Method and apparatus for classification of movement states in Parkinson's disease

• US 20050234309A1
• Filed: 01/05/2005
• Published: 10/20/2005
• Est. Priority Date: 01/07/2004
• Status: Abandoned Application

• Current Assignee: Park Kinesis Instruments, LLC
• Original Assignee: Park Kinesis Instruments, LLC
• Inventors: Klapper, David
• Application Number:
  US11/030,490
• Publication Number:
  US 20050234309A1
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/1101 Detecting tremor
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1124 Determining motor skills
• 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 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/6828 Leg
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• 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: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

For Parkinson'"'"'s patients to function at their best, their medications need to be optimally adjusted to the diurnal variation of symptoms. For this to occur, it is important for the managing clinician to have an accurate picture of how the patient'"'"'s bradykinesia/hypokinesia and dyskinesia and the patient'"'"'s perception of movement state fluctuate throughout the normal daily activities. The present invention uses wearable accelerometers coupled with computer implemented learning and statistical analysis techniques in order to classify the movement states of Parkinson'"'"'s patients and to provide a timeline of how the patients fluctuate throughout the day.

Claims

1. A method for automatically classifying the movement states in a Parkinson'"'"'s patient, the method comprising the steps of:

creating an algorithm capable of predicting the movement states of a current patient based upon information collected from prior patients;

collecting information as to the movements of the current patient over time; and

processing the information collected from the current patient using the prediction algorithm to classify the movement states of the current patient over time; and

recording the movement states of the current patient over the given time period.

2. The method of claim 1 wherein the step of creating the algorithm comprises the steps of:

collecting sensor data representative of the movement of prior patients over time utilizing multiple sensors worn by the prior patients;

converting the collected sensor data into a series of data scores representative of the movements of the prior patients over time;

observing the prior patients and assigning a series of observation scores representative of the observed movement states of the prior patients over time; and

utilizing the data scores and observation scores to create the movement states predicting algorithm.

3. The method of claim 1 wherein the step of creating the algorithm comprises the following steps:

collecting sensor data representative of the movement of prior patients over time utilizing multiple sensors worn by the prior patients;

converting the collected sensor data into a series of data scores representative of the movements of the prior patients over time;

assigning a series of scores representative of the prior patients'"'"' self-assessment of the symptoms experienced over time; and

utilizing the data scores and self-assessment scores to create the movement states predicting algorithm.

4. The method claim 2 wherein the data scores and observation scores are obtained over multiple time segments and wherein the step of utilizing the data scores and observation scores to create the movement states prediction algorithm comprises the steps of:

constructing a “

machine learning”

program; and

utilizing the data scores and the observation scores to train the program.

5. The method claim 3 wherein the data scores and self-assessment scores are obtained over multiple time segments and wherein the step of utilizing the data scores and self-assessment scores to create the movement states prediction algorithm comprises the steps of:

constructing a “

machine learning”

program; and

utilizing the data scores and the self-assessment scores to train the of the program.

6. The method of claim 2 wherein the step of converting the collected sensor data comprises the steps of:

converting the data from each sensor into a single magnitude for each of multiple points of time in the time segment;

performing a fast Fourier transform on the magnitudes for multiple time points;

converting the fast Fourier transformation results to real numbers by obtaining the absolute values thereof;

integrating the converted fast Fourier transformation results over first and second selected frequency ranges;

forming the ratio of the integration results over the selected frequency ranges for each time segment;

obtaining covariances for the ratios of the integration results obtained from selected accelerometer pairs for each time segment; and

assigning data scores for each time segment of accelerometer data based upon the covariances.

7. The method of claim 3 wherein the step of converting the collected sensor data comprises the steps of:

converting the data from each sensor into a single magnitude for each of multiple points of time in the time segment;

performing a fast Fourier transform on the magnitudes for multiple time points;

converting the fast Fourier transformation results to real numbers by obtaining the absolute values thereof;

integrating the converted fast Fourier transformation results over first and second selected frequency ranges;

forming the ratio of the integration results over the selected frequency ranges for each time segment;

obtaining covariances for the ratios of the integration results obtained from selected accelerometer pairs for each time segment; and

assigning data scores for each time segment of accelerometer data based upon the covariances.

