Efficacy of biological state and action affecting biological state, judging apparatus, judging system, judging program and recording medium holding the program

• US 6,954,700 B2
• Filed: 04/25/2002
• Issued: 10/11/2005
• Est. Priority Date: 04/25/2001
• Status: Expired due to Fees

• Current Assignee: Bio-Complex System Research Institute, Computer Convenience Incorporated
• Original Assignee: Bio-Complex System Research Institute, Computer Convenience Incorporated
• Inventors: Miao, Tiejun, Higashida, Gesshi, Asaoka, Hideyuki, Miyazaki, Wasei, Shimizu, Toshiyuki
• Primary Examiner(s): Assouad, Patrick J.
• Application Number:
  US10/474,507
• Publication Number:
  US 20040137639A1
• Time in Patent Office: 1,265 Days
• Field of Search: 702/19, 600/300, 600/301, 600/481, 600/485, 600/508, 600/509, 600/515, 600/529, 600/544, 600/518, 600/519, 607/2, 607/5, 706/924, 706/20, 706/22
• US Class Current: 702/19
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass
• CPC Class Codes: Y10S 706/924 Medical
• CPC Class Codes: Y10T 436/25875 Gaseous sample or with change of physical state

Abstract

A method of judging a biological state comprising using: (1) correlation and/or symmetry between Lyapunov exponent and entropy; and/or (2) Higuchi fractal dimension, wherein the Lyapunov exponent, entropy and Higuchi fractal dimension are indices that can express chaotic nature and derived from time series data of biological signals from a subject.

Claims

1. A method of judging a biological state comprising using at least correlation or symmetry between Lyapunov exponent and entropy,

wherein the Lyapunov exponent and entropy are indices that can express chaotic nature and are derived from time series data of biological signals from a subject, comprising:

the step of analyzing at least the correlation or the symmetry between the Lyapunov exponent and the entropy derived from time series data of biological signals from a subject; and

the step of judging the biological state based on the result of analyzing at least the correlation or symmetry.

2. A method of judging a biological state using at least correlation between Lyapunov exponent and entropy,

wherein the Lyapunov exponent and entropy are indices that can express chaotic nature and are derived from time series data of biological signals from a subject, wherein the correlation between the Lyapunov exponent and the entropy is analyzed using DFA method (Detrended Fluctuation Analysis).

3. A method of judging a biological state using at least correlation between Lyapunov exponent and entropy,

wherein the Lyapunov exponent and entropy are indices that can express chaotic nature and are derived from time series data of biological signals from a subject, wherein the correlation between the Lyapunov exponent and the entropy is analyzed using the DFA method, which comprises the step of performing microscopic DFA analysis and the step of performing macroscopic DFA analysis.

4. A method of judging a biological state using at least symmetry between Lyapunov exponent and entropy,

wherein the Lyapunov exponent and entropy are indices that can express chaotic nature and are derived from time series data of biological signals from a subject, wherein the correlation between the Lyapunov exponent and the entropy is analyzed using any method selected from the group consisting of M-symmetry method, Wavelet analysis method and a method based on Mirror value.

5. A method of judging a biological state using at least correlation or symmetry between Lyapunov exponent and entropy,

wherein the Lyapunov exponent and entropy are indices that can express chaotic nature and are derived from time series data of biological signals from a subject, comprising the steps of:

calculating two or more Lyapunov exponents and two or more entropies from time series data of biological signals from a subject and determining the maximum values among the Lyapunov exponents as a maximum Lyapunov exponents;

determining offset values (Os) corresponding to the maximum Lyapunov exponents and the entropies;

calculating Mirror values from the maximum Lyapunov exponents and the entropies; and

calculating weighted Lyapunov exponents from the Mirror values and the offset values.

6. A method of judging a biological state using at least Higuchi fractal dimension,

wherein Higuchi fractal dimension is an index that can express chaotic nature and is derived from time series data of biological signals from a subject, comprising using a ratio of two Higuchi fractal dimensions (D1/D2) or a F-constant as the Higuchi fractal dimension.

19. The method according to claim 6 comprising judging a calculation result of time series data of biological signals from the subject using an Artificial Neural Network.

7. A method of judging a biological state using at least Higuchi fractal dimension,

wherein Higuchi fractal dimension is an index that can express chaotic nature and is derived from time series data of biological signals from a subject, comprising the step of calculating a ratio of Higuchi fractal dimensions (D1/D2) by analyzing time series data of a biological signal from a subject using F-symmetry method, wherein the biological state is judged based on at least the ratio of Higuchi fractal dimensions (D1/D2).

8. A method of judging a biological state using:

(1) correlation or symmetry between Lyapunov exponent and entropy; and

(2) Higuchi fractal dimension,

wherein the Lyapunov exponent, entropy and Higuchi fractal dimension are indices that can express chaotic nature and are derived from time series data of biological signals from a subject wherein the time series data of a biological signal are obtained from a pulse wave.

