Methods for the diagnosis and treatment of neurological disorders

• US 10,176,299 B2
• Filed: 11/13/2012
• Issued: 01/08/2019
• Est. Priority Date: 11/11/2011
• Status: Active Grant

• Current Assignee: Indiana University Research & Technology Corporation (Indiana University), Rutgers University
• Original Assignee: Indiana University Research & Technology Corporation (Indiana University), Rutgers University
• Inventors: Torres, Elizabeth B., Jose-Valenzuela, Jorge
• Primary Examiner(s): Henson, Devin
• Application Number:
  US14/354,796
• Publication Number:
  US 20140336539A1
• Time in Patent Office: 2,247 Days
• CPC Class Codes: A61B 5/0015 characterised by features of the telemetry system
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1104 induced by stimuli or drugs; A61B5/1102 takes precedence; investigating medicinal preparations G01N33/15
• CPC Class Codes: A61B 5/1114 Tracking parts of the body
• CPC Class Codes: A61B 5/1124 Determining motor skills
• CPC Class Codes: A61B 5/1125 Grasping motions of hands
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/162 Testing reaction times
• CPC Class Codes: A61B 5/168 Evaluating attention deficit, hyperactivity
• CPC Class Codes: A61B 5/40 Detecting, measuring or recording for evaluating the nervous system A61B5/4806, A61B5/4821, A61B5/4824 take precedence
• CPC Class Codes: A61B 5/4076 Diagnosing or monitoring particular conditions of the nervous system A61B5/4821, A61B5/4824 take precedence
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/4088 Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• 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 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 40/20 : for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass
• CPC Class Codes: H04L 63/0428 : wherein the data content is protected, e.g. by encrypting or encapsulating the payload

Abstract

The present invention provides objective methods of diagnosis and behavioral treatments of neurological disorders such as autism spectral disorders and Parkinson'"'"'s disease.

Claims

1. A system for determining a neurological disorder in a subject, comprising:

a computer comprising an audio/visual device and configured to output a cue on the audio/visual device;

a motion sensor configured for placement on a limb of the subject and further configured to capture movements and micro-movements of the limb of the subject in response to the cue on the audiovisual device;

programming instructions configured to cause the computer to;

determine a series of sensor data representing the captured movements and micro-movements as a function of time, wherein the movements and micro-movements include one or more intended movements,

estimate parameters of a continuous Gamma distribution family for each series of sensor data, wherein the parameters define a Gamma parameter space,

determine a position, in the Gamma parameter space, of estimated parameters corresponding to series of sensor data associated with the one or more intended movements, and

analyze a motion pattern associated with movements and micro-movements of the limb of the subject in response to the cue on the audio/visual device to determine whether there is a presence of a neurological disorder in the subject by analyzing the position of estimated parameters in the Gamma parameter space corresponding to series of sensor data associated with the one or more intended movements.

2. The system of claim 1, wherein the programming instructions for estimating the parameters of the continuous Gamma distribution family comprise programming instructions configured to estimate a shape and scale parameter of the continuous Gamma distribution family.

3. The system of claim 2, wherein the programming instructions for estimating the parameters of the continuous Gamma distribution family comprise programming instructions configured to:

display the estimated shape and scale parameters of the continuous Gamma distribution family on a Gamma parameter plane with confidence intervals, wherein an inherent variability in the motion pattern is represented and tracked on the Gamma parameter plane.

4. The system of claim 1, wherein the programming instructions for estimating the parameters of the continuous Gamma distribution family comprise programming instructions configured to:

estimate one or more moments of the continuous Gamma distribution family; and

display the estimated one or more moments of the Gamma distributions on a multi-dimensional space, wherein the motion pattern is represented in the multi-dimensional space.

5. The system of claim 1, wherein each motion sensor is an electro-magnetic sensor.

6. The system of claim 1, further comprising one or more image sensors configured to capture the bodily movements and micro-movements of the subject over time, wherein the programming instructions for determining the series of sensor data comprise additional programming instructions configured to include additional data that represent the bodily movements and micro-movements captured from the one or more image sensors.

7. The system of claim 1, wherein programming instructions for estimating the parameters of the continuous Gamma distribution family are configured to use a maximum likelihood estimation.

8. The system of claim 1, wherein the programming instructions for determining whether there is a presence of neurological disorder in the subject comprise programming instructions configured to cause the computer to determine whether the estimated parameters of the continuous Gamma distribution family correspond to an exponential distribution or a Gaussian distribution.

9. The system of claim 1, further comprising additional programming instructions configured to determine whether the one or more series of sensor data correspond to an intended motion or a spontaneous motion based on a type of the estimated Gamma distribution family.

