Abstract: An automated external defibrillator (AED) and methods for a corrective CPR prompting system. The AED includes a sensor that obtains compression measurement data of CPR chest compressions and a control system including a microprocessor programmed to run a non-parametric, Information-Theoretic analysis of the compression measurement data. The analysis includes ranking provided compression measurement data, determining a prompt time TN for review, locating the compression measurement data at TN in an initial expected histogram of depth and rate aspects of the compression data measurements with upper and lower limits, that divides the intervals of the histogram into a plurality of sections, weighting the compression measurement data based on a plurality of factors, deriving information content of the compression measurement data by mapping a probability density function into an information content function, and determining if a particular corrective prompt is necessary.

Abstract: A method and apparatus utilizing a piecewise stitching adaptive algorithm (PSAA) to filter signal artifacts, such as those induced by cardiopulmonary resuscitation (CPR) from sensed signals in real-time. PSAA is a method of estimating artifact component present in a first signal that is highly correlated with a second signal. The PSAA may utilize autocorrelation and cross-correlation calculations to determine signal sample windows in the first and second signals. The PSAA may estimate a signal artifact in a primary signal segment based on the determined correlations between the primary signal and an artifact signal. The PSAA may remove the estimated signal artifact from the primary signal. In the absence of an artifact signal, PSAA is able to estimate artifacts in the first signal utilizing filters. The PSAA may be implemented in Automated External Defibrillators, Monitor Defibrillators or other devices capable sensing highly correlated signals such as, for example, ECG and CPR signals.

Abstract: A method and apparatus utilizing a piecewise stitching adaptive algorithm (PSAA) to filter signal artifacts, such as those induced by cardiopulmonary resuscitation (CPR) from sensed signals in real-time. PSAA is a method of estimating artifact component present in a first signal that is highly correlated with a second signal. The PSAA may utilize autocorrelation and cross-correlation calculations to determine signal sample windows in the first and second signals. The PSAA may estimate a signal artifact in a primary signal segment based on the determined correlations between the primary signal and an artifact signal. The PSAA may remove the estimated signal artifact from the primary signal. In the absence of an artifact signal, PSAA is able to estimate artifacts in the first signal utilizing filters. The PSAA may be implemented in Automated External Defibrillators, Monitor Defibrillators or other devices capable sensing highly correlated signals such as, for example, ECG and CPR signals.

Abstract: A system to detect the presence or absence of T wave alternans is described based on statistical tests and periodicity transform. T wave and ST segment boundaries are detected in multi-lead ECG signals acquired from the regular clinical leads. Once the fiducial point and the above boundaries are delineated, computation of regular parameters like T wave amplitude, area under the T waves or segments of T wave, ST segment slope and/or the curvature of T wave are performed. Each parameter forms a rolling array of values with each successive beat. The array of values, or the time series, is used to make the decision about the T wave alternans. Two different methods are employed based on periodicity transforms and statistical tests. A set of numerical values (e.g. norm of the projection on to p-2 space, sums of adjacent terms after the trend removal, t-value, and number of deviations from alternans pattern) are all computed and compared to threshold values.

Abstract: A system to detect the presence or absence of T wave alternans is described based on statistical tests and periodicity transform. T wave and ST segment boundaries are detected in multi-lead ECG signals acquired from the regular clinical leads. Once the fiducial point and the above boundaries are delineated, computation of regular parameters like T wave amplitude, area under the T waves or segments of T wave, ST segment slope and/or the curvature of T wave are performed. Each parameter forms a rolling array of values with each successive beat. The array of values, or the time series, is used to make the decision about the T wave alternans. Two different methods are employed based on periodicity transforms and statistical tests. A set of numerical values (e.g. norm of the projection on to p-2 space, sums of adjacent terms after the trend removal, t-value, and number of deviations from alternans pattern) are all computed and compared to threshold values.