Abstract: An implantable vagus nerve stimulation (VNS) system includes a sensor configured to measure ECG data for a patient, a stimulation subsystem configured to deliver VNS to the patient, and a control system configured to perform a heart rate variability analysis with the ECG data. In some aspects, performing the heart rate variability analysis includes measuring R-R intervals between successive R-waves for the ECG data measured during a stimulation period of VNS therapy and a baseline period of the VNS therapy, wherein the stimulation period comprises at least a portion of the ON period and the baseline period comprises at least a portion of the OFF period immediately preceding the ON period, and generating a plot of each R-R interval against an immediately successive R-R interval for each of the stimulation period and the baseline period configured to be displayed on a display.

Abstract: An implantable vagus nerve stimulation (VNS) system includes a sensor configured to measure ECG data for a patient, a stimulation subsystem configured to deliver VNS to the patient, and a control system configured to perform a heart rate variability analysis with the ECG data. In some aspects, performing the heart rate variability analysis includes measuring R-R intervals between successive R-waves for the ECG data measured during a stimulation period of VNS therapy and a baseline period of the VNS therapy, wherein the stimulation period comprises at least a portion of the ON period and the baseline period comprises at least a portion of the OFF period immediately preceding the ON period, and generating a plot of each R-R interval against an immediately successive R-R interval for each of the stimulation period and the baseline period configured to be displayed on a display.

Abstract: An implantable vagus nerve stimulation (VNS) system includes a sensor configured to measure ECG data for a patient, a stimulation subsystem configured to deliver VNS to the patient, and a control system configured to perform a heart rate variability analysis with the ECG data. In some aspects, performing the heart rate variability analysis includes measuring R-R intervals between successive R-waves for the ECG data measured during a stimulation period and a baseline period, plotting each R-R interval against an immediately preceding R-R interval for each of the stimulation period and the baseline period, and determining at least one of a standard deviation from an axis of a line perpendicular to an identity line for each of the stimulation period plot and the baseline period plot or a centroid of each of the stimulation period plot and the baseline period plot.

Abstract: Disclosed herein are example methods and systems for predicting efficacy of pacemakers or cardiac resynchronization therapy (CRT) devices prior to implantation in patients based on electrocardiogram (ECG) heterogeneity analysis. A method of determining or predicting efficacy of implanting a pacemaker or cardiac resynchronization therapy (CRT) device in a patient includes receiving a first set of electrocardiogram (ECG) signals associated with the patients heart from spatially separated leads, analyzing data from the first set of ECG signals, quantifying a spatio-temporal heterogeneity of the first set of ECG signals based on the analysis, and determining or predicting efficacy of implanting the pacemaker or cardiac re-synchronization therapy (CRT) device in the patient based on the quantified spatio-temporal heterogeneity.

Abstract: A method and system for high-throughput detection of coronary artery stenosis observes trends in abnormal or pathologic morphology of the electrocardiogram (ECG). A first set of ECG signals is monitored from a patient. A baseline measurement is generated from the monitored first set of ECG signals to contain nonpathologic ECG morphologies in each lead. A second set of ECG signals is monitored from the patient and a second mean measurement during or after stress is generated from the second set of ECG signals. A residuum signal is generated for each lead based on the baseline measurement and the second mean measurement. The residuum signals are averaged across the leads for each timepoint. T-wave heterogeneity is quantified based on the generated residuum signals and the averaged residuum signal at each timepoint, and used to detect coronary artery stenosis.

Abstract: An implantable vagus nerve stimulation (VNS) system includes a sensor configured to measure ECG data for a patient, a stimulation subsystem configured to deliver VNS to the patient, and a control system configured to perform a heart rate variability analysis with the ECG data. In some aspects, performing the heart rate variability analysis includes measuring R-R intervals between successive R-waves for the ECG data measured during a stimulation period and a baseline period, plotting each R-R interval against an immediately preceding R-R interval for each of the stimulation period and the baseline period, and determining at least one of a standard deviation from an axis of a line perpendicular to an identity line for each of the stimulation period plot and the baseline period plot or a centroid of each of the stimulation period plot and the baseline period plot.

Abstract: A method and system for high-throughput prediction of the onset of heart arrhythmias observes trends in abnormal or pathologic morphology of the electrocardiogram (ECG). A first set of ECG signals is monitored from a patient. A baseline measurement is generated from the monitored first set of ECG signals to contain nonpathologic ECG morphologies in each lead. A second set of ECG signals is monitored from the patient and a second baseline measurement is generated from the second set of ECG signals. A residuum signal is generated for each lead based on the baseline measurement and the second baseline measurement. The residuum signals are averaged across the leads. R-wave heterogeneity, T-wave heterogeneity, P-wave heterogeneity, or ST-segment heterogeneity or other indicators of arrhythmia risk or myocardial ischemia are quantified based on the generated residuum signals and the averaged residuum signal.

Abstract: A method and system for high-throughput prediction of the onset of heart arrhythmias observes trends in abnormal or pathologic morphology of the electrocardiogram (ECG). A first set of ECG signals is monitored from a patient. A baseline measurement is generated from the monitored first set of ECG signals to contain nonpathologic ECG morphologies in each lead. A second set of ECG signals is monitored from the patient and a second baseline measurement is generated from the second set of ECG signals. A residuum signal is generated for each lead based on the baseline measurement and the second baseline measurement. The residuum signals are averaged across the leads. R-wave heterogeneity, T-wave heterogeneity, P-wave heterogeneity, or ST-segment heterogeneity or other indicators of arrhythmia risk or myocardial ischemia are quantified based on the generated residuum signals and the averaged residuum signal.