10. The system of claim 1, wherein the programming instructions for analyzing the motion pattern further comprise programming instructions configured to cause the computer to classify whether the motion pattern corresponds to a typical motion pattern or an atypical motion pattern.

11. The system of claim 1, wherein the motion sensor configured for placement on a limb of the subject is configured for placement on a digit corresponding to the limb.

12. The system of claim 1, wherein the one or more movements and micro-movements correspond to one or more of the following:

intended movements of the limb of the subject or spontaneous movements of the limb of the subject.

13. The system of claim 1, wherein the audio/visual device is selected from one or more of the following:

a robot, a three-dimensional animate, a speaker device, a touch-sensitive user interface, or a display device.

14. The system of claim 1, wherein the presence of the neurological disorder in the subject is characterized by a position of the estimated parameters in an upper left quadrant of the Gamma parameter space.

15. The system of claim 1, wherein the programming instructions for determining whether there is a presence of neurological disorder in the subject comprise programming instructions configured to cause the computer to determine whether the estimated parameters of the continuous Gamma distribution family correspond to a skewed distribution.

16. A method for determining a neurological disorder in a subject, comprising:

outputting, by a computer, a cue on an audio/visual device;

capturing, by a motion sensor configured for placement on a limb of the subject, one or more movements and micro-movements of the limb of the subject in response to the cue on the audio/visual device, wherein the movements and micro-movements include one or more intended movements;

determining, by the computer, a series of sensor data representing the captured movements and micro-movements as a function of time;

estimating, by the computer, parameters of a continuous Gamma distribution family for each series of sensor data, wherein the parameters define a Gamma parameter space;

determining, by the computer, a position, in the Gamma parameter space, of the estimated parameters corresponding to series of sensor data associated with the one or more intended movements; and

analyzing, by the computer, a motion pattern associated with movements and micro-movements of the limb of the subject in response to the cue on the audio/visual device to determine whether there is a presence of a neurological disorder in the subject by analyzing the position of the estimated parameters in the Gamma parameter space corresponding to series of sensor data associated with the one or more intended movements.

17. The method of claim 16, wherein estimating the parameters of the continuous Gamma distribution family comprises estimating a shape and scale parameter of the continuous Gamma distribution family.

18. The method of claim 17, wherein estimating the parameters of the continuous Gamma distribution family comprises displaying the estimated shape and scale parameters of the continuous Gamma distribution family on a Gamma parameter plane with confidence intervals, wherein an inherent variability in the motion pattern is represented and tracked on the Gamma parameter plane.

19. The method of claim 16, wherein estimating the parameters of the continuous Gamma distribution family comprises:

estimating, by the computer, one or more moments of the continuous Gamma distribution family; and

displaying, by the computer, the estimated one or more moments of the continuous Gamma distribution family on a multi-dimensional space, wherein the motion pattern is represented in the multi-dimensional space.

20. The method of claim 16, wherein each motion sensor is an electro-magnetic sensor.

21. The method of claim 16, wherein determining the series of sensor data further comprises:

capturing, by one or more image sensors, the bodily movements and micro-movements of the subject over time; and

including, by the computer, in the series of sensor data additional data representing the captured bodily movements and micro-movements of the subject from the one or more image sensors.

22. The method of claim 16, wherein estimating the parameters of the continuous Gamma distribution family comprises using a maximum likelihood estimation.

23. The method of claim 16, wherein determining whether there is a presence of neurological disorder in the subject comprises determining whether the estimated parameters of the continuous Gamma distribution family correspond to an exponential distribution or a Gaussian distribution.

24. The method of claim 16, further comprising determining whether the one or more series of sensor data correspond to an intended motion or a spontaneous motion based on a type of the estimated Gamma distribution family.

25. The method of claim 16, wherein analyzing the motion pattern further comprises classifying whether the motion pattern corresponds to a typical motion pattern or an atypical motion pattern.

26. The method of claim 16, wherein the motion sensor configured for placement on a limb of the subject is configured for placement on a digit corresponding to the limb.

27. The method of claim 16, wherein the one or more movements and micro-movements correspond to one or more of the following:

intended movements of the limb of the subject or spontaneous movements of the limb of the subject.

28. The method of claim 16, wherein the audio/visual device is selected from one or more of the following:

a robot, a three-dimensional animate, a speaker device, a touch-sensitive user interface, or a display device.

29. The method of claim 16, wherein the presence of the neurological disorder in the subject is characterized by a position of the estimated parameters in an upper left quadrant of the Gamma parameter space.