8. The method of claim 4 wherein the step of constructing a “

machine learning”

program comprises the step of constructing a linear regression model.

9. The method of claim 5 wherein the step of constructing a “

machine learning”

program comprises the step of constructing a neutral network model.

10. The method of claim 6 wherein the step of converting the sensed data comprises the step of converting the data from each sensor in accordance with the following formula:

magnitude value=the (positive) square root of (X²+Y²+Z²⁾

wherein X, Y and Z represent the data value obtained for each axis of the accelerometer.

11. The method of claim 7 wherein the step of converting the data comprises the step of converting the accelerometric data from each sensor in accordance with the following formula:

magnitude value=the (positive) square root of (X²+Y²+Z²⁾

wherein X, Y and Z represent the data value obtained for each axis of the accelerometer.

12. The method of claim 6 wherein the sensors are accelerometers and wherein the step of converting the data comprises converting the data at approximately the sampling rate of the accelerometers.

13. The method of claim 7 wherein the sensors are accelerometers and wherein step of converting the data comprises converting the data at approximately the sampling rate of the accelerometers.

14. The method of claim 6 wherein the step of performing a fast Fourier transform comprises the step of performing the fast Fourier transform over 800 samples at a time.

15. The method of claim 7 wherein the step of performing a fast Fourier transform comprises the step of performing the fast Fourier transform over 800 samples at a time.

16. The method of claim 6 wherein the first selected frequency range is the sum of values between 0.25 Hz-3 Hz.

17. The method of claim 7 wherein the first selected frequency range is the sum of values between 0.25 Hz-3 Hz

18. The method of claim 16 wherein the second selected frequency range is the sum of values between 4 Hz-6 Hz.

19. The method of claim 17 wherein the second selected frequency range is the sum of values between 4 Hz-6 Hz.

20. The method of claim 6 wherein the sensors are accelerometers and one accelerometer measures hip movement and another accelerometer measures movement of the right upper extremity and wherein the step of obtaining covariances comprises the step obtaining the covariance of the frequency ratio of the output of the hip movement accelerometer and of the right upper extremity movement accelerometer.

21. The method of claim 7 wherein the sensors are accelerometers and wherein one accelerometer measures hip movement and another accelerometer measures movement of the right upper extremity and wherein the step of obtaining covariances comprises the step obtaining the covariance of the frequency ratio of the output of the hip movement accelerometer and of the right upper extremity movement accelerometer.

22. The method of claim 6 wherein the sensors are accelerometers and wherein one accelerometer measures hip movement and another accelerometer measures movement of the right lower extremity and wherein the step of obtaining covariances comprises the step of obtaining the covariant of the frequency ratio of the output of the hip movement accelerometer and of the right lower extremity movement accelerometer.

23. The method of claim 7 wherein the sensors are accelerometers and wherein one accelerometer measures hip movement and another accelerometer measures movement of the right lower extremity and wherein the step of obtaining covariances comprises the step of obtaining the covariant of the frequency ratio of the output of the hip movement accelerometer and of the right lower extremity movement accelerometer.

24. The method of claim 6 wherein the sensors are accelerometers and wherein one accelerometer measures hip movement and another accelerometer measures movement of the left lower extremity and wherein the step of obtaining covariances comprises obtaining the covariance of the frequency ratio of the output of the hip movement accelerometer and of the left lower extremity movement accelerometer.

25. The method of claim 7 wherein the sensors are accelerometers and wherein one accelerometer measures hip movement and another accelerometer measures movement of the left lower extremity and wherein the step of obtaining covariances comprises obtaining the covariance of the frequency ratio of the output of the hip movement accelerometer and of the left lower extremity movement accelerometer.

