Method and apparatus utilizing computational intelligence to diagnose neurological disorders

• US 20030191406A1
• Filed: 05/13/2003
• Published: 10/09/2003
• Est. Priority Date: 09/05/2000
• Status: Abandoned Application

• Current Assignee: Indiana University Research & Technology Corporation (Indiana University)
• Original Assignee: Indiana University Research & Technology Corporation (Indiana University)
• Inventors: Worth, Robert M., Eberhart, Russell C.
• Application Number:
  US10/436,843
• Publication Number:
  US 20030191406A1
• Time in Patent Office: Days
• US Class Current: 600/535
• CPC Class Codes: A61B 2560/0456 Apparatus provided with a docking unit
• CPC Class Codes: A61B 5/1101 Detecting tremor
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y10S 706/924 Medical

Abstract

A method of diagnosing patients suspected of having a neurological disorder is disclosed and includes monitoring movement of a patient in order to obtain movement data that is representative of the movement of the patient. Another step of the method includes processing the movement data in order to obtain an input pattern that is representative of the movement data. The method also includes the step of processing the input pattern with a computational intelligence system that has been trained to classify movement based upon a predetermined group of neurological disorder classifications. Furthermore, the method includes generating with the computational intelligence system an output that is indicative of an appropriate neurological disorder classification for the patient. An analysis system for diagnosing patients suspected of having a neurological disorder is also disclosed.

Claims

1. A method of diagnosing patients suspected of having a neurological disorder, comprising the steps of:

a) monitoring movement of a patient in order to obtain movement data that is representative of said movement of said patient;

b) processing said movement data in order to obtain an input pattern that is representative of said movement data;

c) processing said input pattern with a computational intelligence system that has been trained to classify movement based upon a predetermined group of neurological disorder classifications; and

d) generating with said computational intelligence system an output that is indicative of an appropriate neurological disorder classification for said patient.

2. The method of claim 1, wherein step a) comprises the steps of:

generating an analog movement signal having an amplitude that varies with respect to time and that is representative of said movement of said patient over said collection period; and

sampling said analog movement signal at a predetermined sampling rate in order to obtain a plurality of digital samples that are representative of said movement of said patient over said collection period.

3. The method of claim 1, wherein step b) comprises the steps of:

extracting characteristics of said movement from said movement data, and

generating said input pattern based upon said characteristics extracted from said movement data.

4. The method of claim 1, wherein step b) comprises the steps of:

extracting first characteristics of said movement from first data of said movement data associated with a first time interval;

extracting second characteristics of said movement from second data of said movement data associated with a second time interval; and

generating said input pattern based upon said first characteristics and said second characteristics of said movement.

5. The method of claim 1, wherein step a) comprises the step of:

collecting said movement data such that said movement data comprises a plurality of postural data sets that are each representative of a different postural tremor type.

6. The method of claim 5, wherein step b) comprises the steps of:

extracting first characteristics of said movement from a first postural data set of said plurality of postural data sets;

extracting second characteristics of said movement from a second postural data set of said plurality of postural data sets; and

generating said input pattern based upon said first characteristics and said second characteristics.

7. The method of claim 1, wherein step b) comprises the steps of:

extracting frequency characteristics of said movement from said movement data, and

generating said input pattern based upon said frequency characteristics of said movement data.

8. The method of claim 1, wherein step b) comprises the steps of:

extracting power spectral density characteristics of said movement from said movement data, and

generating said input pattern based upon said power spectral density characteristics of said movement data.

9. The method of claim 1, wherein step b) comprises the steps of:

extracting statistical characteristics of said movement from said movement data, and

generating said input pattern based upon said statistical characteristics of said movement.

10. The method of claim 1, wherein step b) comprises the steps of:

extracting first frequency characteristics of said movement from first data of said movement data associated with a first time interval,

extracting first statistical characteristics of said movement from said first data of said movement data associated with said first time interval,

extracting second frequency characteristics of said movement from second data of said movement data associated with a second time interval,

extracting second statistical characteristics of said movement from said second data of said movement data associated with said second time interval, and

generating said input pattern based upon said first frequency characteristics, said first statistical characteristics, said second frequency characteristics, and said second statistical characteristics of said movement.

11. The method of claim 1, wherein step c) comprises the step of processing said input pattern with a neural network of said computational intelligence system that is trained to classify movement.

12. The method of claim 1, wherein step c) comprises the step of:

classifying said movement of said patient with said computational intelligence system based upon said predefined group of neurological disorder classifications which comprises a normal tremor classification and a non-normal tremor classification.

13. The method of claim 1, wherein step c) comprises the step of:

classifying said movement of said patient with said computational intelligence system based upon said predefined group of neural classification which comprises a normal classification and a Parkinson'"'"'s disease classification.

14. The method of claim 1, wherein step c) comprises the step of:

classifying said movement of said patient with said computational intelligence system based upon said predefined group of neurological disorder classifications comprising a normal classification, a Parkinson'"'"'s disease classification, and an essential tremor classification.

15. An analysis system for diagnosing patients suspected of having a neurological disorder, comprising:

a movement monitoring device operable to monitor movement of a patient over a collection period in order to obtain movement data that is representative of said movement of said patient over said collection period;

a preprocessor operable to generate an input pattern that is representative of said movement data collected by said movement monitoring device over said collection period; and

a computation intelligence system comprising a neural network that has been trained to classify movement based upon a predetermined group of neurological disorder classifications, said neural network operable to (i) process said input pattern generated by said preprocessor, and (ii) generate an output that is indicative of an appropriate neurological disorder classification for said patient.

16. The analysis system of claim 15, wherein said movement monitoring device comprises an actigraph operable to:

generate an analog movement signal having an amplitude with respect to time that is indicative of said movement of said patient with respect to time, and

sample said analog movement signal at a predetermined sampling rate in order to obtain a plurality of digital samples that are representative of said movement of said patient.

17. The analysis system of claim 15, wherein said preprocessor is further operable to:

extract characteristics of said movement from said movement data, and

generate said input pattern based upon said characteristics extracted from said movement data.

18. The analysis system of claim 15, wherein said preprocessor is further operable to:

extract first characteristics of said movement from first data of said movement data associated with a first time interval;

extract second characteristics of said movement from second data of said movement data associated with a second time interval;

generate said input pattern based upon said first characteristics and said second characteristics of said movement.

19. The analysis system of claim 15, wherein said movement monitoring device is further operable to collect said movement data such that said movement data comprises a plurality of postural data sets that are each representative of a different postural tremor type.

20. The analysis system of claim 15, wherein said preprocessor is further operable to:

extract first characteristics of said movement from a first postural data set of said plurality of postural data sets;

extract second characteristics of said movement from a second postural data set of said plurality of postural data sets;

generate said input pattern based upon said first characteristics and said second characteristics of said movement.