Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Electrical signal parameter measurement system
Reexamination Certificate
1998-09-03
2001-07-24
Shah, Kamini (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Electrical signal parameter measurement system
C702S079000, C128S098100
Reexamination Certificate
active
06266624
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention is directed to computerized method, i.e., a method conducted in a computer, for classification of a time series having a prescribable number of samples, such as an electrical signal.
2. Description of the Prior Art
In many technical fields wherein it is of interest to draw conclusions about the future behavior of the time series from measured time series. The prediction of the future “behavior” of the time series ensues given the assumption that the time series comprises non-linear correlations between the samples of the time series.
This problem also obtains to considerable significance in various medical fields, for example in cardiology. Specifically in the problem area of sudden cardiac death, it can be vital to recognize early warning signs of sudden cardiac death in order to initiate counter-measures against the occurrence of sudden cardiac death as early as possible.
It is known that a time series of an electrocardiogram that is not correlated describes a heart that is not at risk with respect to sudden cardiac death. A heart at risk with respect to sudden cardiac death is described by a time series of the electrocardiogram that comprises non-linear correlations between the samples of the time series (G. Morfill, “Komplexitätsanalyse in der Kardiologie, Physikalische Blätter,” Vol. 50, No. 2, pp. 156-160, (1994)). It is also known from this Morfill article to determine time series of an electrocardiogram that describe hearts that are at risk with respect to sudden cardiac death from the graphic phase space presentation (Fourier transformation) of two successive heartbeats.
The method disclosed in this Morfill article exhibits all of the disadvantages that are typical of empirical methods. In particular, the error susceptibility of graphic interpretations by a human, the problem of setting a threshold from which a time series is classified as at risk, as well as imprecisions in the presentation of the Fourier transform on the picture screen are considered disadvantageous in the known method.
Further, methods for determining stochastic, conditioned entropies are known from W. Ebeling et al., “Entropy, Transinformation and Word Distribution of Information-Carrying Sequences,” International Journal of Bifurcation and Chaos, Vol. 5, No. 1, pp. 51-61, (1995) and D. Wolpert et al., “Estimation Functions of Probability Distributions from a Finite Set of Samples,” Physical Review E, Vol. 52, No. 6, pp. 6841-6854, (December 1995).
LICOX, GMS, Gesellschaft für Medizinische Sondentechnik mbH, Advanced Tissue Monitoring discloses a method with which the time curve of the local oxygen voltage of the brain (tip02) can be determined.
German OS 39 12 028 discloses a method and an arrangement for comparing wave shapes of time-variable signals.
SUMMARY OF THE INVENTION
An object of the present invention is to provide a method in order to quickly and dependably classify a time series that contains a prescribable number of samples with the assistance of a computer.
The above object is achieved in a method conducted in a computer for classification of a time series that contains a prescribable number of samples, such as an electrical signal, by determining, in the computer, conditioned entropies for the prescribable number of samples contained in the time series, with at least one information flow for a prescribable number of future sampling times being determined in the computer from the conditioned entropies, and wherein a classification of the time series is implemented in the computer on the basis of the information flow.
In the inventive method, conditioned entropies are determined for a prescribable plurality of samples. An information flow for a prescribable plurality of future sampling points with reference whereto the time series is classified is determined from the conditioned entropies.
It is possible to speed up the classification with the method according to patent claim
5
since only a binary classification has to be implemented on the basis of the shape of the graph of the information flow. The classification of the time series into a first time series type and into a second time series type can be very simply implemented since the first time series type is classified when the graph of the information flow exhibits an approximately curved shape.
It is also advantageous to utilize the method for a time series that is made available by a measured electrocardiogram signal (ECG). A classification of the time series into an electrocardiogram signal (ECG) that describes a heart at risk with respect to sudden cardiac death as well as into an electrocardiogram signal (ECG) of a heart not at risk is possible with the determination of stochastic correlations between the samples of the time series. As a result, it is possible to recognize a risk early and to initiate a treatment against sudden cardiac death.
REFERENCES:
patent: WO009725676 (1997-07-01), None
patent: WO009733238 (1997-02-01), None
“Komolexitätsanalyse in der Kardiologie,”Morfill, Physikalische Blätter, vol. 50, No. 2, pp. 156-160, (1994.
“Entropy, Transinformation and Word Distribution of Information-Carrying Sequences,” Ebeling et al., International Journal of Bifurcation and Chaos, vol. 5, No. 1, pp. 51-61, (1995).
Estimation Functions of Probability Distributions From a Finite Set of Samples, Wolpert et al., Physical Review E, vol. 52, No. 6, pp. 6841-6854, (Dec. 1995).
LICOX, GMS, Gesellschaft für Medizinische Sondentechnik mbH, Advanced Tissue Monitoring, no date.
“Time Series Analysis of Complex Dynamics in Physiology and Medicine,” Glass et al., Medical Progress Through Technology, vol. 19, No. 3, pp. 115-128 (1993).
Deco Gustavo
Schurmann Bernd
Schiff & Hardin & Waite
Shah Kamini
Siemens Aktiengesellschaft
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