Method, System and Computer Program Product for Limb Movement Analysis for Diagnosis of Convulsions

• US 20110230730A1
• Filed: 08/01/2008
• Published: 09/22/2011
• Est. Priority Date: 08/03/2007
• Status: Abandoned Application

• Current Assignee: University of Virginia Patent Foundation (University of Virginia)
• Original Assignee: University of Virginia Patent Foundation (University of Virginia)
• Inventors: Campbell, William W., Johnson, Michael L., Quigg, Mark S., Redmond, Daniel
• Application Number:
  US12/671,788
• Publication Number:
  US 20110230730A1
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/372 Analysis of electroencephalograms
• CPC Class Codes: A61B 5/384 Recording apparatus or displays specially adapted therefor
• CPC Class Codes: A61B 5/386 Accessories or supplementary instruments therefor
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy

Abstract

A method or system is provided for diagnosing convulsions of a subject. The method or system may include two general components. First, a device or apparatus that measures and accumulates an electronic data stream representing the physical movements of the human limb(s) and presents that data to a suitable computational device or system for further processing. Second, a set of software or firmware algorithms that operates within the computational device on the data stream for the purpose of describing and characterizing the statistical properties of the movement signal during clinical seizure episodes. The system comprised of both components serves to distinguish and diagnose the movement features of ES events from those of PNES.

Claims

1. A computer implemented method of distinguishing epileptic (ES) from non-epileptic pseudoseizures (PS) derived from motor activity of the limbs of a subject.

2. The method of claim 1, comprising:

measuring and accumulating an electronic data stream representing said motor activity.

3. The method of claim 2, further comprising:

quantifying differences within said electronic data stream to distinguish said epileptic (ES) from non-epileptic pseudoseizures (PS).

4. The method of claim 3, wherein said measuring is provided by a sensor device.

5. The method of claim 4, wherein said said sensor device comprises at least one of the following:

accelerometer, electromyographic electrodes or optoelectronic distortion sensors.

6. The method of claim 4, further comprising:

placing said sensor device on a limb of said subject.

7. A system for diagnosing convulsions of a subject, said system comprising:

a sensor device for measuring and accumulating an electronic data stream representing motor activity; and

a processor for processing said electronic data to distinguish epileptic (ES) from non-epileptic pseudoseizures (PS).

8. The system of claim 7, wherein said sensor device is provided by at least one of the following types of devices:

accelerometer, electromyographic electrodes or optoelectronic distortion sensors.

9. The system of claim 7, wherein said processing comprises:

approximate entropy (ApEn) technique or peak-to-peak amplitude of repeated patterns (PPARP) technique, or any combination thereof.

10. A computer program product comprising a computer useable medium having computer program logic for enabling at least one processor in a computer system to diagnose convulsions of a subject, said computer program logic comprising:

receiving electronic data representing motor activity; and

processing said electronic data to distinguish epileptic (ES) from non-epileptic pseudoseizures (PS).

11. The computer program product of claim 10, wherein said processing comprises:

approximate entropy (ApEn) technique or peak-to-peak amplitude of repeated patterns (PPARP) technique, or any combination thereof.