Movement disorder recovery system and method for continuous monitoring

• US 20130123666A1
• Filed: 07/18/2011
• Published: 05/16/2013
• Est. Priority Date: 03/17/2005
• Status: Active Grant

• Current Assignee: Great Lakes Neurotechnologies Inc.
• Original Assignee: Great Lakes Neurotechnologies Inc.
• Inventors: Heldman, Dustin A., Mera, Thomas O., Giuffrida, Joseph P.
• Granted Patent: US 8,702,629 B2
• Time in Patent Office: Days
• US Class Current: 600/595
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/1101 Detecting tremor
• 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 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/6826 Finger
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: A61N 1/36067 Movement disorders, e.g. tremor or Parkinson disease stimulating motor muscle A61N1/36003
• CPC Class Codes: A61N 1/36139 with automatic adjustment
• CPC Class Codes: A61N 1/37282 characterised by communication with experts in remote locations using a network

Abstract

The present invention relates to a movement disorder monitor, and a method of measuring the severity of a subject'"'"'s movement disorder. The present invention additionally relates to a treatment delivery system for treating a subject in response to changes in the severity of a subject'"'"'s symptoms. The present invention further provides for a system and method, which can accurately quantify symptoms of movement disorders, utilizing continuously obtained kinetic information to be analyzed, accurately distinguishing between symptoms of movement disorders and activities of daily living, relating quantified symptoms to a standard clinical rating scale, and correlating a subject'"'"'s symptoms with certain physiological and environmental factors. The present invention still further provides for home monitoring of symptoms in subjects with these movement disorders in order to capture the complex fluctuation patterns of the disease over the course of days, weeks, months, or years.

Claims

1. A system for quantifying symptoms of a subject having a movement disorder comprising:

a movement measuring apparatus for acquiring movement data corresponding to movement of a subject comprising at least one sensor;

a processor in communication with the movement measuring apparatus capable of using a trained algorithm to calculate a score correlated at least in part to a clinician'"'"'s standardized movement assessment rating score; and

a device for outputting or otherwise communicating the score calculated by the trained algorithm to a clinician or other user for review and analysis,

wherein the trained algorithm has been trained to distinguish at least in part between voluntary and involuntary movement, and between postural and kinetic tremor.

2. The system of claim 1, wherein the trained algorithm distinguishes between voluntary and involuntary movement by using bandpass filters.

3. The system of claim 2, wherein the trained algorithm further distinguishes between motions of postural and kinetic tremor by using the average logarithm of peak power of three orthogonal angular velocity sensor signals for known postural and kinetic movements.

4. The system of claim 1, wherein the trained algorithm is a linear regression model.

5. The system of claim 1, wherein the calculated score is correlated to the Washington Heights-Inwood Genetic Study of Essential Tremor (WHIGET) tremor rating scale or Fahn-Tolosa-Marin tremor rating scale (TRS).

6. The system of claim 1, wherein the output device is a deep brain stimulator system able to meaningfully reinterpret calculated scores into deep brain stimulation parameters.

7. The system of claim 1, wherein the output device is a central database system.

8. A system for quantifying tremor during activities of daily living in the home, comprising:

a movement measuring apparatus for continuously acquiring movement data corresponding to continuous movement of a subject and comprising at least one sensor;

a processor in communication with the movement measuring apparatus capable of using a trained algorithm to calculate a score correlated at least in part to a clinician'"'"'s standardized rating score; and

a device for outputting or otherwise communicating the score calculated by the trained algorithm to a clinician or other user for review and analysis,

wherein the trained algorithm has been trained to distinguish at least in part between voluntary and involuntary movement, and between postural and kinetic tremor.

9. The system of claim 8, wherein the trained algorithm distinguishes between voluntary and involuntary movement by using bandpass filters.

10. The system of claim 9, wherein the trained algorithm further distinguishes between motions of postural and kinetic tremor using the average logarithm of peak power of three orthogonal angular velocity sensor signals for known postural and kinetic movements.

11. The system of claim 8, wherein the trained algorithm uses a linear regression model.

12. The system of claim 8, wherein the calculated score is correlated to the Washington Heights-Inwood Genetic Study of Essential Tremor (WHIGET) tremor rating scale or Fahn-Tolosa-Marin tremor rating scale (TRS).

13. The system of claim 8, wherein the output device is a deep brain stimulator system able to meaningfully reinterpret calculated scores into deep brain stimulation parameters.

14. The system of claim 8, wherein the output device is a central database system.

15. A method for quantifying tremor during activities of daily living in the home, comprising the steps of:

providing a movement measuring apparatus comprising at least one sensor;

acquiring movement data continuously from a subject wearing the movement measuring apparatus, the movement data corresponding to continuous movement of the subject;

transmitting the movement data from the movement measuring apparatus to a processor;

processing, with the processor, the movement data using a trained algorithm to calculate a score that correlates to a clinician'"'"'s standardized rating score; and

outputting the score calculated by the trained algorithm to a clinical device or user for review and analysis,

wherein the trained algorithm has been trained to distinguish at least in part between voluntary and involuntary movement, and between postural and kinetic tremor.

16. The method of claim 15, wherein training the algorithm is based at least in part on reference data of like scores given by skilled clinicians.

17. The method of claim 16, wherein the scores given by skilled clinicians are based on the Washington Heights-Inwood Genetic Study of Essential Tremor (WHIGET) tremor rating scale or Fahn-Tolosa-Marin tremor rating scale (TRS).

18. The method of claim 15, wherein the trained algorithm is trained to distinguish at least in part between postural and kinetic tremor using the average logarithm of peak power of three orthogonal angular velocity sensor signals for known postural and kinetic movements.

19. The method of claim 15, wherein the clinician'"'"'s standardized rating score is based at least in part on the Washington Heights-Inwood Genetic Study of Essential Tremor (WHIGET) tremor rating scale or Fahn-Tolosa-Marin tremor rating scale (TRS).

20. The method of claim 15, wherein the clinical device is a deep brain stimulator system able to meaningfully reinterpret calculated scores into deep brain stimulation parameters.