Method and system for detecting and/or predicting biological anomalies

• US 7,076,288 B2
• Filed: 01/29/2003
• Issued: 07/11/2006
• Est. Priority Date: 01/29/2003
• Status: Expired due to Fees

• Current Assignee: Vicor Technologies, Inc.
• Original Assignee: Vicor Technologies, Inc.
• Inventors: Skinner, James
• Primary Examiner(s): MANUEL, GEORGE C
• Application Number:
  US10/353,849
• Publication Number:
  US 20040147815A1
• Time in Patent Office: 1,259 Days
• Field of Search: 600/300, 600/437, 600/509, 600515-518, 600/544, 600/545, 702/19, 128/920
• US Class Current: 600/509
• 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 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: G06F 2218/12 Classification; Matching
• 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

Biological anomalies 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 biological anomalies. 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 biological anomalies, 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 biological anomalies to produce a 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 biological anomalies,

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.

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

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

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

5. The method of claim 4, wherein the input biological or physical data includes electrophysiological data and ECG data that is analyzed to detect or predict the onset of at least one of cardiac arrhythmias and cerebral epileptic seizure and/or to measure the severity of myocardial ischemia.

6. A method of detecting or predicting biological anomalies, 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 biological anomalies to produce a 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;

using the data series to detect or predict the onset of the biological anomalies;

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.

7. The method of claim 6, wherein the other predetermined number is two.

8. The method of claim 6, wherein the predetermined range is −

x to x, where x is any number.

9. The method of claim 8, wherein the predetermined range is −

5 to 5.

10. A method of detecting or predicting biological anomalies, 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 biological anomalies to produce a data series;

determining a noise interval within the data series; and

if the noise interval is within 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 outside the predetermined range, using the data series to detect or predict the onset of the biological anomalies.

11. The method of claim 10, 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.

12. The method of claim 10, wherein the predetermined number is two.

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

x to x, where x is any number.

14. The method of claim 13, wherein the predetermined range is −

5 to 5.

15. The method of claim 10, 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.

16. The method of claim 15, wherein the predetermined value is approximately 0.5.

17. The method of claim 15, wherein the other predetermined number is zero.

18. The method of claim 10, wherein the biological or physical data includes electrophysiological data.

19. The method of claim 18, wherein the electrophysiological data is ECG data that is analyzed to detect or predict the onset of at least one of cardiac arrhythmias and cerebral epilepsy and/or to measure the severity of myocardial ischemia.

20. An apparatus for detecting or predicting biological anomalies, the apparatus comprising:

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

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

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

means for using the data series to detect or predict the onset of the biological anomalies.

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.

21. The apparatus of claim 20, wherein the predetermined value is approximately 0.5.

22. The apparatus of claim 20, wherein the predetermined number is zero.

23. The apparatus of claim 20, wherein the input biological or physical data includes electrophysiological data.

24. The apparatus of claim 23, wherein the input biological or physical data includes ECG data that is analyzed to detect or predict the onset of at least one of cardiac arrhythmias and cerebral epilepsy and/or to measure the severity of myocardial ischemia.

25. An apparatus for detecting or predicting biological anomalies, the apparatus comprising:

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

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

means for setting the slope to a predetermined number if the slope is less than a predetermined value;

means for using the data series to detect or predict the onset of the biological anomalies;

means for determining a noise interval within the data series; and

means for dividing the data series by another predetermined number if the noise interval is within a predetermined range and providing the divided data series to the analyzing means for producing new values for the data series.

26. The apparatus of claim 25, wherein the other predetermined number is two.

27. The apparatus of claim 25, wherein the predetermined range is −

x to x, where x is any number.

28. The apparatus of claim 27, wherein the predetermined range is −

5 to 5.

29. An apparatus for detecting or predicting biological anomalies, the apparatus comprising:

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

means determining a noise interval within the data series;

means for dividing the data series by a predetermined number and providing the divided data to the analyzing means to produce new values for the data series if the noise interval is within a predetermined range; and

means for using the data series to detect or predict the onset of the biological anomalies if the noise interval is outside the predetermined range.

30. The apparatus of claim 29, 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.

31. The apparatus of claim 29, wherein the predetermined number is two.

32. The apparatus of claim 29, wherein the predetermined range is −

x to x, where x is any number.

33. The apparatus of claim 32, wherein the predetermined range is −

5 to 5.

34. The apparatus of claim 29, further comprising:

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

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

35. The apparatus of claim 34, wherein the predetermined value is approximately 0.5.

36. The apparatus of claim 34, wherein the other predetermined number is zero.

37. The apparatus of claim 29, wherein the biological or physical data includes electrophysiological data.

38. The apparatus of claim 37, wherein the electrophysiological data is ECG data that is analyzed to detect or predict the onset of at least one of cardiac arrhythmias and cerebral epilepsy and/or to measure the severity of myocardial ischemia.