Abstract: A data processing environment processes a plurality of signals to detect a system state. The specific system can be, for example, a living human, where the signals are biological parameters. A processor operates the data points from two signals by defining respective classes of contiguous runs of a prescribed length of the data. The processor then assigns quantitative values to measure the regularity and stability of similar patterns between the first and second sets of classes of data points from the defined classes. These assigned quantitative values are aggregated to quantify the degree of asynchrony or conditional irregularity between the signals.

Abstract: A quantification of approximate entropy is determined on a set of data by comparing subsets of the data. The comparison reveals the regularity and stability of similar patterns amongst subsets of the data. The comparisons perform such that the contribution of noise to measurement of the regularity and stability is minimized. Quantitative values are assigned to measure the degree of regularity and stability. From these quantitative values a single output measure is generated indicative of the amount of patternness of the sequence of data. The calculations required to determine this approximate entropy are preferably performed within a data processing system. Numerous peripheral devices may be attached to such a data processing system. The types of data for which the approximate entropy may be calculated include any sets of data wherein the amount of patternness is sought.

Abstract: A quantification of approximate entropy is determined on a set of data by comparing subsets of the data. The comparison reveals the regularity and stability of similar patterns amongst subsets of the data. The comparisons perform such that the contribution of noise to measurement of the regularity and stability is minimized. Quantitative values are assigned to measure the degree of regularity and stability. From these quantitative values a single output measure is generated indicative of the amount of patternness of the sequence of data. The calculations required to determine this approximate entropy are preferably performed within a data processing system. Numerous peripheral devices may be attached to such a data processing system. The types of data for which the approximate entropy may be calculated include any sets of data wherein the amount of patternness is sought.

Abstract: An approximation of entropy is determined on a set of data by comparing subsets of the data. The comparison reveals the regularity and stability of similar patterns amongst subsets of the data. The comparisons perform such that the contribution of noise to measurement of the regularity and stability is minimized. Quantitative values are assigned to measure the degree of regularity and stability. From these quantitative values a single output measure is generated indicative of the amount of patternness of the sequence of data. The calculations required to determine this approximate entropy are preferably performed within a data processing system. Numerous peripheral devices may be attached to such a data processing system. The types of data for which the approximate entropy may be calculated include any sets of data wherein the amount of patternness is sought.