Method and system for detecting and/or predicting cerebral disorders

• US 7,276,026 B2
• Filed: 01/29/2004
• Issued: 10/02/2007
• Est. Priority Date: 01/29/2003
• Status: Expired due to Fees

• Current Assignee: Nonlinear Medicine Incorporated
• Original Assignee: Nonlinear Medicine Incorporated
• Inventors: Skinner, James
• Primary Examiner(s): MANUEL, GEORGE C
• Application Number:
  US10/767,861
• Publication Number:
  US 20040225202A1
• Time in Patent Office: 1,342 Days
• Field of Search: 600/509, 600/544, 600/545, 600/300, 435/7.9, 128/920
• US Class Current: 600/300
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• 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

Abstract

Cerebral disorders are detected and/or predicted by analyzing input biological or physical data using a data processing routine. The data processing routine includes a set of application parameters associated with biological data correlating with the biological anomalies. The data processing routine uses an algorithm to produce a data series, e.g., a PD2i data series. The data series is used to detect or predict the onset of the cerebral disorders. To reduce noise in the data series, the slope is set to a predetermined number if it is less than a predetermined value. To further reduce noise, a noise interval within the data series is determined and, if the noise interval is within a predetermined range, the data series is divided by another predetermined number, and new values are produced for the data series.

Claims

1. A method of detecting or predicting a cerebral disorder, comprising the steps of:

analyzing input biological or physical data using a data processing routine including a set of application parameters associated with biological data correlating with the cerebral disorder to produce a data series,

to reduce noise in the data series determining whether a slope of the data series is smaller than a predetermined value;

if the slope is less than a predetermined value, setting the slope to a predetermined number; and

using the data series to detect or predict the onset of the cerebral disorder.

2. The method of claim 1, wherein the cerebral disorder is bovine spongioform encephalitis.

3. The method of claim 1, wherein the cerebral disorder is Alzheimer'"'"'s disease.

4. The method of claim 1, wherein the data processing routine uses the following algorithm to produce a data series PD2i:

PD2i ⊂

log C(n, r, nref*)/log r

where ⊂

means scales as, C is the correlation integral for PD2i in which n equals the data length, r equals the scaling range, and nref* equals a location of the reference vector for estimating the scaling region slope of log C/log r in a restricted small log-r range that is devoid of the effects of non-stationary data.

5. The method of claim 1, wherein the predetermined value is approximately 0.5.

6. The method of claim 1, wherein the predetermined number is zero.

7. The method of claim 1, further comprising:

determining a noise interval within the data series; and

if the noise interval is within a predetermined range, dividing the data series by another predetermined number and repeating the step of analyzing to produce new values for the data series.

8. The method of claim 7, wherein the other predetermined number is two.

9. The method of claim 7, wherein the predetermined range is −

x to x, where x is any number.

10. The method of claim 9, wherein the predetermined range is −

5 to 5.

11. The method of claim 1, wherein the input biological or physical data includes electrophysiological data.

12. A method of detecting or predicting cerebral disorder, comprising the steps of:

analyzing input biological or physical data using a data processing routine including a set of application parameters associated with biological data correlating with the cerebral disorder to produce a data series;

determining a noise interval within the data series; and

if the noise interval is outside a predetermined range, dividing the data series by a predetermined number and repeating the step of analyzing to produce new values for the data series;

or

if the noise interval is within the predetermined range, using the data series to detect or predict the onset of cerebral disorder.

13. The method of claim 12, wherein the cerebral disorder is bovine spongioform encephalitis.

14. The method of claim 12, wherein the cerebral disorder is Alzheimer'"'"'s disease.

15. The method of claim 12, wherein the data processing routine uses the following algorithm to produce a data series PD2i:

PD2i ⊂

log C(n, r, nref*)/log r

where ⊂

log means scales as, C is the correlation integral for PD2i in which n equals the data length, r equals the scaling range, and nref* equals a location of the reference vector for estimating the scaling region slope of log C/log r in a restricted small log-r range that is devoid of the effects of non-stationary data.

16. The method of claim 12, wherein the predetermined number is two.

17. The method of claim 12, wherein the predetermined range is −

x to x, where x is any number.

18. The method of claim 17, wherein the predetermined range is −

5 to 5.

19. The method of claim 12, further comprising:

determining whether a slope of the data series is smaller than a predetermined value; and

if the slope is less than a predetermined value, setting the slope to another predetermined number.

20. The method of claim 19, wherein the predetermined value is approximately 0.5.

21. The method of claim 19, wherein the other predetermined number is zero.

22. The method of claim 12, wherein the biological or physical data includes electrophysiological data.

23. method of detecting or predicting a cerebral disorder selected from the group consisting of human prion diseases, cardiovascular dementia, traumatic dementia, and genetic dementia, the method comprising the steps of:

analyzing input biological or physical data using a data processing routine including a set of application parameters associated with biological data correlating with the cerebral disorder to produce a data series;

to reduce noise in the data series determining whether a slope of the data series is smaller than a predetermined value;

if the slope is less than a predetermined value, setting the slope to a predetermined number; and

using the data series to detect or predict the onset of the cerebral disorder.

24. The method of claim 23, wherein the data processing routine uses the following algorithm to produce a data series PD2i:

PD2i ⊂

log C(n, r, nref*)/log r

where ⊂

log means scales as, C is the correlation integral for PD2i in which n equals the data length, r equals the scaling range, and nref8 equals a location of the reference vector for estimating the scaling region slope of log C/log r in a restricted small log-r range that is devoid of the effects of non-stationary data.

25. The method of claim 24, further comprising:

determining a noise interval within the data series; and

if the noise interval is within a predetermined range, dividing the data series by another predetermined number and repeating the step of analyzing to produce new values for the data series.