Fractal analysis of time varying data

• US 6,422,998 B1
• Filed: 09/20/1999
• Issued: 07/23/2002
• Est. Priority Date: 09/20/1999
• Status: Expired due to Fees

• Current Assignee: UT-Battelle LLC
• Original Assignee: UT-Battelle LLC
• Inventors: Sadana, Ajit, Vo-Dinh, Tuan
• Primary Examiner(s): Shaver, Kevin
• Assistant Examiner(s): SZMAL, BRIAN SCOTT
• Application Number:
  US09/398,703
• Time in Patent Office: 1,037 Days
• Field of Search: 600/300, 600/480, 600/479, 600/485, 600/487, 600/561, 600/323, 600/509, 600/512, 128/897, 128/898, 356/41, 702/28, 382/133, 364/413.08, 364/413.1, 364/496
• US Class Current: 600/300
• CPC Class Codes: G06F 17/17 Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method G06F17/18 takes precedence ; interpolation for numerical control G05B19/18

Abstract

Characteristics of time varying data, such as an electrical signal, are analyzed by converting the data from a temporal domain into a spatial domain pattern. Fractal analysis is performed on the spatial domain pattern, thereby producing a fractal dimension D_F. The fractal dimension indicates the regularity of the time varying data.

Claims

1. A method for analyzing time domain data representing characteristics of temporal events, said method comprising the steps of:

receiving said time domain data;

converting said time domain data into a continuous spatial domain representation of said temporal events; and

performing fractal analysis on the said continuous spatial domain representation derived from said time domain data, thereby producing a fractal dimension D_F indicative of heterogeneity of said time domain data.

2. The method as recited in claim 1 wherein the temporal events are produced by a physiological function of an animal.

3. The method as recited in claim 2 further comprises diagnosing a pathological condition in response to the value of the fractal dimension D_F.

4. The method as recited in claim 1 wherein the step of converting comprises creating a plot of the time domain data;

scanning the plot to produce image data; and

storing the image data into a computer.

5. The method as recited in claim 4 wherein the step of performing fractal analysis analyzes image data which was stored into the computer.

6. The method as recited in claim 1 wherein the step of converting comprises transforming the time domain data into multi-dimensional image data;

and storing the multi-dimensional image data into a computer.

7. The method as recited in claim 6 wherein the step of performing fractal analysis processes the multi-dimensional image data which was stored into the computer.

8. The method as recited in claim 1 wherein the time domain data is spectroscopy data.

9. The method of claim 1, wherein said converting comprises time-to-space coordinate conversion, wherein a waveform representing said time domain data is preserved upon said conversion.

10. The method of claim 9, wherein following said coordinate conversion, said fractal analysis directly analyzes said waveform.

11. A method for analyzing characteristics of a time varying phenomenon represented by an electrical signal, said method comprising the steps of:

receiving said electrical signal;

converting said electrical signal from a time domain into a continuous spatial domain representation of said electrical signal; and

performing fractal analysis on said continuous spatial domain representation of the electrical signal thereby producing a fractal dimension D_F associated with said time varying phenomenon.

12. The method as recited in claim 11 wherein the step of converting comprises transforming the electrical signal in the time domain into a multi-dimensional image;

and storing the image into a computer.

13. The method as recited in claim 12 wherein the step of performing fractal analysis analyzes multi-dimensional graphical image which was stored into the computer.

14. The method as recited in claim 11 wherein the electrical signal represents a physiological function of an animal.

15. The method as recited in claim 14 further comprises diagnosing a pathological condition of the animal in response to the value of the fractal dimension D_F.

19. The method as recited in claim 14 further comprises diagnosing a physiological condition of the animal in response to a value of the fractal dimension D_F.

16. A method for analyzing physiological activity of an animal, said method comprising the steps of:

sensing said physiological activity and producing an electrical signal representative of that physiological activity;

converting said electrical signal from a time domain into a spatial domain representation of said electrical signal; and

performing fractal analysis on the spatial domain representation of said electrical signal thereby producing a fractal dimension D_F indicative of regularity of said physiological activity.

17. The method as recited in claim 16 wherein the step of converting comprises creating a plot of the time domain data;

scanning the plot to produce image data; and

storing the image data into a computer.

18. The method as recited in claim 17 wherein the step of performing fractal analysis analyzes image data which was stored into the computer.

20. A method for analyzing spectroscopy data derived from a sample;

said method comprising the steps of;

receiving said spectroscopy data, said spectroscopy data being derived from an optical spectroscopic technique capable of providing functional information regarding said sample;

producing an image from said spectroscopy data, said image providing said functional information regarding said sample; and

performing fractal analysis on said image to obtain a fractal dimension D_F.

21. The method as recited in claim 20 wherein the step of producing an image produces a multi-dimensional image from the spectroscopy data.

22. The method as recited in claim 20 further comprising storing the image into a computer.

23. The method as recited in claim 22 wherein the step of performing fractal analysis analyzes the image which was stored into the computer.

24. The method as recited in claim 20, wherein said optical spectroscopic technique is at least one selected from the group consisting of fluorescence, reflection and Raman spectroscopy.

25. The method as recited in claim 20, wherein said sample comprises a biological sample.

26. The method as recited in claim 25, wherein said biological sample comprises at least one cell.

27. The method as recited in claim 20, further comprising the step of relating said fractal dimension D_F to a disease state in said sample.