Abstract: A method and system for predicting the onset of heart arrhythmias more accurately observes trends in abnormal or pathologic morphology of the electrocardiogram (ECG). A first set of ECG signals is monitored from a patient. A baseline measurement is generated from the monitored first set of ECG signals to contain nonpathologic ECG morphologies in each lead. A second set of ECG signals is monitored from the patient and the baseline measurement is subtracted from the second set of ECG signals on a beat-to-beat basis. Afterwards, a residuum signal is generated for each lead based on the subtraction. R-wave heterogeneity, T-wave heterogeneity, P-wave heterogeneity, or ST-segment heterogeneity or other indicators of arrhythmia risk or myocardial ischemia are quantified based on the generated residuum signals.

Abstract: A method and system for predicting the onset of heart arrhythmias more accurately observes trends in abnormal or pathologic morphology of the electrocardiogram (ECG). A first set of ECG signals is monitored from a patient. A baseline measurement is generated from the monitored first set of ECG signals to contain nonpathologic ECG morphologies in each lead. A second set of ECG signals is monitored from the patient and the baseline measurement is subtracted from the second set of ECG signals on a beat-to-beat basis. Afterwards, a residuum signal is generated for each lead based on the subtraction. R-wave heterogeneity, T-wave heterogeneity, P-wave heterogeneity, or ST-segment heterogeneity or other indicators of arrhythmia risk or myocardial ischemia are quantified based on the generated residuum signals.

Abstract: A system and method for quantifying alternation in the T-wave and ST segment of an ECG signal receives a digitized ECG signal (i.e., ECG data) for processing. The ECG data are used to calculate an odd median complex for the odd beats in the ECG data and an even median complex for the even beats in the ECG data. The odd median complex and the even median complex are then compared to obtain an estimate of the amplitude of beat-to-beat alternation in the ECG signal. Prior to calculation of the even and odd median complexes, the ECG data are filtered. Filtering of the ECG data involves low pass filtering the ECG data to remove high frequency noise, applying a baseline wander removal filter to the ECG data to remove low frequency artifacts, removing arrhythmic beats from the ECG data, and eliminating noisy beats from the ECG data. The filtered data are more suitable for calculation of an accurate estimate of alternation.

Abstract: The non-invasive, dynamic tracking and diagnosing of cardiac vulnerability to ventricular fibrillation involves analysis of both cardiac electrical stability and the influence of autonomic activity. The magnitude of alternation in an electrocardiogram is indicative of cardiac electrical stability. Alternans are made manifest by applying a physiologic stress such as exercise or behavioral stress to a subject.

Abstract: A method and an apparatus for the non-invasive, dynamic tracking and diagnosing of cardiac vulnerability to ventricular fibrillation are disclosed. T-wave alternans and heart rate variability are simultaneously evaluated. T-wave alternation is an absolute predictor of cardiac electrical stability. Heart rate variability is a measure of autonomic influence, a major factor in triggering cardiac arrhythmias. By simultaneously analyzing both phenomena, the extent and cause of cardiac vulnerability can be assessed.

Abstract: A method and apparatus for predicting cardiac electrical instability simultaneously assesses T-Wave Alternans and QT Interval Dispersion. T-wave alternation is an excellent predictor of cardiac electrical instability but can be influenced by mechano-electrical coupling. Thus, a measure of alternation has a high degree of sensitivity but a low degree of specificity. The low specificity of alternation is addressed by simultaneously analyzing QT interval dispersion. Dispersion is not a measure of excitable stimulus and is not sensitive to mechano-electrical coupling. The resulting combination of alternans and dispersion yields an accurate predictor of cardiac electrical instability caused by intrinsic factors.

Abstract: A method and apparatus for predicting susceptibility to sudden cardiac death simultaneously assessing cardiac electrical stability and autonomic influence. Cardiac electrical stability is assessed by analyzing at least one of a beat-to-beat alternation in a T-wave of an ECG of a patient's heart and dispersion of repolarization in the ECG of the patient's heart. Autonomic influence on the patient's heart is assessed by analyzing at least one of a magnitude of heart rate variability in the ECG of the patient's heart and baroreceptor sensitivity.

Abstract: A method and apparatus for the non-invasive, dynamic tracking and diagnosing of cardiac vulnerability to ventricular fibrillation are disclosed. T-wave alternans and heart rate variability are simultaneously evaluated. T-wave alternation is an absolute predictor of cardiac electrical instability. Heart rate variability is a measure of autonomic influence, a major factor in triggering cardiac arrythmias. By simultaneously analyzing both phenomena, the extent and cause of cardiac vulnerability can be assessed. The method includes the following steps. An ECG signal is sensed from a heart. The T-wave portions of the ECG signal are analyzed to estimate an amplitude of beat-to-beat alternation. The amplitude of beat-to-beat alternation represents cardiac electrical instability. The R-R intervals are analyzed to estimate a magnitude of a high frequency component of heart rate variability and to estimate a magnitude of a low frequency component of heart rate variability.

Abstract: A non-invasive method for dynamic tracking of cardiac vulnerability to ventricular fibrillation is disclosed. A heart is monitored to sense an ECG signal. The sensed ECG signal is then amplified and low-pass filtered before it is digitally sampled and stored. The location of the T-wave in each R-R interval (pulse) of the ECG is estimated and each T-wave is partitioned into a plurality of time divisions. The sampled ECG signal in each of the time divisions is summed together and a time series is formed for each of the time divisions such that each time series includes corresponding sums from corresponding time divisions from successive T-waves. Each time series is detrended in order to eliminate the effects of drift and DC bias, and then a method of dynamic estimation is performed on each time series to estimate the amplitude of alternation for each time division.