26. The method of claim 1 wherein the step of collecting information from the current patient comprises the steps of:

collecting the sensor data representative of the movement of the current patient over time utilizing multiple sensors worn by the current patient;

converting the collected sensor data into a series of data scores representative of the movements of the current patient over time;

utilizing the movement states algorithm to create a timeline of the current patient'"'"'s movement states based upon the current patient'"'"'s data scores.

27. The method of claim 26 further comprising the step of utilizing the timeline to manage the medicine of the current patient.

28. The method of claim 1 wherein the movement states comprise bradykinesia/hypokinesia.

29. The method of claim 1 wherein the movement states comprise dyskinesia.

30. The method of claim 1 wherein the movement states are classified over a time period in which the patient can participate in normal activities.

31. A method for automatically classifying the movement states of patients with Parkinson'"'"'s disease comprising the steps of:

creating an algorithm capable of predicting the movement states of a current patient based upon sensed data representative of the movement of the body parts of the current patient without ongoing observational or self-assessment data from the current patient;

obtaining sensed data representative of the movement states of the body parts of the current patient over time; and

processing the sensed data with the algorithm to provide an output.

32. The method of claim 31 further comprising the steps of:

creating a graphical representation of the output over time; and

utilizing the graphical representation to adjust the medication of the patient over time.

35. The method of claim 31 wherein the predicted movement states are recorded on a continual basis with no less than one predicted movement state per hour of time that the current patient had movement information collected.

37. The method of claim 35 wherein the period of time in which the predicted movement states are recorded exceeds 2 hours and 30 minutes.

39. The method of claim 31 wherein the current patient can participate in normal activities during the time period over which the sensor data is obtained.

41. The method of claim 31 wherein the step of obtaining sensed data comprises the step of collecting sensed data using a wearable device.

43. The method of claim 41 wherein the step of collecting sensed data comprises the step of wearing more than one accelerometer attached to different parts of the current patient'"'"'s body.

45. The method of claim 41 wherein the step of collecting sensed data comprises the step of wearing four or more 3 dimensional accelerometers.

47. The method of claim 31 wherein the step of creating the algorithm comprises the steps of:

selecting prior patients;

collecting information as to the movements over time of the prior patients utilizing sensors; and

collecting observational information as to the movement states and/or the patient'"'"'s self-assessments of movement states in the prior patients during time intervals corresponding to the time in which the movement states of the prior patient were collected by the sensors.

49. The method of claim 31 wherein the step of creating an algorithm comprises the step of creating an algorithm that provides increasingly improved predictions for the current patient as data from more prior patients is collected and processed.

33. A method for automatically classifying the patient'"'"'s self-assessment of movement states of patients with Parkinson'"'"'s disease comprising the steps of:

creating an algorithm capable of predicting the patient'"'"'s self-assessment of movement states of a current patient based upon sensed data representative of the movement of the body parts of the current patient without ongoing observational or self-assessment data from the current patient;

obtaining sensed data representative of the movement states of the body parts of the current patient over time; and

processing the sensed data with the algorithm to provide an output.

34. The method of claim 33 further comprising the steps of:

creating a graphical representation of the output over time; and

utilizing the graphical representation to adjust the medication of the patient over time.

36. The method of claim 33 wherein the predicted movement states are recorded on a continual basis with no less than one predicted movement state per hour of time that the current patient had movement information collected.

38. The method of claim 36 wherein the period of time in which the predicted self-assessment of movement states are recorded exceeds 2 hours and 30 minutes.

40. The method of claim 33 wherein the current patient can participate in normal activities during the time period over which the sensor data is obtained.

42. The method of claim 33 wherein the step of obtaining sensed data comprises the step of collecting sensed data using a wearable device.

44. The method of claim 42 wherein the step of collecting sensed data comprises the step of wearing more than one accelerometer attached to different parts of the current patient'"'"'s body.

46. The method of claim 42 wherein the step of collecting sensed data comprises the step of wearing four or more 3 dimensional accelerometers.