9. A method of judging efficacy of an action that may affect a biological state comprising the steps of:

judging the biological state at a certain point of time according to at least correlation or symmetry between Lyapunov exponent and entropy,

wherein the Lyapunov exponent and entropy are indices that can express chaotic nature and are derived from time series data of biological signals from a subject;

taking the action that may change the biological state;

analyzing time series data of biological signal after the action to determine correlation and/or symmetry between Lyapunov exponent and entropy; and

comparing at least the correlation or the symmetry between Lyapunov exponent and entropy before the action with at least the correlation or the symmetry between Lyapunov exponent and entropy after the action to judge whether an influence of the action on a living body is positive, negative or neutral (unchanging).

10. The method according to claim 9, wherein the action is selected from the group consisting of preventive or therapeutic actions on diseases selected from medication, dialysis, radiation or laser irradiation, treatment by electric or magnetic pulse, rehabilitation, ingestion of food that may ameliorate a biological state, and producing a physical or mental stimulus.

11. A method of judging a biological state using at least correlation or symmetry between Lyapunov exponent and entropy,

wherein the Lyapunov exponent and entropy are indices that can express chaotic nature and are derived from time series data of biological signals from a subject, comprising the steps of:

calculating two or more Lyapunov exponents and two or more entropies from time series data of biological signals from a subject and determining the maximum values among the Lyapunov exponents as maximum Lyapunov exponents; and

calculating scaling coefficients and wavelet coefficients from the maximum Lyapunov exponents and the entropies.

12. A method of judging a biological state using at least correlation or symmetry between Lyapunov exponent and entropy,

wherein the Lyapunov exponent and entropy are indices that can express chaotic nature and are derived from time series data of biological signals from a subject, comprising judging a calculation result of time series data of biological signals from the subject using an Artificial Neural Network.

13. A system for judging a biological state comprising a measuring apparatus to collect time series data of biological signals from a subject and a judging apparatus for judging the biological state based on the time series data,

wherein the measuring apparatus transmits the time series data via a communication line;

and

the judging apparatus calculates from the transmitted time series data at least correlation or symmetry between Lyapunov exponent and entropy as indices that can express chaotic nature, and uses the calculation result to perform a judgement, wherein the judging apparatus transmits a result of the judgement to the measuring apparatus.

14. A program for judging a biological state comprised in an apparatus for judging a biological state of a subject based on time series data of biological signals from the subject,

wherein a function of calculating at least correlation or symmetry between Lyapunov exponent and entropy as indices that can express chaotic nature from the time series data is fulfilled by the apparatus further fulfilling the functions of:

determining maximum values among the two or more Lyapunov exponents as maximum Lyapunov exponents;

determining offset values corresponding to the maximum Lyapunov exponents and the entropies;

calculating Mirror values from the maximum Lyapunov exponents and the entropies; and

calculating weighted Lyapunov exponents from the Mirror values and the offset values.

18. A computer-readable recording medium holding the judging program according to any one of claims 14 to 17.

15. A program for judging a biological state comprised in an apparatus for judging a biological state of a subject based on time series data of biological signals from the subject,

wherein a function of calculating at least correlation or symmetry between Lyapunov exponent and entropy as indices that can express chaotic nature from the time series data is fulfilled by the apparatus further fulfilling the functions of:

determining maximum values among the two or more Lyapunov exponents as maximum Lyapunov exponents; and

calculating scaling coefficients and wavelet coefficients from the maximum Lyapunov exponents and the entropies.

16. A program for judging a biological state comprised in an apparatus for judging a biological state of a subject based on time series data of biological signals from the subject,

wherein a function of calculating at least correlation or symmetry between Lyapunov exponent and entropy as indices that can express chaotic nature from the time series data is fulfilled by the apparatus further fulfilling the function of performing macroscopic DFA method and microscopic DFA method with respect to the Lyapunov exponent and the entropy.

17. A program for judging a biological state comprised in an apparatus for judging a biological state of a subject based on time series data of biological signals from the subject,

wherein a function of calculating at least Higuchi fractal dimension as an index that can express chaotic nature from the time series data is fulfilled by the apparatus, fulfilling the function of calculating Higuchi fractal dimension and F-constant from the time series data.

20. A system for judging a biological state comprising a measuring apparatus to collect time series data of biological signals from a subject and a judging apparatus for judging the biological state based on the time series data,

wherein the measuring apparatus calculates from the transmitted time series data at least correlation or symmetry between Lyapunov exponent and entropy as indices that can express chaotic nature and transmits the calculation result via a communication line;

and

the judging apparatus uses the calculation result to perform a judgement,

wherein the judging apparatus transmits a result of the judgement to the measuring apparatus.