible from good cardiac cycle electrocardiogram signals.

Abstract: In determining whether a patient has ischemia or other conditions discernible in the variation occurring in the ST portion of the electrocardiogram signal, we filter out bad ST change parameters that are not changing at a rate representative of human ischemia ST change parameter rates of change. This can be used for driving therapy systems to alleviate cardiac ischemia. This filtering can be enhanced by using multiple cardiac electrical vectors for the electrogram signal vectors, and using a determination of Axis shift to modify filter parameters and the expected ranges of precursors to the ST change parameter (an ST change variable) to eliminate bad cardiac cycles, that is cardiac cycle information that may be misleading.

Abstract: In using electrogram signals to determine physiologic conditions like ischemia, the bad cardiac cycle information due to noise, axis shifts in the cardiac electrical axis, and the like must be removed if the electrogram signal can be made to be a good indicator. If this is accomplished through the adaptive filtering techniques shown here, the signal can be used to drive a closed loop therapy system responsive to those physiologic conditions discernible from good cardiac cycle electrocardiogram signals.