48. The method of claim 33 wherein the step of creating the algorithm comprises the steps of:

selecting prior patients;

collecting information as to the movements over time of the prior patients utilizing sensors; and

collecting observational information as to the movement states and/or the patient'"'"'s self-assessments of symptoms in the prior patients during time intervals corresponding to the time in which the movement states of the prior patient were collected by the sensors.

50. The method of claim 33 wherein the step of creating an algorithm comprises the step of creating an algorithm that provides increasingly improved predictions for the current patient as data from more prior patients is collected and processed.

51. Apparatus for automatically classifying the movement states in a Parkinson'"'"'s patient comprising means for creating an algorithm capable of predicting the movement states of a current Parkinson'"'"'s patient based upon information collected from prior patients;

means for collecting information as to the movements of the current patient over time;

means for processing the information collected from the current patient using the prediction algorithm to classify the movement states of the current patient over time; and

means for recording the movement states of the current Parkinson'"'"'s patient over the given time period.

52. The apparatus of claim 51 wherein the means for creating the algorithm comprises means for collecting sensor data representative of the movement of prior patients over time utilizing multiple sensors worn by the prior patients;

means for converting the collected sensor data into a series of data scores representative of the movements of the prior patients over time;

wherein the prior patients are observed and a series of observation scores representative of the observed movement states of the prior patients over time are assigned; and

means for utilizing the data scores and observation scores to create the movement states predicting algorithm.

53. The apparatus of claim 51 wherein the means for creating the algorithm comprises means for collecting sensor data representative of the movement of prior patients over time utilizing multiple sensors worn by the prior patients;

means for converting the collected sensor data into a series of data scores representative of the movements of the prior patients over time;

wherein a series of scores representative of the prior patients'"'"' self-assessment of the symptoms experienced over time are assigned; and

means for utilizing the data scores and self-assessment scores to create the movement states predicting algorithm.

54. The apparatus claim 52 wherein the data scores and observation scores are assigned over multiple time segments and wherein the means for utilizing the data scores and observation scores to create the movement states prediction algorithm comprises means for constructing a “

machine learning”

program; and

means for utilizing the data scores and the observation scores to train the program.

55. The apparatus claim 53 wherein the data scores and self-assessment scores are assigned over multiple time segments and wherein the means for utilizing the data scores and self-assessment scores to create the movement states prediction algorithm comprises means for constructing a “

machine learning”

program; and

means for utilizing the data scores and the self-assessment scores to train the program

56. The apparatus of claim 52 wherein the means for converting the collected sensor data comprises means for converting the data from each sensor into a single magnitude for each of multiple points of time in the time segment;

means for performing a fast Fourier transform on the magnitudes for multiple time points;

means for converting the fast Fourier transformation results to real numbers by obtaining the absolute values thereof;

means for integrating the converted fast Fourier transformation results over first and second selected frequency ranges;

means for forming the ratio of the integration results over the selected frequency ranges for each time segment;

means for obtaining covariances for the ratios of the integration results obtained from selected accelerometer pairs for each time segment; and

means for assigning data scores for each time segment of accelerometer data based upon the covariances.

57. The apparatus of claim 53 wherein the means for converting the collected sensor data comprises means for converting the data from each sensor into a single magnitude for each of multiple points of time in the time segment;

means for performing a fast Fourier transform on the magnitudes for multiple time points;

means for converting the fast Fourier transformation results to real numbers by obtaining the absolute values thereof;

means for integrating the converted fast Fourier transformation results over first and second selected frequency ranges;

means for forming the ratio of the integration results over the selected frequency ranges for each time segment;

means for obtaining covariances for the ratios of the integration results obtained from selected accelerometer pairs for each time segment; and

means for assigning data scores for each time segment of accelerometer data based upon the covariances.

58. The apparatus of claim 54 wherein the means for constructing a “

machine learning”

program comprises means for constructing a linear regression model.

59. The apparatus of claim 55 wherein the means for constructing a “

machine learning”

program comprises the step of constructing a neutral network model.

60. The apparatus of claim 56 wherein the sensors are accelerometers and wherein the means for converting the data comprises means for converting the accelerometric data from each accelerometer in accordance with the following formula:

magnitude value=the (positive) square root of (X²+Y²+Z²⁾

wherein X, Y and Z represent the data value obtained for each axis of the accelerometer.

61. The apparatus of claim 57 wherein the sensors are accelerometers and wherein the means for converting the data comprises means for converting the accelerometric data from each accelerometer in accordance with the following formula:

magnitude value=the (positive) square root of (X²+Y²+Z²⁾

wherein X, Y and Z represent the data value obtained for each axis of the accelerometer.

62. The apparatus of claim 56 wherein the sensors are accelerometers and wherein the means for converting the data comprises means for converting the accelerometric data at approximately the sampling rate of the accelerometers.

63. The apparatus of claim 57 wherein the sensors are accelerometers and wherein the means for converting the accelerometric data comprises means for converting the accelerometric data at approximately the sampling rate of the accelerometers.

64. The apparatus of claim 56 wherein the means for performing a fast Fourier transform comprises means for performing the fast Fourier transform over 800 samples at a time.

65. The apparatus of claim 57 wherein the means for performing a fast Fourier transform comprises means for performing the fast Fourier transform over 800 samples at a time.

66. The apparatus of claim 56 wherein the first selected frequency range is the sum of values between 0.25 Hz-3 Hz.

67. The apparatus of claim 57 wherein the first selected frequency range is the sum of values between 0.25 Hz-3 Hz

68. The apparatus of claim 66 wherein the second selected frequency range is the sum of values between 4 Hz-6 Hz.

69. The apparatus of claim 67 wherein the second selected frequency range is the sum of values between 4 Hz-6 Hz.

70. The apparatus of claim 56 wherein the sensors are accelerometers and wherein one accelerometer measures hip movement and another accelerometer measures movement of the upper right extremity and wherein the means for obtaining covariances comprises means for obtaining the covariance of the frequency ratio of the output of the hip movement accelerometer and of the upper right extremity movement accelerometer.

71. The apparatus of claim 57 wherein the sensors are accelerometers and wherein one accelerometer measures hip movement and another accelerometer measures movement of the upper right extremity and wherein the means for obtaining covariances comprises means for obtaining the covariance of the frequency ratio of the output of the hip movement accelerometer and of the upper right extremity movement accelerometer.

72. The apparatus of claim 56 wherein the sensors are accelerometers and wherein one accelerometer measures hip movement and another accelerometer measures movement of the lower right extremity and wherein the means for obtaining covariances comprises means for obtaining the covariant of the frequency ratio of the output of the hip movement accelerometer and of the lower right extremity movement accelerometer.

73. The apparatus of claim 57 wherein the sensors are accelerometers and wherein one accelerometer measures hip movement and another accelerometer measures movement of the lower right extremity and wherein the means for obtaining covariances comprises means for obtaining the covariant of the frequency ratio of the output of the hip movement accelerometer and of the lower right extremity movement accelerometer.

74. The apparatus of claim 56 wherein the sensors are accelerometers and wherein one accelerometer measures hip movement and another accelerometer measures movement of the lower left extremity and wherein the means for obtaining covariances comprises means for obtaining the covariance of the frequency ratio of the output of the hip movement accelerometer and of the lower left extremity movement accelerometer.

75. The apparatus of claim 57 wherein the sensors are accelerometers and wherein one accelerometer measures hip movement and another accelerometer measures movement of the lower left extremity and wherein the means for obtaining covariances comprises means for obtaining the covariance of the frequency ratio of the output of the hip movement accelerometer and of the lower left extremity movement accelerometer.

76. The apparatus of claim 51 wherein the means for collecting information from the current patient comprises means for collecting the sensor data representative of the movement of the current patient over time utilizing multiple accelerometers worn by the current patient;

means for converting the collected sensor data into a series of data scores representative of the movements of the current patient over time; and

means for utilizing the movement states algorithm to create a timeline of the current patient'"'"'s movement states based upon the current patient'"'"'s data scores.

77. The apparatus of claim 76 wherein the timeline is used to manage the medicine of the current patient.

78. The apparatus of claim 51 wherein the movement states comprise bradykinesia/hypokinesia.

79. The apparatus of claim 51 wherein the movement states comprise dyskinesia.

80. The apparatus of claim 51 wherein the movement states are classified over a time period in which normal activities are taking place.

81. Apparatus for automatically classifying the movement states of patients with Parkinson'"'"'s disease comprising means for creating an algorithm capable of predicting the movement states of a current patient based upon sensed data representative of the movement of the body parts of the current patient without any prior information about the current patient;

means for obtaining sensed data representative of the movement states of the body parts of the current patient over time; and

means for processing the sensed data with the algorithm to provide an output.

82. The apparatus of claim 81 further comprising means for creating a graphical representation of the output over time, wherein the graphical representation is used to adjust the medication of the patient over time.

85. The apparatus of claim 81 further comprising means for recoding the predicted movement states on a continual basis with no less than one predicted movement state per hour of time that the current patient had movement information collected.

87. The apparatus of claim 85 wherein the recording means records the predicted movement states over a time period that exceeds 2 hours and 30 minutes.

89. The apparatus of claim 81 wherein the current patient can participate in normal activities during the time period over which the sensor data is obtained.

91. The apparatus of claim 81 wherein the means for obtaining sensed data comprises a wearable device.

93. The apparatus of claim 81 wherein the means for collecting sensed data comprises more than one accelerometer attached to different parts of the current patient'"'"'s body.

95. The apparatus of claim 81 wherein the means for collecting sensed data comprises four or more 3 dimensional accelerometers.

97. The apparatus of claim 81 wherein the means for creating the algorithm comprises means for collecting information as to the movements over time of the prior patients utilizing sensors;

wherein observational information as to the movement states and/or the patient'"'"'s self-assessments of movement states in the prior patients is collected during time intervals corresponding to the time in which the movement states of the prior patient were collected by the sensors.

99. The apparatus of claim 81 wherein the means for creating an algorithm comprises means for creating an algorithm that provides increasingly improved predictions for the current patient as data from more prior patients is collected and processed.

83. Apparatus for automatically classifying the patient'"'"'s self-assessment of movement states of patients with Parkinson'"'"'s disease comprising means for creating an algorithm capable of predicting the self-assessment of movement states of a current patient based upon sensed data representative of the movement of the body parts of the current patient without any prior information about the current patient;

means for obtaining sensed data representative of the movement states of the body parts of the current patient over time; and

means for processing the sensed data with the algorithm to provide an output.

84. The apparatus of claim 83 further comprising means for creating a graphical representation of the output over time, wherein the graphical representation is used to adjust the medication of the patient over time.

86. The apparatus of claim 83 wherein the recording means records the predicted movement states on a continual basis with no less than one predicted movement state per hour of time that the current patient had movement information collected.

88. The apparatus of claim 86 wherein the recording means records the predicted self-assessment of movement states over a period of time that exceeds 2 hours and 30 minutes.

90. The apparatus of claim 83 wherein the current patient can participate in normal activities during the time period over which the sensor data is obtained.

92. The apparatus of claim 83 wherein the means for obtaining sensed data comprises a wearable device.

94. The apparatus of claim 83 wherein the means for collecting sensed data comprises more than one accelerometer attached to different parts of the current patient'"'"'s body.

96. The apparatus of claim 83 wherein the means for collecting sense data comprises the four or more 3 dimensional accelerometers.

98. The apparatus of claim 83 wherein the means for creating the algorithm comprises means for collecting information as to the movements over time of the prior patients utilizing sensors;

wherein observational information as to the movement states and/or the patient'"'"'s self-assessments of movement states in the prior patients is collected during time intervals corresponding to the time in which the movement states of the prior patient were collected by the sensors.

100. The apparatus of claim 83 wherein the means for creating an algorithm comprises means for creating an algorithm that provides increasingly improved predictions for the current patient as data from more prior patients is collected and processed.